Browsing by Author "Ghysels, E"

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Alternative models for stock price dynamics
(Journal of Econometrics, 2003-09-01) Tauchen, G; Chernov, M; Gallant, AR; Ghysels, E
This paper evaluates the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions. We use estimation technology that facilitates nonnested model comparisons and use a long data set which provides rich information about the conditional and unconditional distribution of returns. We consider two broad families of models: (1) the multifactor loglinear family, and (2) the affine-jump family. Both classes of models have attracted much attention in the derivatives and econometrics literatures. There are various tradeoffs in considering such diverse specifications. If pure diffusion SV models are chosen over jump diffusions, it has important implications for hedging strategies. If logarithmic models are chosen over affine ones, it may seriously complicate option pricing. Comparing many different specifications of pure diffusion multifactor models and jump diffusion models, we find that (1) log linear models have to be extended to two factors with feedback in the mean reverting factor, (2) affine models have to have a jump in returns, stochastic volatility or probably both. Models (1) and (2) are observationally equivalent on the data set in hand. In either (1) or (2) the key is that the volatility can move violently. As we obtain models with comparable empirical fit, one must make a choice based on arguments other than statistical goodness-of-fit criteria. The considerations include facility to price options, to hedge and parsimony. The affine specification with jumps in volatility might therefore be preferred because of the closed-form derivatives prices. © 2003 Elsevier B.V. All rights reserved.
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Frontiers of financial econometrics and financial engineering
(Journal of Econometrics, 2003-09-01) Ghysels, E; Tauchen, G
The papers in this volume represent the most recent advances in the intersection of the fields of financial econometrics and financial engineering. A collection of papers presented at a conference organized by the Guest Editors in collaboration with Robert E. Whaley at the Fuqua School of Business of Duke University was supplemented with several additional articles to make up this volume. The articles cover four topics: (1) option pricing, (2) fixed income securities, (3) stochastic volatility and jumps, (4) general asset pricing and portfolio allocation. It concludes with a review essay by David Bates that provides a general perspective on the interface between financial econometrics and financial economics, including current issues and the research agenda for the future. © 2003 Elsevier B.V. All rights reserved.
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Periodic Autoregressive Conditional Heteroskedasticity
(1996) Bollerslev, T; Ghysels, E
Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) models for the mean. The implicit relation between periodic generalized ARCH (P-GARCH) structures and time-invariant seasonal weak GARCH processes documents how neglected autoregressive conditional heteroscedastic periodicity may give rise to a loss in forecast efficiency. The importance and magnitude of this informational loss are quantified for a variety of loss functions through the use of Monte Carlo simulation methods. Two empirical examples with daily bilateral Deutschemark/British pound and intraday Deutschemark/U.S. dollar spot exchange rates highlight the practical relevance of the new P-GARCH class of models. Extensions to discrete-time periodic representations of stochastic volatility models subject to time deformation are briefly discussed.