Browsing by Author "Gallant, AR"

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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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    Estimation of continuous-time models for stock returns and interest rates
    (Macroeconomic Dynamics, 1997-12-01) Gallant, AR; Tauchen, G
    Efficient Method of Moments is used to estimate and test continuous-time diffusion models for stock returns and interest rates. For stock returns, a four-state, two-factor diffusion with one state observed can account for the dynamics of the daily return on the S&P Composite Index, 1927-1987. This contrasts with results indicating that discrete-time, stochastic volatility models cannot explain these dynamics. For interest rates, a trivariate Yield-Factor Model is estimated from weekly, 1962-1995, Treasury rates. The Yield-Factor Model is sharply rejected, although extensions permitting convexities in the local variance come closer to fitting the data.
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    Estimation of stochastic volatility models with diagnostics
    (Journal of Econometrics, 1997-11-01) Gallant, AR; Hsiehb, D; Tauchen, G
    Efficient method of moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are 'semiparametric ARCH' and 'nonlinear nonparametric'. With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient. © 1997 Elsevier Science S.A.
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    Nonparametric estimation of structural models for high-frequency currency market data
    (Journal of Econometrics, 1995-01-01) Bansal, R; Gallant, AR; Hussey, R; Tauchen, G
    Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975-90. © 1995.
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    Rational Pessimism, Rational Exuberance, and Asset Pricing Models
    (1999) Bansal, R; Gallant, AR; Tauchen, G
    estimates and examines the empirical plausibility of asset pricing models that attempt to explain features of financial markets such as the size of the equity premium and the volatility of the stock market. In one model, the long-run risks (LRR) model of Bansal and Yaron, low-frequency movements, and time-varying uncertainty in aggregate consumption growth are the key channels for understanding asset prices. In another, as typified by Campbell and Cochrane, habit formation, which generates time-varying risk aversion and consequently time variation in risk premia, is the key channel. These models are fitted to data using simulation estimators. Both models are found to fit the data equally well at conventional significance levels, and they can track quite closely a new measure of realized annual volatility. Further, scrutiny using a rich array of diagnostics suggests that the LRR model is preferred.
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    The relative efficiency of method of moments estimators
    (Journal of Econometrics, 1999-09-01) Gallant, AR; Tauchen, G
    The asymptotic relative efficiency of efficient method of moments when implemented with a seminonparametric auxiliary model is compared to that of conventional method of moments when implemented with polynomial moment functions. Because the expectations required by these estimators can be computed by simulation, these two methods are commonly used to estimate the parameters of nonlinear latent variables models. The comparison is for the models in the Marron-Wand test suite, a scale mixture of normals, and the second largest order statistic of the lognormal distribution. The latter models are representative of financial market data and auction data, respectively, which are the two most common applications of simulation estimators. Efficient method of moments dominates conventional method of moments over these models. © 1999 Elsevier Science S.A. All rights reserved.
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    Using daily range data to calibrate volatility diffusions and extract the forward integrated variance
    (Review of Economics and Statistics, 1999-11-01) Hsu, G Tauchen with Chiente; Gallant, AR
    A common model for security price dynamics is the continuous-time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the Black-Scholes price with the forward integrated variance replacing the Black-Scholes variance. Implementing the Hull and White characterization requires both estimates of the price dynamics and the conditional distribution of the forward integrated variance given observed variables. Using daily data on close-to-close price movement and the daily range, we find that standard models do not fit the data very well and that a more general three-factor model does better, as it mimics the long-memory feature of financial volatility. We develop techniques for estimating the conditional distribution of the forward integrated variance given observed variables.