MODELING AND FORECASTING REALIZED VOLATILITY*
by Torben G. Andersen a, Tim Bollerslev b, Francis X. Diebold c and Paul Labys d
First Draft: January 1999
Revised: January 2001, January 2002
We provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on potentially restrictive and complicated parametric multivariate ARCH or stochastic volatility models. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time-series methods for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark / Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to a variety of popular daily ARCH and more complicated high-frequency models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, produces well-calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.
K EYWORDS: Continuous-time methods, quadratic variation, realized volatility, realized correlation, high-frequency data, exchange rates, vector autoregression, long memory, volatility forecasting, correlation forecasting, density forecasting, risk management, value at risk.
_________________
* This research was supported by the National Science Foundation. We are grateful to Olsen and Associates, who generously made available their intraday exchange rate data. For insightful suggestions and comments we thank three anonymous referees and the Co-Editor, as well as Kobi Bodoukh, Sean Campbell, Rob Engle, Eric Ghysels, Atsushi Inoue, Eric Renault, Jeff Russell, Neil Shephard, Til Schuermann, Clara Vega, Ken West, and seminar participants at BIS (Basel), Chicago, CIRANO/Montreal, Emory, Iowa, Michigan, Minnesota, NYU, Penn, Rice, UCLA, UCSB, the June 2000 Meeting of the Western Finance Association, the July 2001 NSF/NBER Conference on Forecasting and Empirical Methods in Macroeconomics and Finance, the November 2001 NBER Meeting on Financial Risk Management, and the January 2002 North American Meeting of the Econometric Society.
a Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208, and NBER,
phone: 847-467-1285, e-mail: t-andersen@https://www.doczj.com/doc/9213812626.html,
b Department of Economics, Duke University, Durham, NC 27708, and NBER,
phone: 919-660-1846, e-mail: boller@https://www.doczj.com/doc/9213812626.html,
c Department of Economics, University of Pennsylvania, Philadelphia, PA 19104, an
d NBER,
phone: 215-898-1507, e-mail: fdiebold@https://www.doczj.com/doc/9213812626.html,
d Graduat
e Group in Economics, University o
f Pennsylvania, 3718 Locust Walk, Philadelphia, PA 19104,
phone: 801-536-1511, e-mail: labys@https://www.doczj.com/doc/9213812626.html,
Copyright ? 2000-2002 T.G. Andersen, T. Bollerslev, F.X. Diebold and P. Labys
1. INTRODUCTION
The joint distributional characteristics of asset returns are pivotal for many issues in financial economics. They are the key ingredients for the pricing of financial instruments, and they speak directly to the risk-return tradeoff central to portfolio allocation, performance evaluation, and managerial decision-making. Moreover, they are intimately related to the fractiles of conditional portfolio return distributions, which govern the likelihood of extreme shifts in portfolio value and are therefore central to financial risk management, figuring prominently in both regulatory and private-sector initiatives.
The most critical feature of the conditional return distribution is arguably its second moment structure, which is empirically the dominant time-varying characteristic of the distribution. This fact has spurred an enormous literature on the modeling and forecasting of return volatility.1 Over time, the availability of data for increasingly shorter return horizons has allowed the focus to shift from modeling at quarterly and monthly frequencies to the weekly and daily horizons. Forecasting performance has improved with the incorporation of more data, not only because high-frequency volatility turns out to be highly predictable, but also because the information in high-frequency data proves useful for forecasting at longer horizons, such as monthly or quarterly.
In some respects, however, progress in volatility modeling has slowed in the last decade. First, the availability of truly high-frequency intraday data has made scant impact on the modeling of, say, daily return volatility. It has become apparent that standard volatility models used for forecasting at the daily level cannot readily accommodate the information in intraday data, and models specified directly for the intraday data generally fail to capture the longer interdaily volatility movements sufficiently well. As a result, standard practice is still to produce forecasts of daily volatility from daily return observations, even when higher-frequency data are available. Second, the focus of volatility modeling continues to be decidedly very low-dimensional, if not universally univariate. Many multivariate ARCH and stochastic volatility models for time-varying return volatilities and conditional distributions have, of course, been proposed (see, for example, the surveys by Bollerslev, Engle and Nelson (1994) and Ghysels, Harvey and Renault (1996)), but those models generally suffer from a curse-of-dimensionality problem that severely constrains their practical application. Consequently, it is rare to see substantive applications of those multivariate models dealing with more than a few assets simultaneously.
In view of such difficulties, finance practitioners have largely eschewed formal volatility modeling and forecasting in the higher-dimensional situations of practical relevance, relying instead on
1 Here and throughout, we use the generic term “volatilities” in reference both to variances (or standard deviations)
ad hoc methods, such as simple exponential smoothing coupled with an assumption of conditionally normally distributed returns.2 Although such methods rely on counterfactual assumptions and are almost surely suboptimal, practitioners have been swayed by considerations of feasibility, simplicity and speed of implementation in high-dimensional environments.
Set against this rather discouraging background, we seek to improve matters. We propose a new and rigorous framework for volatility forecasting and conditional return fractile, or value-at-risk (VaR), calculation, with two key properties. First, it efficiently exploits the information in intraday return data, without having to explicitly model the intraday data, producing significant improvements in predictive performance relative to standard procedures that rely on daily data alone. Second, it achieves a simplicity and ease of implementation, which, for example, holds promise for high-dimensional return volatility modeling.
We progress by focusing on an empirical measure of daily return variability called realized volatility, which is easily computed from high-frequency intra-period returns. The theory of quadratic variation suggests that, under suitable conditions, realized volatility is an unbiased and highly efficient estimator of return volatility, as discussed in Andersen, Bollerslev, Diebold and Labys (2001) (henceforth ABDL) as well as in concurrent work by Barndorff-Nielsen and Shephard (2002, 2001a).3 Building on the notion of continuous-time arbitrage-free price processes, we advance in several directions, including rigorous theoretical foundations, multivariate emphasis, explicit focus on forecasting, and links to modern risk management via modeling of the entire conditional density.
Empirically, by treating volatility as observed rather than latent, our approach facilitates modeling and forecasting using simple methods based directly on observable variables.4 We illustrate the ideas using the highly liquid U.S. dollar ($), Deutschemark (DM), and Japanese yen (￥) spot exchange rate markets. Our full sample consists of nearly thirteen years of continuously recorded spot quotations from 1986 through 1999. During that period, the dollar, Deutschemark and yen constituted
2This approach is exemplified by the highly influential “RiskMetrics” of J.P. Morgan (1997).
3 Earlier work by Comte and Renault (1998), within the context of estimation of a long-memory stochastic volatility model, helped to elevate the discussion of realized and integrated volatility to a more rigorous theoretical level.
4 The direct modeling of observable volatility proxies was pioneered by Taylor (1986), who fit ARMA models to absolute and squared returns. Subsequent empirical work exploiting related univariate approaches based on improved realized volatility measures from a heuristic perspective includes French, Schwert and Stambaugh (1987) and Schwert (1989), who rely on daily returns to estimate models for monthly realized U.S. equity volatility, and Hsieh (1991), who fits an AR(5) model to a time series of daily realized logarithmic volatilities constructed from 15-minute S&P500 returns.
the main axes of the international financial system, and thus spanned the majority of the systematic currency risk faced by large institutional investors and international corporations.
We break the sample into a ten-year "in-sample" estimation period, and a subsequent two-and-a-half-year "out-of-sample" forecasting period. The basic distributional and dynamic characteristics of the foreign exchange returns and realized volatilities during the in-sample period have been analyzed in detail by ABDL (2000a, 2001).5 Three pieces of their results form the foundation on which the empirical analysis of this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly right-skewed, the distributions of the logarithms of realized volatilities are approximately Gaussian. Third, the long-run dynamics of realized logarithmic volatilities are well approximated by a fractionally-integrated long-memory process.
Motivated by the three ABDL empirical regularities, we proceed to estimate and evaluate a multivariate model for the logarithmic realized volatilities: a fractionally-integrated Gaussian vector autoregression (VAR) . Importantly, our approach explicitly permits measurement errors in the realized volatilities. Comparing the resulting volatility forecasts to those obtained from currently popular daily volatility models and more complicated high-frequency models, we find that our simple Gaussian VAR forecasts generally produce superior forecasts. Furthermore, we show that, given the theoretically motivated and empirically plausible assumption of normally distributed returns conditional on the realized volatilities, the resulting lognormal-normal mixture forecast distribution provides conditionally well-calibrated density forecasts of returns, from which we obtain accurate estimates of conditional return quantiles.
In the remainder of this paper, we proceed as follows. We begin in section 2 by formally developing the relevant quadratic variation theory within a standard frictionless arbitrage-free multivariate pricing environment. In section 3 we discuss the practical construction of realized volatilities from high-frequency foreign exchange returns. Next, in section 4 we summarize the salient distributional features of returns and volatilities, which motivate the long-memory trivariate Gaussian VAR that we estimate in section 5. In section 6 we compare the resulting volatility point forecasts to those obtained from more traditional volatility models. We also evaluate the success of the density forecasts and corresponding VaR estimates generated from the long-memory Gaussian VAR in
5 Strikingly similar and hence confirmatory qualitative findings have been obtained from a separate sample consisting of individual U.S. stock returns in Andersen, Bollerslev, Diebold and Ebens (2001).
conjunction with a lognormal-normal mixture distribution. In section 7 we conclude with suggestions for future research and discussion of issues related to the practical implementation of our approach for other financial instruments and markets.
2. QUADRATIC RETURN VARIATION AND REALIZED VOLATILITY
We consider an n -dimensional price process defined on a complete probability space, (,?, P), evolving
in continuous time over the interval [0,T], where T denotes a positive integer. We further consider an information filtration, i.e., an increasing family of -fields, (?
t )t 0[0,T] f ? , which satisfies the usual conditions of P -completeness and right continuity. Finally, we assume that the asset prices through time t , including the relevant state variables, are included in the information set ?t .
Under the standard assumptions that the return process does not allow for arbitrage and has a
finite instantaneous mean the asset price process, as well as smooth transformations thereof, belong to the class of special semi-martingales, as detailed by Back (1991). A fundamental result of stochastic integration theory states that such processes permit a unique canonical decomposition. In particular, we have the following characterization of the logarithmic asset price vector process, p = (p(t))t 0[0,T].PROPOSITION 1: For any n-dimensional arbitrage-free vector price process with finite mean, the logarithmic vector price process, p, may be written uniquely as the sum of a finite variation and predictable mean component, A = (A 1 , ... , A n ), and a local martingale, M = (M 1 , ... , M n ). These may each be decomposed into a continuous sample-path and jump part,
p(t) = p(0) + A(t) + M(t) = p(0) + A c (t) + )A(t) + M c (t) + )M(t),
(1)
where the finite-variation predictable components, A c and )A, are respectively continuous and pure jump processes, while the local martingales, M c and )M, are respectively continuous sample-path and compensated jump processes, and by definition M(0) / A(0) / 0. Moreover, the predictable jumps are associated with genuine jump risk, in the sense that if )A(t) ú 0, then
P [ sgn( )A(t) ) = - sgn( )A(t)+)M(t) ) ] > 0 ,
(2)where sgn(x) / 1 for x $0 and sgn(x) / -1 for x < 0.Equation (1) is standard, see, for example, Protter (1992), chapter 3. Equation (2) is an implication of
6 This does not appear particularly restrictive. For example, if an announcement is pending, a natural way to model the arrival time is according to a continuous hazard function. Then the probability of a jump within each (infinitesimal)instant of time is zero - there is no discrete probability mass - and by arbitrage there cannot be a predictable jump.
the no-arbitrage condition. Whenever )A(t) ú 0, there is a predictable jump in the price - the timing and size of the jump is perfectly known (just) prior to the jump event - and hence there is a trivial arbitrage (with probability one) unless there is a simultaneous jump in the martingale component, )M(t) ú 0. Moreover, the concurrent martingale jump must be large enough (with strictly positive probability) to overturn the gain associated with a position dictated by sgn()A(t)).
Proposition 1 provides a general characterization of the asset return process. We denote the
(continuously compounded) return over [t-h,t] by r(t,h) = p(t) - p(t-h). The cumulative return process from t=0 onward, r = (r(t))t 0[0,T] , is then r(t) / r(t,t) = p(t) - p(0) = A(t) + M(t). Clearly, r(t) inherits all the main properties of p(t) and may likewise be decomposed uniquely into the predictable and
integrable mean component, A , and the local martingale, M . The predictability of A still allows for quite general properties in the (instantaneous) mean process, for example it may evolve stochastically and display jumps. Nonetheless, the continuous component of the mean return must have smooth sample paths compared to those of a non-constant continuous martingale - such as a Brownian motion - and any jump in the mean must be accompanied by a corresponding predictable jump (of unknown magnitude) in the compensated jump martingale, )M . Consequently, there are two types of jumps in the return process, namely, predictable jumps where )A(t)ú0 and equation (2) applies, and purely unanticipated jumps where )A(t)=0 but )M(t)ú0. The latter jump event will typically occur when unanticipated news hit the market. In contrast, the former type of predictable jump may be associated with the release of information according to a predetermined schedule, such as macroeconomic news releases or company earnings reports. Nonetheless, it is worth noting that any slight uncertainty about the precise timing of the news (even to within a fraction of a second) invalidates the assumption of predictability and removes the jump in the mean process. If there are no such perfectly anticipated news releases, the predictable,finite variation mean return, A , may still evolve stochastically, but it will have continuous sample paths. This constraint is implicitly invoked in the vast majority of the continuous-time models employed in the literature.6
Because the return process is a semi-martingale it has an associated quadratic variation process. Quadratic variation plays a critical role in our theoretical developments. The following proposition
7 All of the properties in Proposition 2 follow, for example, from Protter (1992), chapter 2.
8 In the general case with predictable jumps the last term in equation (4) is simply replaced by
0#s #t
r i (s)r j (s),where r i (s) /
A i (s) + M i (s) explicitly incorporates both types of jumps. However, as discussed above, this case is arguable of little interest from a practical empirical perspective.enumerates some essential properties of the quadratic return variation process.7
PROPOSITION 2: For any n-dimensional arbitrage-free price process with finite mean, the quadratic variation nxn matrix process of the associated return process, [r,r] = { [r,r]t }t 0[0,T] , is well-defined. The i’th diagonal element is called the quadratic variation process of the i’th asset return while the ij’th off-diagonal element, [r i ,r j ], is called the quadratic covariation process between asset returns i and j. The quadratic variation and covariation processes have the following properties:
(i)For an increasing sequence of random partitions of [0,T], 0 = J m,0 # J m,1 # ..., such that
sup j $1(J m,j+1 - J m,j )60 and sup j $1 J m,j 6T for m 64 with probability one, we have that
lim m 64 { E j $1 [r(t v J m,j ) - r(t v J m,j-1)] [r(t v J m,j ) - r(t v J m,j-1)]’ } 6 [r,r]t ,
(3)
where t v J / min(t,J ), t 0 [0,T], and the convergence is uniform on [0,T] in probability.
(ii)If the finite variation component, A, in the canonical return decomposition in Proposition 1 is
continuous, then
[r i ,r j ]t = [M i ,M j ]t = [M i c ,M j c ]t + E 0#s #t )M i (s) )M j (s) .(4)The terminology of quadratic variation is justified by property (i) of Proposition 2. Property (ii) reflects the fact that the quadratic variation of continuous finite-variation processes is zero, so the mean
component becomes irrelevant for the quadratic variation.8 Moreover, jump components only contribute to the quadratic covariation if there are simultaneous jumps in the price path for the i ’th and j ’th asset,whereas the squared jump size contributes one-for-one to the quadratic variation. The quadratic variation process measures the realized sample-path variation of the squared return processes. Under the weak auxiliary condition ensuring property (ii), this variation is exclusively induced by the innovations to the return process. As such, the quadratic covariation constitutes, in theory, a unique and invariant ex-post realized volatility measure that is essentially model free. Notice that property (i) also suggests that we
9 This has previously been discussed by Comte and Renault (1998) in the context of estimating the spot volatility for a stochastic volatility model corresponding to the derivative of the quadratic variation (integrated volatility) process. 10 This same intuition underlies the consistent filtering results for continuous sample path diffusions in Merton (1980)and Nelson and Foster (1995).
may approximate the quadratic variation by cumulating cross-products of high-frequency returns.9 We refer to such measures, obtained from actual high-frequency data, as realized volatilities .
The above results suggest that the quadratic variation is the dominant determinant of the return covariance matrix, especially for shorter horizons. Specifically, the variation induced by the genuine return innovations, represented by the martingale component, locally is an order of magnitude larger than the return variation caused by changes in the conditional mean.10 We have the following theorem which generalizes previous results in ABDL (2001).
THEOREM 1: Consider an n-dimensional square-integrable arbitrage-free logarithmic price process with a continuous mean return, as in property (ii) of Proposition 2. The conditional return covariance matrix at time t over [t, t+h], where 0 # t # t+h # T, is then given by
Cov(r(t+h,h)*?t ) = E([r,r ]t+h - [r,r ]t *?t ) + 'A (t+h,h) + 'AM (t+h,h) + 'AM ’(t+h,h),
(5)
where 'A (t+h,h) = Cov(A(t+h) - A(t) * ?t ) and 'AM (t+h,h) = E(A(t+h) [M(t+h) - M(t)]’ *?t ).PROOF: From equation (1), r(t+h,h) = [ A(t+h) - A(t) ] + [ M(t+h) - M(t) ]. The martingale property implies E( M(t+h) - M(t) *?t ) = E( [M(t+h) - M(t)] A(t) *?t ) = 0, so, for i,j 0 {1, ..., n}, Cov( [A i (t+h)- A i (t)], [M j (t+h) - M j (t)] * ?t ) = E( A i (t+h) [M j (t+h) - M j (t)] * ?t ). It therefore follows that
Cov(r(t+h,h) * ?t ) = Cov( M(t+h) - M(t) * ?t ) + 'A (t+h,h) + 'AM (t+h,h) + 'AM ’(t+h,h). Hence, it only remains to show that the conditional covariance of the martingale term equals the expected value of the quadratic variation. We proceed by verifying the equality for an arbitrary element of the covariance
matrix. If this is the i ’th diagonal element, we are studying a univariate square-integrable martingale and by Protter (1992), chapter II.6, corollary 3, we have E[M i 2(t+h)] = E( [M i ,M i ]t+h ), so Var(M i (t+h) -M i (t) * ?t ) = E( [M i ,M i ]t+h - [M i ,M i ]t * ?t ) = E( [r i ,r i ]t+h - [r i ,r i ]t * ?t ), where the second equality follows from equation (3) of Proposition 2. This confirms the result for the diagonal elements of the covariance matrix. An identical argument works for the off-diagonal terms by noting that the sum of two square-integrable martingales remains a square-integrable martingale and then applying the reasoning to
each component of the polarization identity, [M i ,M j ]t = ? ( [M i +M j , M i +M j ]t - [M i ,M i ]t - [M j ,M j ]t ). In particular, it follows as above that E( [M i ,M j ]t+h - [M i ,M j ]t * ?t ) = ? [ Var( [M i (t+h)+M j (t+h)] -
[(M i (t)+M j (t)]* ?t ) - Var( M i (t+h) - M i (t)*?t ) - Var( M j (t+h) - M j (t)*?t ) ]= Cov( [M i (t+h) - M i (t)],
[M j (t+h) - M j (t)]*?t ). Equation (3) of Proposition 2 again ensures that this equals E( [r i ,r j ]t+h - [r i ,r j ]t * ?t ). 9
Two scenarios highlight the role of the quadratic variation in driving the return volatility process. These important special cases are collected in a corollary which follows immediately from Theorem 1.COROLLARY 1: Consider an n-dimensional square-integrable arbitrage-free logarithmic price process, as described in Theorem 1. If the mean process, {A(s) - A(t)}s 0[t,t+h] , conditional on information at time t is independent of the return innovation process, {M(u)}u 0[t,t+h], then the conditional return covariance matrix reduces to the conditional expectation of the quadratic return variation plus the conditional variance of the mean component, i.e., for 0 # t # t+h # T,
Cov( r(t+h,h) * ?t ) = E( [r,r ]t+h - [r,r ]t * ?t ) + 'A (t+h,h).
If the mean process, {A(s) - A(t)}s 0[t,t+h], conditional on information at time t is a predetermined function over [t, t+h], then the conditional return covariance matrix equals the conditional expectation of the quadratic return variation process, i.e., for 0 # t # t+h # T,
Cov( r(t+h,h) * ?t ) = E( [r,r ]t+h - [r,r ]t * ?t ).(6)Under the conditions leading to equation (6), the quadratic variation is the critical ingredient in volatility measurement and forecasting. This follows as the quadratic variation represents the actual variability of the return innovations, and the conditional covariance matrix is the conditional expectation of this quantity. Moreover, it implies that the time t+h ex-post realized quadratic variation is an unbiased estimator for the return covariance matrix conditional on information at time t .
Although the corollary’s strong implications rely upon specific assumptions, these sufficient
conditions are not as restrictive as an initial assessment may suggest, and they are satisfied for a wide set of popular models. For example, a constant mean is frequently invoked in daily or weekly return models. Equation (6) further allows for deterministic intra-period variation in the conditional mean,
11 Merton (1982) provides a similar intuitive account of the continuous record h-asymptotics . These limiting results are also closely related to the theory rationalizing the quadratic variation formulas in Proposition 2 and Theorem 1.
induced by time-of-day or other calendar effects. Of course, equation (6) also accommodates a stochastic mean process as long as it remains a function, over the interval [t, t+h], of variables in the time t
information set. Specification (6) does, however, preclude feedback effects from the random intra-period evolution of the system to the instantaneous mean. Although such feedback effects may be present in high-frequency returns, they are likely trivial in magnitude over daily or weekly frequencies, as we argue subsequently. It is also worth stressing that (6) is compatible with the existence of an asymmetric return-volatility relation (sometimes called a leverage effect), which arises from a correlation between the return innovations, measured as deviations from the conditional mean, and the innovations to the volatility process. In other words, the leverage effect is separate from a contemporaneous correlation between the return innovations and the instantaneous mean return. Furthermore, as emphasized above,equation (6) does allow for the return innovations over [t-h, t] to impact the conditional mean over [t,t+h] and onwards, so that the intra-period evolution of the system may still impact the future expected returns. In fact, this is how potential interaction between risk and return is captured in discrete-time stochastic volatility or ARCH models with leverage effects.
In contrast to equation (6), the first expression in Corollary 1 involving 'A explicitly
accommodates continually evolving random variation in the conditional mean process, although the random mean variation must be independent of the return innovations. Even with this feature present,the quadratic variation is likely an order of magnitude larger than the mean variation, and hence the former remains the critical determinant of the return volatility over shorter horizons. This observation follows from the fact that over horizons of length h , with h small, the variance of the mean return is of order h 2, while the quadratic variation is of order h . It is an empirical question whether these results are a good guide for volatility measurement at relevant frequencies.11 To illustrate the implications at a daily horizon, consider an asset return with standard deviation of 1% daily, or 15.8% annually, and a (large)mean return of 0.1%, or about 25% annually. The squared mean return is still only one-hundredth of the variance. The expected daily variation of the mean return is obviously smaller yet, unless the required daily return is assumed to behave truly erratically within the day. In fact, we would generally expect the within-day variance of the expected daily return to be much smaller than the expected daily return itself. Hence, the daily return fluctuations induced by within-day variations in the mean return are almost
certainly trivial. For a weekly horizon, similar calculations suggest that the identical conclusion applies.
12 See, for example, Karatzas and Shreve (1991), chapter 3.
The general case, covered by Theorem 1, allows for direct intra-period interaction between the
return innovations and the instantaneous mean. This occurs, for example, when there is a leverage effect,or asymmetry, by which the volatility impacts the contemporaneous mean drift. In this - for some assets - empirically relevant case, a string of negative within-period return innovations will be associated with an increase in return volatility which in turn raises the risk premium and the return drift. Relative to the corollary, the theorem involves the additional 'AM terms. Nonetheless, the intuition discussed above remain intact. It is readily established that the ik ’th component of these terms may be bounded as, {'AM (t+h,h)}i,k # {Var(A i (t+h) - A i (t) * ?t )}? {Var(M k (t+h) - M k (t) * ?t )}?, where the latter terms are of order h and h ? respectively, so that the 'AM terms are at most of order h 3/2, which again is dominated by the corresponding quadratic variation of order h . Moreover, this upper bound is quite conservative,because it allows for a correlation of unity, whereas typical correlations estimated from daily or weekly returns is much lower, de facto implying that the quadratic variation process is the main driving force behind the corresponding return volatility.
We now turn towards a more ambitious goal. Because the above results carry implications for
the measurement and modeling of return volatility, it is natural to ask whether we can also infer
something about the appropriate specification of the return generating process that builds on the realized volatility measures. Obviously, at the present level of generality, requiring only square integrability and absence of arbitrage, we cannot derive specific distributional results. Nonetheless, we may obtain a
useful benchmark under somewhat more restrictive conditions, including a continuous price process, i.e.,no jumps or, M / 0. We first recall the martingale representation theorem.12
PROPOSITION 3: For any n-dimensional square-integrable arbitrage-free logarithmic price process,p, with continuous sample path and a full rank of the associated nxn quadratic variation process, [r,r ]t we have a.s.(P) for all 0 # t # T,
r(t+h,h) = p(t+h) - p(t) = I 0h μt+s ds + I 0h F t+s dW(s) ,(7)
where μs denotes an integrable predictable nx1 dimensional vector, F s = ( F (i,j),s )i,j=1,...,n is a nxn matrix, W(s) is a nx1 dimensional standard Brownian motion, integration of a matrix or vector w.r.t. a scalar denotes component-wise integration, so that
13 See Karatzas and Shreve (1991), section 3.4.
I 0h μt+s ds = ( I 0h μ1,t+s ds, ... , I 0h μn,t+s ds )’ ),
and integration of a matrix w.r.t. a vector denotes component-wise integration of the associated vector, so that
I 0h F t+s dW(s) = ( I 0h E j=1,..,n F (1,j),t+s dW j (s) , ... , I 0h E j=1,..,n F (n,j),t+s dW j (s) )’.(8)
Moreover, we have
P[ I 0h (F (i,j),t+s )2 ds < 4 ] = 1, 1 # i, j # n. (9)
Finally, letting S s = F s F s ’, the increments to the quadratic return variation process take the form
[r,r ]t+h - [r,r ]t = I 0h S t+s ds .(10)
The requirement that the nxn matrix [r,r]t is of full rank for all t, implies that no asset is redundant at any time, so that no individual asset return can be spanned by a portfolio created by the remaining assets. This condition is not restrictive; if it fails, a parallel representation may be achieved on an extended
probability space.13
We are now in position to state a distributional result that inspires our empirical modeling of the
full return generating process in section 6 below. It extends the results recently discussed by Barndorff-Nielsen and Shephard (2002) by allowing for a more general specification of the conditional mean process and by accommodating a multivariate setting. It should be noted that for volatility forecasting, as
discussed in sections 5 and 6.1 below, we do not need the auxiliary assumptions invoked here.
THEOREM 2: For any n-dimensional square-integrable arbitrage-free price process with continuous sample paths satisfying Proposition 3, and thus representation (7), with conditional mean and volatility processes μs and F s independent of the innovation process W(s) over [t,t+h], we have
r(t+h,h) * F { μt+s , F t+s }s 0[0,h] - N( I 0h μt+s ds , I 0h S t+s ds ),(11)
where F { μt+s , F t+s }s 0[0,h] denotes the F -field generated by ( μt+s , F t+s )s 0[0,h] .
PROOF: Clearly, r(t+h,h) - I 0h μt+s ds = I 0h F t+s dW(s) and E(I 0h F t+s dW(s) *{ μt+s , t+s }s 0[0,h] ) = 0. We
proceed by establishing the normality of I 0h F t+s dW(s) conditional on the volatility path { t+s }s 0[0,h]. The
integral is n -dimensional, and we define I 0h F t+s dW(s) = (I 0h (F (1),t+s )’dW(s), .., I 0h (F (n),t+s )’dW(s))’, where
F (i),s = (F (i,1),s , ... , F (i,n),s )’, so that I 0h (F (i),t+s )’ dW(s) denotes the i ’th element of the nx1 vector in equation
(8). The vector is multivariate normal if and only if any (non-zero) linear combination of the elements are univariate normal. Each element of the vector represents a sum of independent stochastic integrals, as also detailed in equation (8). Any non-zero linear combination of this n -dimensional vector will thus produce another linear combination of the same n independent stochastic integrals. Moreover, the linear combination will be non-zero given the full rank condition of Proposition 3. It will therefore be normally distributed if each constituent component of the original vector in equation (8) is normally distributed conditional on the volatility path. A typical element of the sums in equation (8), representing the j ’th volatility factor loading of asset i over [t,t+h], takes the form, I i,j (t+h,h) / I 0h F (i,j),t+s dW j (s), for 1 # i, j #n. Obviously, I i,j (t) / I i,j (t,t) is a continuous local martingale, and then by the "change of time" result (see, e.g., Protter (1992), Chapter II, Theorem 41), it follows that I i,j (t) = B( [I i,j , I i,j ]t ), where B(t)denotes a standard univariate Brownian motion. Further, we have I i,j (t+h,h) = I i,j (t+h) - I i,j (t) = B([I i,j , I i,j ]t+h ) - B([I i,j , I i,j ]t ), and this increment to the Brownian motion is distributed N(0, [I i,j , I i,j ]t+h - [I i,j , I i,j ]t ). Finally, the quadratic variation governing the variance of the Gaussian distribution is readily determined
to be [I i,j , I i,j ]t+h - [I i,j , I i,j ]t = I 0h (F (i,j),t+s )2 ds (see, e.g., Protter (1992), Chapter II.6), which is finite by
equation (9) of Proposition 3. Conditional on the ex-post realization of the volatility path, the quadratic variation is given (measurable), and the conditional normality of I i,j (t+h,h) follows. Because both the mean and the volatility paths are independent of the return innovations over [t,t+h], the mean is readily determined from the first line of the proof. This verifies the conditional normality asserted in equation
(11). The only remaining issue is to identify the conditional return covariance matrix. For the ik ’th element of the matrix we have
Cov[ I 0h (F (i),t+s )’ dW(s) , I 0h (F (k),t+s )’ dW(s) * { μt+s , t+s }s 0[0,h] ]
= E[ E j=1,..,n I 0h F (i,j),t+s dW j (s) @ E j=1,..,n I 0h F (k,j),t+s dW j (s) * { μt+s , t+s }s 0[0,h] ]
= E j=1,..,n E[ I 0h F (i,j),t+s F (k,j),t+s ds * { μt+s ,
t+s }s 0[0,h] ] = E j=1,..,n I 0h F (i,j),t+s F (k,j),t+s ds
= I 0h (F (i),t+s )’ F (k),t+s ds
= ( I 0h F t+s (F t+s )’ ds )ik
= (I 0h t+s ds )ik .
14 See, for example, Andersen, Benzoni and Lund (2002), Bates (2000), Bakshi, Cao and Chen (1997), Pan (2002),and Eraker, Johannes and Polson (2002).
This confirms that each element of the conditional return covariance matrix equals the corresponding element of the variance term in equation (11). 9
Notice that the distributional characterization in Theorem 2 is conditional on the ex-post sample-path realization of ( μs , F s )s 0[t,t+h] . The theorem may thus appear to be of little practical relevance, because such realizations typically are not observable. However, Proposition 2 and equation (10) suggest that we may construct approximate measures of the realized quadratic variation, and hence of the conditional return variance, directly from high-frequency return observations. In addition, as discussed previously,for daily or weekly returns, the conditional mean variation is negligible relative to the return volatility. Consequently, ignoring the time variation of the conditional mean, it follows by Theorem 2 that the distribution of the daily returns, say, is determined by a normal mixture with the daily realized quadratic return variation governing the mixture.
Given the auxiliary assumptions invoked in Theorem 2, the normal mixture distribution is strictly only applicable if the price process has continuous sample paths and the volatility and mean processes are independent of the within-period return innovations. The latter implies a conditionally symmetric return distribution. This raises two main concerns. First, some recent evidence suggests the possibility of discrete jumps in asset prices, rendering sample paths discontinuous.14 But these studies also find that jumps are infrequent and have a jump size distribution about which there is little consensus. Second, for some asset classes there is evidence of leverage effects that may indicate a correlation between concurrent return and volatility innovations. However, as argued above, such contemporaneous correlation effects are likely to be unimportant quantitatively at the daily or weekly horizon. Indeed, Theorem 2 allows for the more critical impact leading from the current return innovations to the volatility in subsequent
periods, beyond time t+h , corresponding to the effect captured in the related discrete-time literature. We thus retain the normal mixture distribution as a natural starting point for our empirical work. However, if the return-volatility asymmetry is important and the forecast horizon, h , relatively long, say monthly or quarterly, then one may expect the empirical return distribution to display asymmetries that are incompatible with the symmetric return distribution (conditional on time t information) implied by Theorem 2. One simple diagnostic is to check if the realized volatility-standardized returns over the relevant horizon fail to be normally distributed, as this will speak to the importance of incorporating
jumps and/or contemporaneous return innovation-volatility interactions into the modeling framework.
In summary, the arbitrage-free setting imposes a semi-martingale structure that leads directly to the representation in Proposition 1 and the associated quadratic variation in Proposition 2. In addition, property (i) and equation (3) in Proposition 2 suggests a practical way to approximate the quadratic variation. Theorem 1 and the associated Corollary 1 reveal the intimate relation between the quadratic variation and the return volatility process. For the continuous sample path case, we further obtain the representation in equation (7), and the quadratic variation reduces by equation (10) to I 0h S t+s ds , which is often referred to as the integrated volatility . Theorem 2 consequently strengthens Theorem 1 by showing that the realized quadratic variation is not only a useful estimator of the ex-ante conditional volatility, but also, under auxiliary assumptions, identical to the realized integrated return volatility over the relevant horizon. Moreover, the theorem delivers a reference distribution for appropriately standardized returns. Combined, the results provide a general framework for integration of high-frequency intraday data into the measurement, modeling and forecasting of daily and lower frequency return volatility and return distributions, tasks to which we now turn.
3. MEASURING REALIZED EXCHANGE RATE VOLATILITY
Practical implementation of the procedures suggested by the theory in section 2 must confront the fact that no financial market provides a frictionless trading environment with continuous price recording. Consequently, the notion of quadratic return variation is an abstraction that, strictly speaking, cannot be observed. Nevertheless, the continuous-time arbitrage-free framework directly motivates the creation of our return series and associated volatility measures from high-frequency data. We do not claim that this provides exact counterparts to the (non-existing) corresponding continuous-time quantities. Instead, we use the theory to guide and inform collection of the data, transformation of the data into volatility
measures, and selection of the models used to construct conditional return volatility and density forecasts,after which we assess the usefulness of the theory through the lens of predictive accuracy.
3.1 Data
Our empirical analysis focuses on the spot exchange rates for the U.S. dollar, the Deutschemark and the
Japanese yen.15 The raw data consists of all interbank DM/$ and ￥/$ bid/ask quotes displayed on the Reuters FXFX screen during the sample period, December 1, 1986 through June 30, 1999.16 These quotes are merely indicative (that is, non-binding) and subject to various market microstructure "frictions," including strategic quote positioning and standardization of the size of the quoted bid/ask spread. Such features are generally immaterial when analyzing longer horizon returns, but they may distort the statistical properties of the underlying "equilibrium" high-frequency intraday returns.17 The sampling frequency at which such considerations become a concern is intimately related to market activity. For our exchange rate series, preliminary analysis based on the methods of ABDL (2000b) suggests that the use of equally-spaced thirty-minute returns strikes a satisfactory balance between the accuracy of the continuous-record asymptotics underlying the construction of our realized volatility measures on the one hand, and the confounding influences from market microstructure frictions on the other.18 The actual construction of the returns follows Müller et al. (1990) and Dacorogna et al. (1993). First, we calculate thirty-minute prices from the linearly interpolated logarithmic average of the bid and ask quotes for the two ticks immediately before and after the thirty-minute time stamps throughout the global 24-hour trading day. Second, we obtain thirty-minute returns as the first difference of the logarithmic prices.19 In order to avoid modeling specific weekend effects, we exclude all of the returns from Friday 21:00 GMT until Sunday 21:00 GMT. Similarly, to avoid complicating the inference by the decidedly slower trading activity during certain holiday periods, we delete a number of other inactive days from the sample. We are left with a bivariate series of thirty-minute DM/$ and ￥/$ returns spanning a total of 3,045 days. In order to explicitly distinguish the empirically constructed continuously
15 Before the advent of the Euro, the dollar, Deutschemark and yen were the most actively traded currencies in the foreign exchange market, with the DM/$ and ￥/$ accounting for nearly fifty percent of the daily trading volume, according to a 1996 survey by the Bank for International Settlements.
16 The data comprise several million quotes kindly supplied by Olsen & Associates. Average daily quotes number approximately 4,500 for the Deutschemark and 2,000 for the Yen.
17See Bai, Russell and Tiao (2000) for discussion and quantification of various aspects of microstructure bias in the context of realized volatility.
18 An alternative approach would be to utilize all of the observations by explicitly modeling the high-frequency market microstructure. That approach, however, is much more complicated and subject to numerous pitfalls of its own.
19 We follow the standard convention of the interbank market by measuring the exchange rates and computing the corresponding rates of return from the prices of $1 expressed in terms of DM and ￥, i.e., DM/$ and ￥/$. Similarly, we express the cross rate as the price of one DM in terms of ￥, i.e., ￥/DM.
20 All of the empirical results in ABDL (2000a, 2001), which in part motivate our approach, were based on data for the in-sample period only, justifying the claim that our forecast evaluation truly is “out-of-sample.”
compounded discretely sampled returns and corresponding volatility measures from the theoretical counterparts in section 2, we will refer to the former by time subscripts. Specifically for the half-hour returns r t+),), t = ), 2), 3), ..., 3045, where ) = 1/48 . 0.0208. Also, for notational simplicity we label the corresponding daily returns by a single time subscript, so that r t+1 / r t+1,1 / r t+),) + r t+2),) + ... r t+1,) for t = 1, 2, ..., 3045. Finally, we partition the full sample period into an in-sample estimation period
covering the 2,449 days from December 1, 1986 through December 1, 1996, and a genuine out-of-sample forecast evaluation period covering the 596 days from December 2, 1996 through June 30, 1999.20
3.2 Construction of Realized Volatilities
The preceding discussion suggests that meaningful ex-post interdaily volatility measures may be
constructed by cumulating cross-products of intraday returns sampled at an appropriate frequency, such as thirty minutes. In particular, based on the bivariate vector of thirty-minute DM/$ and ￥/$ returns, i.e.,with n = 2, we define the h-day realized volatility, for t = 1, 2, ..., 3045, ) = 1/48, by
V t,h / E j=1,..,h/) r t-h+j·),) · r t N -h+j·),) = R t,h N R t,h ,(12)where the (h/))xn matrix, R t,h , is defined by R t,h N / (r t-h+),), r t-h+2·),) , ... , r t,)). As before, we simplify the notation for the daily horizon by defining V t /V t,1. The V t,h measure constitutes the empirical counterpart to the h-period quadratic return variation and, for the continuous sample path case, the integrated
volatility. In fact, by Proposition 2, as the sampling frequency of the intraday returns increases, or ) 6 0,V t,h converges almost surely to the quadratic variation.
One issue frequently encountered in multivariate volatility modeling is that constraints must be imposed to guarantee positive definiteness of estimated covariance matrices. Even for relatively low-dimensional cases such as three or four assets, imposition and verification of conditions that guarantee positive definiteness can be challenging; see, for example, the treatment of multivariate GARCH
processes in Engle and Kroner (1995). Interestingly, in contrast, it is straightforward to establish positive definiteness of V t,h . The following lemma follows from the correspondence between our realized
volatility measures and standard unconditional sample covariance matrix estimators which, of course, are positive semi-definite by construction.
LEMMA 1: If the columns of R t,h are linearly independent, then the realized covariance matrix, V t,h ,defined in equation (12) is positive definite.
PROOF: It suffices to show that a N V t,h a > 0 for all non-zero a . Linear independence of the columns of R t,h ensures that b t,h = R t,h a ú 0, ?a 0?n \{0}, and in particular that at least one of the elements of b t,h is non-zero. Hence a N V t,h a = a N R t,h N R t,h a = b t,h N b t,h = E j=1,..,h/) (b t,h )j 2 > 0, ?a 0?n \{0}. 9
The fact that positive definiteness of the realized covariance matrix is virtually assured, even in high-dimensional settings, is encouraging. However, the lemma also points to a problem that will arise for extremely high-dimensional systems. The assumption of linear independence of the columns of R t,h ,although weak, will ultimately be violated as the dimension of the price vector increases relative to the sampling frequency of the intraday returns. Specifically, for n > h/ the rank of the R t,h matrix is obviously less than n , so R t N R t = V t will not have full rank and it will fail to be positive definite. Hence,although the use of V t facilitates rigorous measurement of conditional volatility in much higher dimensions than is feasible with most alternative approaches, it does not allow the dimensionality to
become arbitrarily large. Concretely, the use of thirty-minute returns, corresponding to 1/) = 48 intraday observations, for construction of daily realized volatility measures, implies that positive definiteness of V t requires n , the number of assets, to be no larger than 48.
Because realized volatility V t is observable, we can model and forecast it using standard and
relatively straightforward time-series techniques. The diagonal elements of V t , say v t,1 and v t,2 ,correspond to the daily DM/$ and ￥/$ realized variances, while the off-diagonal element, say v t,12 ,represents the daily realized covariance between the two rates. We could then model vech(V t ) = (v t,1, v t,12,v t,2)N directly but, for reasons of symmetry, we replace the realized covariance with the realized variance of the ￥/DM cross rate which may be done, without loss of generality, in the absence of triangular arbitrage, resulting in a system of three realized volatilities.
Specifically, by absence of triangular arbitrage, the continuously compounded return on the ￥/DM cross rate must be equal to the difference between the ￥/$ and DM/$ returns, which has two key
consequences. First, it implies that, even absent direct data on the ￥/DM rate, we can infer the cross rate using the DM/$ and ￥/$ data. Consequently, the realized cross-rate variance, v t,3, may be constructed by summing the implied thirty-minute squared cross-rate returns,
21 Similarly, the realized correlation between the two dollar rates is given by t,12 = ?·( v t,1 + v t,2 - v t,3 ) / ( v t,1 · v t,2 )?. 22 The no-triangular-arbitrage restrictions are, of course, not available outside the world of foreign exchange. However, these restrictions are in no way crucial to our general approach, as the realized variances and covariances could all be modeled directly. We choose to substitute out the realized covariance in terms of the cross-rate because it makes for a clean and unified presentation of the empirical work, allowing us to exploit the approximate lognormality of the realized variances (discussed below). 23 For a prescient early contribution along these lines, see also Zhou (1996).
v t,3 = E j=1,..,1/) [ ( -1 , 1 )N · r t-1+j·),) ] 2 .
(13)Second, because this implies that v t,3 = v t,1 + v t,2 - 2 v t,12, we can infer the realized covariance from the three realized volatilities,21
v t,12 = ?·( v t,1 + v t,2 - v t,3 ) .
(14)
Building on this insight, we infer the covariance from the three variances, v t / ( v t,1, v t,2, v t,3 )' and the identity in equation (14) instead of directly modeling vech(V t ).22We now turn to a discussion of the pertinent empirical regularities that guide our specification of the trivariate forecasting model for the three DM/$, ￥/$, and ￥/DM volatility series.
4. PROPERTIES OF EXCHANGE RATE RETURNS AND REALIZED VOLATILITIES
The in-sample distributional features of the DM/$ and ￥/$ returns and the corresponding realized
volatilities have been characterized previously by ABDL (2000a, 2001).23 Here we briefly summarize those parts of the ABDL results that are relevant for the present inquiry. We also provide new results for the ￥/DM cross rate volatility and an equally-weighted portfolio that explicitly incorporate the realized covariance measure discussed above.
4.1 Returns
The statistics in the top panel of Table 1 refer to the two daily dollar-denominated returns, r t,1 and r t,2, and the equally-weighted portfolio, ?@ (r t,1+r t,2 ). As regards unconditional distributions, all three return series are approximately symmetric with zero mean. However, the sample kurtoses indicate more probability mass in the center and in the tails of the distribution relative to the normal, which is confirmed by the kernel density estimates shown in Figure 1. As regards conditional distributions, the Ljung-Box test
24 Under the null hypothesis of white noise, the reported Ljung-Box statistics are distributed as chi squared with twenty degrees of freedom. The five percent critical value is 31.4, and the one percent critical value is 37.6.
25 Similar results obtain for the multivariate standardization V t -1/2 r t , where [·] -1/2 refers to the Cholesky factor of the inverse matrix, as documented in ABDL (2000a).
26 This same observation also underlies the ad hoc multiplication factors often employed by practioners in the construction of VaR forecasts.
27 Note that the mixed normality result in Theorem 2 does not generally follow by a standard central limit theorem except under special conditions as delineated in the theorem.
statistics indicate no serial correlation in returns, but strong serial correlation in squared returns.24 The results are entirely consistent with the extensive literature documenting fat tails and volatility clustering in asset returns, dating at least to Mandelbrot (1963) and Fama (1965).
The statistics in the bottom panel of Table 1 refer to the distribution of the standardized daily
returns r t,1@ v t,1 -1/2 and r t,2 @ v t,2 -1/2, along with the standardized daily equally-weighted portfolio returns ? @(r t,1+r t,2) @ (?@ v t,1 +?@ v t,2 +?@ v t,12 )-? , or equivalently by equation (14), ?@ (r t,1+r t,2) @ (?@ v t,1 +?@ v t,2 - ?@ v t,3)-?. The standardized-return results provide striking contrasts to the raw-return results. First, the sample kurtoses indicate that the standardized returns are well approximated by a Gaussian distribution, as
confirmed by the kernel density estimates in Figure 1, which clearly convey the approximate normality. Second, in contrast to the raw returns, the standardized returns display no evidence of volatility clustering.25
Of course, the realized volatility used for standardizing the returns is only observable ex post.
Nonetheless, the result is in stark contrast to the typical finding that, when standardizing daily returns by the one-day-ahead forecasted variance from ARCH or stochastic volatility models, the resulting
distributions are invariably leptokurtic, albeit less so than for the raw returns; see, for example, Baillie and Bollerslev (1989) and Hsieh (1989). In turn, this has motivated the widespread adoption of volatility models with non-Gaussian conditional densities, as suggested by Bollerslev (1987).26 The normality of the standardized returns in Table 1 and Figure 1 suggests a different approach: a fat-tailed normal
mixture distribution governed by the realized volatilities, which moreover is consistent with Theorem 2.27 We now turn to a discussion of the distribution of the realized volatilities.
4.2 Realized Volatilities
The statistics in the top panel of Table 2 summarize the distribution of the realized volatilities, v t,i 1/2 , for each of the three exchange rates: DM/$, ￥/$ and ￥/DM. All the volatilities are severely right-skewed and
1.什么是船舶的浮性？ 船舶在各种装载情况下具有漂浮在水面上保持平衡位置的能力 2.什么是静水力曲线？其使用条件是什么？包括哪些曲线？怎样用静水力曲线查某一吃水时的排水量和浮心位置？ 船舶设计单位或船厂将这些参数预先计算出并按一定比例关系绘制在同一张图中；漂心坐标曲线、排水体积曲线；当已知船舶正浮或可视为正浮状态下的吃水时，便可在静水力曲线图中查得该吃水下的船舶的排水量、漂心坐标及浮心坐标等 3.什么是漂心？有何作用？平行沉浮的条件是什么？ 船舶水线面积的几何中心称为漂心；根据漂心的位置，可以计算船舶在小角度纵倾时的首尾吃水；船舶在原水线面漂心的铅垂线上少量装卸重量时，船舶会平行沉浮；（1）必须为少量装卸重物（2）装卸重物p的重心必须位于原水线面漂心的铅垂线上 4.什么是TPC？其使用条件如何？有何用途？ 每厘米吃水吨数是指船在任意吃水时，船舶水线面平行下沉或上浮1cm时所引起的排水量变化的吨数；已知船舶在吃水d时的tpc数值，便可迅速地求出装卸少量重物p之后的平均吃水变化量，或根据吃水的改变量求船舶装卸重物的重量 5.什么是船舶的稳性？ 船舶在使其倾斜的外力消除后能自行回到原来平衡位置的性能。 6.船舶的稳性分几类？ 横稳性、纵稳性、初稳性、大倾角稳性、静稳性、动稳性、完整稳性、破损稳性 7.船舶的平衡状态有哪几种？船舶处于稳定平衡状态、随遇平衡状态、不稳定平衡状态的条件是什么？ 稳定平衡、不稳定平衡、随遇平衡 当外界干扰消失后，船舶能够自行恢复到初始平衡位置，该初始平衡状态称为稳定平衡当外界干扰消失后，船舶没有自行恢复到初始平衡位置的能力，该初始平衡状态称为不稳定平衡 当外界干扰消失后，船舶依然保持在当前倾斜状态，该初始平衡状态称为随遇平衡8.什么是初稳性？其稳心特点是什么？浮心运动轨迹如何？ 指船舶倾斜角度较小时的稳性；稳心原点不动；浮心是以稳心为圆心，以稳心半径为半径做圆弧运动 9.什么是稳心半径？与吃水关系如何？ 船舶在小角度倾斜过程中，倾斜前、后的浮力作用线的交点，与倾斜前的浮心位置的线段长，称为横稳性半径！随吃水的增加而逐渐减少 10.什么是初稳性高度GM？有何意义？影响GM的因素有哪些？从出发港到目的港整个航行过程中有多少个GM？ 重心至稳心间的距离；吃水和重心高度；许多个 11.什么是大倾角稳性？其稳心有何特点？ 船舶作倾角为10°-15以上倾斜或大于甲板边缘入水角时点的稳性 12.什么是静稳性曲线？有哪些特征参数？ 描述复原力臂随横倾角变化的曲线称为静稳性曲线；初稳性高度、甲板浸水角、最大静复原力臂或力矩、静稳性曲线下的面积、稳性消失角 13.什么是动稳性、静稳性？ 船舶在外力矩突然作用下的稳性。船舶在外力矩逐渐作用下的稳性。 14.什么是自由液面？其对稳性有何影响？减小其影响采取的措施有哪些？ 可自由流动的液面称为自由液面；使初稳性高度减少；（）减小液舱宽度（2）液舱应
文档来源为:从网络收集整理.word版本可编辑.欢迎下载支持. 《船舶原理与结构》课程教学大纲 一、课程基本信息 1、课程代码：NA325 2、课程名称（中/英文）：船舶原理与结构/Principles of Naval Architecture and Structures 3、学时/学分：60/3.5 4、先修课程：《高等数学》《大学物理》《理论力学》 5、面向对象：轮机工程、交通运输工程 6、开课院（系）、教研室：船舶海洋与建筑工程学院船舶与海洋工程系 7、教材、教学参考书： 《船舶原理》，刘红编，上海交通大学出版社，2009.11。 《船舶原理》（上、下册），盛振邦、刘应中主编，上海交通大学出版社，2003、2004。 《船舶原理与结构》，陈雪深、裘泳铭、张永编，上海交通大学出版社，1990.6。 《船舶原理》，蒋维清等著，大连海事大学出版社，1998.8。 相关法规和船级社入级规范。 二、课程性质和任务 《船舶原理与结构》是轮机工程和交通运输工程专业的一门必修课。它的主要任务是通过讲课、作业和实验环节，使学生掌握船舶静力学、船舶阻力、船舶推进、船舶操纵性与耐波性和船体结构的基本概念、基本原理、基本计算方法，培养学生分析、解决问题的基本能力，为今后从事工程技术和航运管理工作，打下基础。 本课程各教学环节对人才培养目标的贡献见下表。
三、教学内容和基本要求 课程教学内容共54学时，对不同的教学内容，其要求如下。 1、船舶类型 了解民用船舶、军用舰船、高速舰船的种类、用途和特征。 2、船体几何要素 了解船舶外形和船体型线力的一般特征，掌握船体主尺度、尺度比和船型系数。 3、船舶浮性 掌握船舶的平衡条件、船舶浮态的概念；掌握船舶重量、重心位置、排水量和浮心位置的计算方法；掌握近似数值计算方法—梯形法和辛普森法。 2
25years of time series forecasting Jan G.De Gooijer a,1,Rob J.Hyndman b ,* a Department of Quantitative Economics,University of Amsterdam,Roetersstraat 11,1018WB Amsterdam,The Netherlands b Department of Econometrics and Business Statistics,Monash University,VIC 3800,Australia Abstract We review the past 25years of research into time series forecasting.In this silver jubilee issue,we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982–1985and International Journal of Forecasting 1985–2005).During this period,over one third of all papers published in these journals concerned time series forecasting.We also review highly influential works on time series forecasting that have been published elsewhere during this period.Enormous progress has been made in many areas,but we find that there are a large number of topics in need of further development.We conclude with comments on possible future research directions in this field.D 2006International Institute of Forecasters.Published by Elsevier B.V .All rights reserved. Keywords:Accuracy measures;ARCH;ARIMA;Combining;Count data;Densities;Exponential smoothing;Kalman filter;Long memory;Multivariate;Neural nets;Nonlinearity;Prediction intervals;Regime-switching;Robustness;Seasonality;State space;Structural models;Transfer function;Univariate;V AR 1.Introduction The International Institute of Forecasters (IIF)was established 25years ago and its silver jubilee provides an opportunity to review progress on time series forecasting.We highlight research published in journals sponsored by the Institute,although we also cover key publications in other journals.In 1982,the IIF set up the Journal of Forecasting (JoF ),published with John Wiley and Sons.After a break with Wiley in 1985,2the IIF decided to start the International Journal of Forecasting (IJF ),published with Elsevier since 1985.This paper provides a selective guide to the literature on time series forecasting,covering the period 1982–2005and summarizing over 940papers including about 340papers published under the b IIF-flag Q .The proportion of papers that concern time series forecasting has been fairly stable over time.We also review key papers and books published else-where that have been highly influential to various developments in the field.The works referenced 0169-2070/$-see front matter D 2006International Institute of Forecasters.Published by Elsevier B.V .All rights reserved.doi:10.1016/j.ijforecast.2006.01.001 *Corresponding author.Tel.:+61399052358;fax:+61399055474. E-mail addresses: j.g.degooijer@uva.nl (J.G.De Gooijer),Rob.Hyndman@https://www.doczj.com/doc/9213812626.html,.au (R.J.Hyndman).1 Tel.:+31205254244;fax:+31205254349. 2 The IIF was involved with JoF issue 44:1(1985). International Journal of Forecasting 22(2006)443– 473 https://www.doczj.com/doc/9213812626.html,/locate/ijforecast
武汉理工大学交通学院2020年研究生入学考试大纲 2020年研究生入学考试考纲 《船舶流体力学》考试说明 一、考试目的 掌握船舶流体力学相关知识是研究船舶水动力学问题的基础。本门考试的目的是考察考生掌握船舶流体力学相关知识的水平，以保证被录取者掌握必要的基础知识，为从事相关领域的研究工作打下基础。 考试对象：2020年报考武汉理工大学交通学院船舶与海洋工程船舶性能方向学术型和专业型研究生的考生。 二、考试要点 （1）流体的基本性质和研究方法 连续介质概念；流体的基本属性；作用在流体上的力的特点和表述方法。 （2）流体静力学 作用在流体上的力；流体静压强及特性；压力体的概念，及静止流体对壁面作用力计算。 （3）流体运动学 研究流体运动的两种方法；流体流动的分类；流线、流管等基本概念。 （4）流体动力学 动量定理；伯努利方程及应用。 （5）船舶静力学 船型系数；浮性；稳性分类；移动物体和液舱内自由液面对船舶稳性的影响；初稳性高计算。 （6）船舶快速性 边界层概念；船舶阻力分类；尺度效应，及船模阻力换算为实船阻力；方尾和球鼻艏对阻力的影响；伴流和推力减额的概念；空泡现象对螺旋桨性能的影响；浅水对船舶性能的影响。 三、考试形式与试卷结构 1. 答卷方式：闭卷，笔试。 2. 答题时间：180分钟。 3. 试卷分数：总分为150分。 4. 题型：填空题（20×1分=20分）；名词解释（10×3分=30分）；简述题（4×10分=40分）；计算题（4×15分=60分）。 四、参考书目 1. 熊鳌魁等编著，《流体力学》，科学出版社，2016。 2. 盛振邦、刘应中，《船舶原理》（上、下册）中的船舶静力学、船舶阻力、船舶推进部分，上海交通大学出版社，2003。 1
《船舶原理与结构》课程教学大纲 一、课程基本信息 1、课程代码： 2、课程名称（中/英文）：船舶原理与结构/Principles of Naval Architecture and Structures 3、学时/学分：60/3.5 4、先修课程：《高等数学》《大学物理》《理论力学》 5、面向对象：轮机工程、交通运输工程 6、开课院（系）、教研室：船舶海洋与建筑工程学院船舶与海洋工程系 7、教材、教学参考书： 《船舶原理》，刘红编，上海交通大学出版社，2009.11。 《船舶原理》（上、下册），盛振邦、刘应中主编，上海交通大学出版社，2003、2004。 《船舶原理与结构》，陈雪深、裘泳铭、张永编，上海交通大学出版社，1990.6。 《船舶原理》，蒋维清等著，大连海事大学出版社，1998.8。 相关法规和船级社入级规范。 二、课程性质和任务 《船舶原理与结构》是轮机工程和交通运输工程专业的一门必修课。它的主要任务是通过讲课、作业和实验环节，使学生掌握船舶静力学、船舶阻力、船舶推进、船舶操纵性与耐波性和船体结构的基本概念、基本原理、基本计算方法，培养学生分析、解决问题的基本能力，为今后从事工程技术和航运管理工作，打下基础。 本课程各教学环节对人才培养目标的贡献见下表。
三、教学内容和基本要求 课程教学内容共54学时，对不同的教学内容，其要求如下。 1、船舶类型 了解民用船舶、军用舰船、高速舰船的种类、用途和特征。 2、船体几何要素 了解船舶外形和船体型线力的一般特征，掌握船体主尺度、尺度比和船型系数。 3、船舶浮性 掌握船舶的平衡条件、船舶浮态的概念；掌握船舶重量、重心位置、排水量和浮心位置的计算方法；掌握近似数值计算方法—梯形法和辛普森法。
2019年上海交通大学船舶与海洋工程考研良心经验 我本科是武汉理工大学的，学的也是船舶与海洋工程，成绩属于中等偏上吧，也拿过两次校三等奖学金，六级第二次才考过。 由于种种原因，我到了8月份才终于下定决心考交大船海并开始准备，只有4个多月，时间比较紧迫。但只要你下定决心，什么时候开始都不算晚，也不要因为复习得不好，开始的晚了就降低学校的要求，放弃了自己的名校梦。每个人情况不一样，自己好好做决定，即使暂时难以决定，也要早点开始复习。决定是在可以在学习过程中做的，学习计划也是可以根据自己的情况更改的。所以即使不知道考哪，每天学习多久，怎样安排学习计划，那也要先开始，这样你才能更清楚学习的难度和量。万事开头难，千万不要拖。由于准备的晚怕靠个人来不及，于是在朋友推荐下我报了新祥旭专业课的一对一，个人觉得一对一比班课好，新祥旭刚好之专门做一对一比较专业，所以果断选择了新祥旭，如果有同学需要可以加卫：chentaoge123 上交船海考研学硕和专硕的科目是一样的，英语一、数学一、政治、船舶与海洋工程专业基础（801）。英语主要是背单词和刷真题，我复习的时间不多，背单词太花时间，就慢慢放弃了，就只是刷真题，真题中出现的陌生单词，都抄到笔记本上背，作文要背一下，准备一下套路，最好自己准备。英语考时感觉着超级简单，但只考了65分，还是很郁闷的。数学是重中之重，我八月份开时复习，直接上手复习全书，我觉得没有必要看课本，毕竟太基础，而且和考研重点不一样，看了课本或许也觉得很难，但是和考研不沾边。计划的是两个月复习一遍，开始刷题，然后一边复习其他的，可是计划跟不上变化，数学基础稍差，复习的较慢，我又不想为了赶进度而应付，某些地方掌握多少自己心里有数，若是只掌握个大概，也不利于后面的学习。所以自打复习开始，我就没放下过数学，期间也听一些网课，高数听张宇、武忠祥的，线代肯定是李永乐，概率论听王式安，课可以听，但最主要还是自己做题，我只听了一些强化班，感觉自己复习不好的地方听了一下。我真题到了11月中旬才开始做，实在是太晚，我8月开始复习时网上就有人说真题刷两遍了，能不慌吗，但再慌也要淡定，不要因此为了赶进度而自欺欺人，做什么事外界的声音是一回事，自己的节奏要自己把握好，不然
第一章 船体形状 三个基准面（1）中线面（xoz 面）横剖线图（2）中站面（yoz 面）总剖线图（3） 基平面 （xoy 面）半宽水线图 型线图：用来描述（绘）船体外表面的几何形状。 船体主尺度 船长 L 、船宽（型宽）B 、吃水d 、吃水差t 、 t = dF – dA 、首吃水dF 、尾吃水dA 、平均吃水dM 、dM = （dF + dA ）/ 2 } 、型深 D 、干舷 F 、（F = D – d ） 主尺度比 L / B 、B / d 、D / d 、B / D 、L / D 船体的三个主要剖面：设计水线面、中纵剖面、中横剖面 1.水线面系数Cw ：船舶的水线面积Aw 与船长L,型宽B 的乘积之比。 2.中横剖面系数Cm ：船舶的中横剖面积Am 与型宽B 、吃水d 二者的乘积之比值。 3.方型系数Cb ：船舶的排水体积V,与船长L,型宽B 、吃水d 三者的乘积之比值。 4. 棱形系数（纵向）Cp ：船舶排水体积V 与中横剖面积Am 、船长L 两者的乘积之比值。 5. 垂向棱形系数 Cvp ：船舶排水体积V 与水线面积Aw 、吃水d 两者的乘积之比值。 船型系数的变化区域为：∈（ 0 ，1 ] 第二章 船体计算的近似积分法 梯形法则约束条件（限制条件）：（1） 等间距 辛氏一法则通项公式 约束条件（限制条件）：（1）等间距 （2）等份数为偶数 (纵坐标数为奇数 )2m+1 辛氏二法则 约束条件（限制条件）（1）等间距 （2）等份数为3 3m+1 梯形法：（1）公式简明、直观、易记 ；（2）分割份数较少时和曲率变化较大时误差偏大。 辛氏法：（1）公式较复杂、计算过程多； （2）分割份数较少时和曲率变化较大时误差相对较小。 第三章 浮性 船舶（浮体）的漂浮状态：（1 ）正浮（2）横倾（3）纵倾（4）纵横倾 排水量：指船舶在水中所排开的同体积水的重量。 平行沉浮条件：少量装卸货物P ≤ 10 ℅D 每厘米吃水吨数： TPC = 0.01×ρ×Aw {指使船舶吃水垂向（水平）改变1厘米应在船上施加的力（或重量） }{或指使船舶吃水垂向（水平）改变1厘米时，所引起的排水量的改变量 } （1）船型系数曲线 （2）浮性曲线 （3）稳性曲线 （4）邦金曲线 静水力曲线图:表示船舶正浮状态时的浮性要素、初稳性要素和船型系数等与吃水的关系曲线的总称，它是由船舶设计部门绘制，供驾驶员使用的一种重要的船舶资料。 第四章 稳性 稳性：是指船受外力作用离开平衡位置而倾斜，当外力消失后，船能回复到原平衡位置的能力。 稳心：船舶正浮时浮力作用线与微倾后浮力作用线的交点。 稳性的分类：（1）初稳性；（2）大倾角稳性；（3）横稳性；（4）纵稳性；（5）静稳性；（6）动稳性；（7）完整稳性；（8）非完整稳性（破舱稳性） 判断浮体的平衡状态：（1）根据倾斜力矩与稳性力矩的方向来判断；（2）根据重心与稳心的 相对位置来判断 浮态、稳性、初稳心高度、倾角 B L A C w w ?=d B A C m m ?=d V C ??=B L b L A V C m p ?=d A V C w vp ?=b b p vp m w C C C C C C ==， 002n n i i y y A l y =+??=-????∑[]012142...43n n l A y y y y y -=+++++[]0123213332...338n n n l A y y y y y y y --=++++++P D ?= P f P f x = x y = y = 0 ()P P d= cm TPC q ?= m g b g b g GM = z z = z BM z = z r z -+-+-
Forecasting Why forecast? Features Common to all Forecasts ?Conditions in the past will continue in the future ?Rarely perfect ?Forecasts for groups tend to be more accurate than forecasts for individuals ?Forecast accuracy declines as time horizon increases Elements of a Good Forecast ?Timely ?Accurate ?Reliable (should work consistently) ?Forecast expressed in meaningful units ?Communicated in writing ?Simple to understand and use Steps in Forecasting Process ?Determine purpose of the forecast ?Establish a time horizon ?Select forecasting technique ?Gather and analyze the appropriate data ?Prepare the forecast ?Monitor the forecast Types of Forecasts ?Qualitative o Judgment and opinion o Sales force o Consumer surveys o Delphi technique ?Quantitative o Regression and Correlation (associative) o Time series
船舶原理试题库 第一部分船舶基础知识 (一)单项选择题 1．采用中机型船的是 A. 普通货船 C．集装箱船D．油船 2．油船设置纵向水密舱壁的目的是 A．提高纵向强度B．分隔舱室 C．提高局部强度 3. 油船结构中，应在 A．货油舱、机舱B．货油舱、炉舱 C. 货油舱、居住舱室 4．集装箱船的机舱一般设在 A. 中部B．中尾部C．尾部 5．需设置上下边舱的船是 B．客船C．油船D．液化气体船 6 B．杂货船C．散货船D．矿砂船 7 A. 散货船B．集装箱船C．液化气体船 8．在下列船舶中,哪一种船舶的货舱的双层底要求高度大 A．杂货船B．集装箱船C．客货船 9．矿砂船设置大容量压载边舱，其主要作用是 A．提高总纵强度B．提高局部强度 C．减轻摇摆程度 10 B．淡水舱C．深舱D．杂物舱 11．新型油船设置双层底的主要目的有 A．提高抗沉性B．提高强度 C．作压载舱用 12．抗沉性最差的船是 A．客船B．杂货船C散货船 13．横舱壁最少和纵舱壁最多的船分别是 B．矿砂船，集装箱船 C．油船，散货船D．客船，液化气体船 14．单甲板船有 A. 集装箱船，客货船，滚装船B．干散货船，油船，客货船 C．油船，普通货船，滚装船 15．根据《SOLASl974 A．20 B．15 D．10 16 A. 普通货船C．集装箱船D．散货船17．关于球鼻首作用的正确论述是 A. 增加船首强度
C．便于靠离码头D．建造方便 18．集装箱船通常用______表示其载重能力 A．总载重量B．满载排水量 C．总吨位 19. 油船的______ A. 杂物舱C．压载舱D．淡水舱 20 A. 集装箱船B，油船C滚装船 21．常用的两种集装箱型号和标准箱分别是 B．40ft集装箱、30ft集装箱 C．40ft集装箱、10ft集装箱 D．30ft集装箱、20ft集装箱 22．集装箱船设置双层船壳的主要原因是 A．提高抗沉性 C．作为压载舱 D. 作为货舱 23．结构简单，成本低，装卸轻杂货物作业效率高，调运过程中货物摇晃小的起货设备是 B．双联回转式 C．单个回转式D．双吊杆式 24. 具有操作与维修保养方便、劳动强度小、作业的准备和收尾工作少，并且可以遥控操 作的起货设备是 B．双联回转式 D.双吊杆式 25．加强船舶首尾端的结构，是为了提高船舶的 A．总纵强B．扭转强度 C．横向强度 26. 肋板属于 A. 纵向骨材 C．连接件D．A十B 27. 在船体结构的构件中，属于主要构件的是：Ⅰ．强横梁；Ⅱ．肋骨；Ⅲ．主肋板；Ⅳ. 甲板纵桁；Ⅴ．纵骨；Ⅵ．舷侧纵桁 A．Ⅰ，Ⅱ，Ⅲ，ⅣB．I，Ⅱ，Ⅲ，Ⅴ D．I，Ⅲ，Ⅳ, Ⅴ 28．船体受到最大总纵弯矩的部位是 A．主甲板B．船底板 D．离首或尾为1／4的船长处 29. ______则其扭转强度越差 A．船越长B．船越宽 C．船越大 30 A．便于检修机器B．增加燃料舱 D．B+C 31
硕士生入学复试考试《船舶原理与结构》 考试大纲 1考试性质 《船舶原理》和《船舶结构设计》均是船舶与海洋工程专业学生重要的专业基础课。它的评价标准是优秀本科毕业生能达到的水平，以保证被录取者具有较好的船舶原理和结构设计理论基础。 2考试形式与试卷结构 (1)答卷方式：闭卷，笔试 (2)答题时间：180分钟 (3)题型：计算题50%；简答题35%；名词解释15% (4)参考数目： 《船舶原理》，盛振邦、刘应中，上海交通大学出版社，2003 《船舶结构设计》，谢永和、吴剑国、李俊来，上海交通大学出版社，2011年 3考试要点 3.1 《船舶原理》 （1）浮性 浮性的一般概念；浮态种类；浮性曲线的计算与应用；邦戎曲线的计算与应用；储备浮力与载重线标志。 （2）船舶初稳性 稳性的一般概念与分类；初稳性公式的建立与应用；重物移动、
增减对稳性的影响；自由液面对稳性的影响；浮态及初稳性的计算；倾斜试验方法。 （3）船舶大倾角稳性 大倾角稳性、静稳性与动稳性的概念；静、动稳性曲线的计算及其特性；稳性的衡准；极限重心高度曲线；IMO建议的稳性衡准原则；提高稳性的措施。 （4）抗沉性 抗沉性的概念；安全限界线、渗透率、可浸长度、分舱因数的概念；可浸长度计算方法；船舶分舱制；提高抗沉性的方法。 （5）船舶阻力的基本概念与特点 船舶阻力的分类；阻力相似定律；阻力（摩擦阻力、粘压阻力、兴波阻力）产生的机理和特性。 （6）船舶阻力的确定方法 船模阻力试验方法；阻力换算方法；阻力近似计算的概念及方法；艾尔法、海军系数法等。 （7）船型对阻力的影响 船型变化及船型参数，主尺度及船型系数的影响，横剖面面积曲线形状的影响，满载水线形状的影响，首尾端形状的影响。 （8）浅水阻力特性 浅水对阻力影响的特点；浅窄航道对船舶阻力的影响。 （9）船舶推进器一般概念 推进器的种类、传送效率及推进效率；螺旋桨的几何特性。
课程名称：计算机辅助船舶制造课程代码：01234（理论） 第一部分课程性质与目标 一、课程性质与特点 《计算机辅助船体建造》是船舶与海洋工程专业的一门专业必修课程，通过本课程各章节不同教学环节的学习，帮助学生建立良好的空间概念，培养其逻辑推理和判断能力、抽象思维能力、综合分析问题和解决问题的能力，以及计算机工程应用能力。 我国社会主义现代化建设所需要的高质量专门人才服务的。 在传授知识的同时，要通过各个教学环节逐步培养学生具有抽象思维能力、逻辑推理能力、空间想象能力和自学能力，还要特别注意培养学生具有比较熟练的计算机运用能力和综合运用所学知识去分析和解决问题的能力。 二、课程目标与基本要求 通过本课程学习，使学生对船舶计算机集成制造系统有较全面的了解，掌握计算机辅助船体建造的数学模型建模的思路和方法，培养计算机的应用能力，为今后进行相关领域的研究和开发工作打下良好的基础。 本课程基本要求： 1.正确理解下列基本概念： 计算机辅助制造，计算机辅助船体建造，造船计算机集成制造系统，船体型线光顺性准则，船体型线的三向光顺。 2.正确理解下列基本方法和公式： 三次样条函数，三次参数样条，三次B样条，回弹法光顺船体型线，船体构件展开计算的数学基础，测地线法展开船体外板的数值表示，数控切割的数值计算，型材数控冷弯的数值计算。 3.运用基本概念和方法解决下列问题： 分段装配胎架的型值计算，分段重量重心及起吊参数计算。 三、与本专业其他课程的关系 本课程是船舶与海洋工程专业的一门专业课，该课程应在修完本专业的基础课和专业基础课后进行学习。 先修课程：船舶原理、船体强度与结构设计、船舶建造工艺学 第二部分考核内容与考核目标 第1章计算机辅助船体建造概论 一、学习目的与要求 本章概述计算机辅助船体建造的主要体系及技术发展。通过对本章的学习，掌握计算机辅助制造的基本概念，了解计算机辅助船体建造的特点、造船计算机集成制造系统的基本含义和主要造船集成系统及其发展概况。 二、考核知识点与考核目标 （一）计算机辅助制造的基本概念（重点）（P1～P5） 识记：计算机在工业生产中的应用，计算机在产品设计中的应用，计算机在企业管理中的应用，计算机应用一体化。 理解：CIM和CIMS概念，计算机辅助制造的概念和组成 （二）造船CAM技术的特点（重点）P6～P8 识记：船舶产品和船舶生产过程的特点，造船CAM技术的特点 理解：船舶产品和船舶生产过程的特点与造船CAM技术之间的联系 应用：造船CAM技术应用范围 （三）计算机集成船舶制造系统概述（次重点）(P8～P11)
船舶维修技术实用手册》出版社：吉林科学技术出版社 出版日期：2005年 作者：张剑 开本：16开 册数：全四卷+1CD 定价：998.00元 详细介绍： 第一篇船舶原理与结构 第一章船舶概述 第二章船体结构与船舶管系 第三章锚设备 第四章系泊设备 第五章舵设备 第六章起重设备 第七章船舶系固设备 第八章船舶抗沉结构与堵漏 第九章船舶修理 第十章船舶人级与检验 第二篇现代船舶维修技术 第一章故障诊断与失效分析 第二章油液监控技术 第三章新材料、新工艺与新技术 第三篇船舶柴油机检修 第一章柴油机概述 第二章柴油机主要机件检修 第三章配气系统检修 第四章燃油系统检修 第五章润滑系统检修 第六章冷却系统检修 第七章柴油机操纵系统检修 第八章实际工作循环 第九章柴油机主要工作指标及其测定 第十章柴油机增压 第十一章柴油机常见故障及其应急处理
第四篇船舶电气设备检修 第一章船舶电气设备概述 第二章船舶常用电工材料 第三章船舶电工仪表及测量 第四章船舶常用低压电器及其检修 第五章船舶电机维护检修 第六章船舶电站维护检修 第七章船舶辅机电气控制装置维护检修第八章船舶内部通信及其信号装置检修第九章船舶照明系统维护检修 第五篇船舶轴舵系装置检修 第一章船舶轴系检修 第二章船舶舵系检修 第三章液压舵机检修 第四章轴舵系主要设备与要求 第五章轴舵系检测与试验 第六篇船舶辅机检修 第一章船用泵概述 第二章往复泵检修 第三章回转泵检修 第四章离心泵和旋涡泵 第五章喷射泵检修 第六章船用活塞空气压缩机检修 第七章通风机检修 第八章船舶制冷装置检修 第九章船舶空气调节装置检修 第十章船用燃油辅机锅炉和废气锅炉检修第十一章船舶油分离机检修 第十二章船舶防污染装置检修 第十三章海水淡化装置检修 第十四章操舵装置检修 第十五章锚机系缆机和起货机检修 第七篇船舶静电安全检修技术 第一章船舶静电起电机理 第二章舱内静电场计算 第三章船舶静电安全技术研究 第四章静电放电点燃估算 第五章船舶静电综合分析防治对策
经济决策定量第二章Forecasting部分翻译 Forecasting the future is a fundamental aspect of business decision making.Future sale is the most important variable in business forecasts.Knowledge about sales is a prerequisite to the budgetary and planning process. 预测未来是企业制定决策的一个基本方面。未来的销售是在业务预测中最重要的变量。有关销售知识是预算和规划过程的一个先决条件。 1.Forecasting using past data The historical patterns of a variable are identified and projected into the future.These patterns are abtained through extrapolation from time series data. 1.用过去的数据预测 一个变量的历史模式可以用来识别和预测未来。这些模式是通过外推法从时间序列数据。 2-Forecasting using causal models A relationship is found between the unknown variable and one or more other known variables. The values of the known variables are then used to predict the value of the variable of interest. 2.用因果模型预测 可以从未知的变量与一个或多个已知的变量发现数值关系。使用已知变量的值然后用于预测的所需的变量值。
1长宽比L/B 快速性、操纵性 宽吃水比B/d 稳性、摇荡性、快速性、操纵性 深吃水比D/d 稳性、抗沉性、船体强度 宽深比B/D 船体强度、稳性 长深比L/D 船体强度、稳性 2船长：船舶的垂线间长代表船长，即沿设计夏季载重水线，由首柱前缘至舵柱后缘或舵杆中心线的长度 3型宽：在船体最宽处，沿设计水线自一舷的肋骨外缘量至另一舷的肋骨外缘之间的水平距离 4型表面：不包括船壳板和甲板板厚度在内的船体表面 5型深：在船长中的处，由平板龙骨上缘量至上甲板边线下缘的垂直距离 6型吃水：在船长中点处由平板龙骨上缘量至夏季载重水线的垂直距离 7型线图是表示船体型表面形状的图谱,由纵剖线图、横剖线图、半宽水线图和型值表组成; 8浮性：船舶在给定载重条件下,能保持一定的浮态的性能； 9平衡条件：作用在浮体上的重力与浮力大小相等、方向相反并作用于同一铅垂线上； 10净载重量NDW：指船舶在具体航次中所能装载货物质量的最大值 11漂浮条件：满足平衡条件，且船体体积大于排水体积; 12浮心：浮心是船舶所受浮力的作用中心，也是排水体积的几何中心； 13漂心：船舶水线面积的几何中心； 14平行沉浮：船舶装卸货物前后水线面保持平行的现象； 15每厘米吃水吨数（TPC）：船舶吃水d每变化1cm，排水量变化的吨数，称为TPC。 16储备浮力：满载吃水以上的船体水密容积所具有的浮力 17干舷：在船长中点处由夏季载重水线量至上甲板边线上缘的垂直距离 18船舶稳性：船舶在外力（矩）作用下偏离其初始平衡位置，当外力（矩）消失后船舶能自行恢复到初始平衡状态的能力 19静稳性曲线：稳性力臂GZ或稳性力矩Ms随横倾角?变化曲线 20动稳性曲线：稳性力矩所做的功Ws或动稳性力臂I d随横倾角?变化的曲线 21吃水差比尺：是一种少量载荷变动时核算船舶纵向浮态变化的简易图表，它表示在船上任意位置加载100t后，船舶首、尾吃水该变量的图表 22最小倾覆力矩（力臂）：船舶所能承受动横倾力矩（力臂）的极限 23进水角：船舶横倾至最低非水密度开口开始进水时的横倾角 24可浸长度：船舶进水后的水线恰与限界线相切时的货仓最大许可舱长称为可浸长度 25稳性衡准数K是指船舶最小倾覆力矩(臂)与风压倾侧力矩（臂）之比 26稳性的调整方法：船内载荷的垂向移动及载荷横向对称增减 27静稳性力臂的表达式：1）基点法2）假定重心法3）初稳心点法 28船体强度：为保证船舶安全，船体结构必须具有抵抗各种内外作用力使之发生极度形变和破坏的能力 29局部强度表示方法：①均布载荷；②集中载荷；③车辆甲板载荷；④堆积载荷 30MTC为每形成1cm吃水差所需的纵倾力矩值，称为每厘米纵倾力矩 31载荷纵向移动包括配载计划编制时不同货舱货物的调整及压载水、淡水或燃油的调拨等情况 32重量增减包括中途港货物装卸、加排压载水、油水消耗和补给、破舱进水等情况 33抗沉性：是指船舶在一舱或数舱破损进水后，仍能保持一定浮性和稳性，使船舶不致沉没或延缓沉没时间，以确保人命和财产安全的性能
Volatility forecasting using high frequency data:Evidence from stock markets ☆ Sibel ?elik a ,?,Hüseyin Ergin b a Dumlupinar University,School of Applied Sciences,Turkey b Dumlupinar University,Business Administration,Turkey a b s t r a c t a r t i c l e i n f o Article history: Accepted 24September 2013JEL classi ?cation:C22G00 Keywords:Volatility Realized volatility High frequency data Price jumps The paper aims to suggest the best volatility forecasting model for stock markets in Turkey.The ?ndings of this paper support the superiority of high frequency based volatility forecasting models over traditional GARCH models.MIDAS and HAR-RV-CJ models are found to be the best among high frequency based volatility forecasting models.Moreover,MIDAS model performs better in crisis period.The ?ndings of paper are important for ?nancial institutions,investors and policy makers. ?2013Elsevier B.V.All rights reserved. 1.Introduction Volatility plays an important role in theoretical and practical applica-tions in ?nance.The availability of high frequency data brings a new dimension to volatility modeling and forecasting of returns on ?nancial assets.First and foremost,nonparametric estimation of volatility of asset returns becomes feasible and so modeling and forecasting volatility of asset returns has been a focus for researchers in the literature (Andersen and Bollerslev,1998;Andersen et al.,2001,2003b ,2007;Corsi,2004;Engle and Gallo,2006;Ghysels et al.,2004,2005,2006a,b;Hansen et al.,2010;Shephard and Sheppard,2010).The empirical ?nd-ings of existing studies support the superiority of high frequency based volatility models to popular GARCH models and stochastic volatility models in the literature (Andersen et al.,2003b ).Besides,earlier studies point to importance of allowing for discontinuities (jumps)in volatility models and pricing derivatives (Andersen et al.,2002;Chernov et al.,2003).Availability of high frequency data is also a turning point in order to distinguishing jump from continuous part of price process.Empirical ?ndings from recent studies show that incorporating the jumps to volatility models increase the forecasting performance of models supporting the earlier evidence (Andersen et al.,2003b,2007). This paper aims to suggest the best volatility forecasting model in stock markets in Turkey.For this purpose,?rst,we analyze the data generating process and calculate the high frequency based volatility and examine the return and volatility characteristics.Second,we propose the best volatility forecasting model by comparing different volatility forecasting models. In doing so,the paper will contribute to the literature in terms of ?lling ?ve main gaps.First,it suggests the best volatility forecasting model from the alternatives including high frequency-based models and traditional GARCH models.Second,it reveals the forecasting performance of volatility models during the periods of structural change.Because,recent studies in the literature indicate that ?nancial crisis affect the volatility dynamics deeply (Dungey et al.,2011).Third,it analyses forecasting performance of volatility in stock futures markets rather than spot markets.There are three reasons for usage of stock futures markets in this study.Firstly,there are ?ndings in the literature that futures markets respond to new information faster than spot markets (Stoll and Whaley,1990).Secondly,using futures contracts rather than spot indexes re-duces nonsynchronous trading problems (Wu et al.,2005).Thirdly,using futures contracts provides additional evidence to the existing literature on spot markets (Wu et al.,2005).Fourth,it compares the ?ndings at different frequencies to inference about optimal fre-quency since the sampling selection is important for high frequency data based studies.Because,while higher sampling frequency may cause bias in realized volatility,lower sampling frequency may cause information https://www.doczj.com/doc/9213812626.html,st,it contributes to literature in terms of presenting evidence from an Emerging Market. Economic Modelling 36(2014)176–190 ☆This paper is based on my doctoral dissertation “Volatility Forecasting in Stock Markets:Evidence From High Frequency Data of Istanbul Stock Exchange ”which was completed at Dumlupinar University,in 2012. ?Corresponding author at:Dumlupinar University,School of Applied Sciences,Insurance and Risk Management Department,Turkey.Tel.:+902742652031x4664. E-mail address:sibelcelik1@https://www.doczj.com/doc/9213812626.html, (S. ?elik).0264-9993/$–see front matter ?2013Elsevier B.V.All rights reserved. https://www.doczj.com/doc/9213812626.html,/10.1016/j.econmod.2013.09.038 Contents lists available at ScienceDirect Economic Modelling j ou r n a l h o m e p a ge :w ww.e l s e v i e r.c o m /l oc a t e /e c mo d