Browsing by Author "Tauchen, G"

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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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    Diagnostic testing and evaluation of maximum likelihood models
    (Journal of Econometrics, 1985-01-01) Tauchen, G
    The paper develops a unified theory of likelihood specification testing based on M-estimators of auxiliary parameters. The theory is sufficiently general to encompass a wide class of specification tests including moment-based tests, Pearson-type goodness of fit tests, the information matrix test, and the Cox test. The paper also presents a framework based on Frechet differentiation for determining the effects of misspecification on the almost sure limits of parameter estimates and specification test statistics. © 1985.
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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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    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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    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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    Notes on financial econometrics
    (Journal of Econometrics, 2001-01-01) Tauchen, G
    The first part of the discussion reviews recent successes in modeling of discrete time financial data and argues that a direct approach is better suited than stochastic volatility. The second part reviews recent work on estimating continuous time models with emphasis on simulation-based techniques and joint estimation of the risk neutral and objective probability distributions. © 2001 Elsevier Science S.A. All rights reserved.
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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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    Risk, jumps, and diversification
    (2008) Bollerslev, T; Law, TH; Tauchen, G
    We test for price discontinuities, or jumps, in a panel of high-frequency intraday stock returns and an equiweighted index constructed from the same stocks. Using a new test for common jumps that explicitly utilizes the cross-covariance structure in the returns to identify non-diversifiable jumps, we find strong evidence for many modest-sized, yet highly significant, cojumps that simply pass through standard jump detection statistics when applied on a stock-by-stock basis. Our results are further corroborated by a striking within-day pattern in the significant cojumps, with a sharp peak at the time of regularly scheduled macroeconomic news announcements.
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    Solving the stochastic growth model by using quadrature methods and value-function iterations
    (Journal of Business and Economic Statistics, 1990-01-01) Tauchen, G
    This article presents a solution algorithm for the capital growth model. The algorithm uses value- function iterations on a discrete state space. The quadrature method is used to set the grid for the exogenous process, and a simple equispaced scheme in logarithms is used to set the grid for the endogenous capital process. The algorithm can produce a solution to within four-digit accuracy using a state space composed of 1,800 points in total. © 1990 American Statistical Association.
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    Stochastic Volatility in General Equilibrium
    (Quarterly Journal of Finance, Forthcoming, 2012) Tauchen, G
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    The objective function of simulation estimators near the boundary of the unstable region of the parameter space
    (Review of Economics and Statistics, 1998) Tauchen, G
    The paper examines the role of stability constraints in estimation by dynamic simulation. In particular, it analyzes the behavior of the objective function on either side of the boundary of the stability region of the parameter space. The main finding is that stability constraints may be ignored because the simulation-based objective function contains a built-in penalty to enforce stability. A key caveat, however, is that the dynamic stability of the auxiliary model that defines the moment conditions must be checked and enforced. An attempt to fit via simulation to moments defined by a dynamically unstable auxiliary model can be expected to lead to an ill-behaved objective function.
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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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    Volume, volatility, and leverage: A dynamic analysis
    (Journal of Econometrics, 1996-09-01) Tauchen, G; Zhang, H; Liu, M
    This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multi-step-ahead characteristics of a nonparametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.
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    Which moments to match?
    (Econometric Theory, 1996-12-01) Ronald Gallant, A; Tauchen, G
    We describe an intuitive, simple, and systematic approach to generating moment conditions for generalized method of moments (GMM) estimation of the parameters of a structural model. The idea is to use the score of a density that has an analytic expression to define the GMM criterion. The auxiliary model that generates the score should closely approximate the distribution of the observed data, but is not required to nest it. If the auxiliary model nests the structural model then the estimator is as efficient as maximum likelihood. The estimator is advantageous when expectations under a structural model can be computed by simulation, by quadrature, or by analytic expressions but the likelihood cannot be computed easily. © 1996 Cambridge University Press.