QUANTILE FORECASTS OF FINANCIAL RETURNS USING REALIZED GARCH MODELS*

This article applies the realized generalized autoregressive conditional heteroskedasticity (GARCH) model, which incorporates the GARCH model with realized volatility, to quantile forecasts of financial returns, such as Value‐at‐Risk and expected shortfall. Student's t‐ and skewed Student's t‐distributions as well as normal distribution are used for the return distribution. The main results for the S&P 500 stock index are: (i) the realized GARCH model with the skewed Student's t‐distribution performs better than that with the normal and Student's t‐distributions and the exponential GARCH model using the daily returns only; and (ii) using the realized kernel to take account of microstructure noise does not improve the performance.     

ANALYTICAL EVALUATION OF VOLATILITY FORECASTS*

Estimation and forecasting for realistic continuous‐time stochastic volatility models is hampered by the lack of closed‐form expressions for the likelihood. In response, Andersen, Bollerslev, Diebold, and Labys (Econometrica, 71 (2003), 579–625) advocate forecasting integrated volatility via reduced‐form models for the realized volatility, constructed by summing high‐frequency squared returns. Building on the eigenfunction stochastic volatility models, we present analytical expressions for the forecast efficiency associated with this reduced‐form approach as a function of sampling frequency. For popular models like GARCH, multifactor affine, and lognormal diffusions, the reduced form procedures perform remarkably well relative to the optimal (infeasible) forecasts.     

VOLUNTARY IMPORT EXPANSION AND DIRECT INVESTMENT*

This paper analyses the welfare effects of a market‐share Voluntary Import Expansion (VIE) in the presence of foreign direct investment utilizing a duality approach. Introducing the cost burden of VIE explicitly, this paper considers the conditions under which a market‐share VIE is voluntary to the importing country. It is shown that the voluntary nature of VIE depends upon the capital import, cost burden and price difference effects and that a VIE is truly voluntary if it is accompanied by direct investment. We also show the existence of a complementary relationship between VIE and direct investment in attaining a particular level of welfare.     

VOLATILITY MODELS*

Models for estimating the volatility of financial assets are reviewed in this paper. The volatility can be estimated by the univariate GARCH family of models, or stochastic volatility models. These univariate models are developed intomultivariate models. Finally, the search for an adequate framework for the estimation has led to the analysis of high frequency intraday data. The variance over a fixed interval can be estimated accurately as the sum of squared realizations, provided the data are available at sufficiently high sampling frequencies. The future of this new area is wide open for theoretical developments and for applied studies.     

IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS*

This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias–variance trade‐off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.     

ESTIMATING MULTICOUNTRY VAR MODELS*

This article presents a method to estimate the coefficients, to test specification hypotheses, and to conduct policy exercises in multicountry Vector Autoregressive (VAR) models with cross‐unit interdependencies, unit‐specific dynamics, and time variations in the coefficients. The framework of analysis is Bayesian: A prior flexibly reduces the dimensionality of the model and puts structure on the time variations, Markov chain Monte Carlo (MCMC) methods are used to obtain posterior distributions, and marginal likelihoods to check the fit of various specifications. Impulse responses and conditional forecasts are obtained with the output of an MCMC routine. The transmission of certain shocks across countries is analyzed.     

A THREE‐STEP PROCEDURE FOR ESTIMATING AND TESTING COINTEGRATED ARMAX MODELS*

To deal with a variety of inferential problems on non‐stationary cointegrated time series, this paper proposes a computationally feasible method based on the Whittle likelihood and examines its performance. For the empirical application of our method, the paper investigates three sets of Japanese and US monetary and financial time‐series data. To evaluate the p‐value of the likelihood ratio statistic, we propose an approximation procedure based on the gamma distribution and the accompanying Laguerre expansion for reducing the computational burden. We also provide a numerical procedure for the asymptotic covariance matrix of the Whittle estimator.     

USING ELICITED CHOICE PROBABILITIES TO ESTIMATE RANDOM UTILITY MODELS: PREFERENCES FOR ELECTRICITY RELIABILITY*

When choice data are not available, researchers studying preferences sometimes ask respondents to state the actions they would choose in choice scenarios. Data on stated choices are then used to estimate random utility models, as if they are data on actual choices. Stated and actual choices may differ because researchers typically provide respondents less information than they would have in actuality. Elicitation of choice probabilities overcomes this problem by permitting respondents to express uncertainty about behavior. This article shows how to use elicited choice probabilities to estimate random utility models and reports estimates of preferences for electricity reliability.     

NONPARAMETRIC CONTINUOUS/DISCRETE CHOICE MODELS*

Modeling choices that are both discrete and continuous is important in several settings. The purpose of this article is to explore formulation and identification of such models when indirect utility functions are specified nonparametrically. Here we consider general nonseparable disturbances. We give identification results for nonseparable sample selection models and use these to analyze identification of discrete/continuous choice models.     

基于投入产出技术分析我国能耗阶段性变动的原因

当前能耗的上升既存在短期投资和生产能力扩张冲动的盲目性,也有其长期工业化转变过程的内在必然性。但能耗的上升在一定程度上与我国当前工业化发展阶段相联系,从而很难在短期内解决。因此,更多从长期角度来研究和制定节能政策显得尤为必要。