Some Papers

(The following papers can be viewed or downloaded in Acrobat pdf format.)

Papers with Finance Applications

  • "The Shape of the Risk Premium: Evidence from a Semiparametric GARCH Model" "Figures" (with B. Perron) We examine the relationship between the risk premium on the S&P500 index total return and its conditional variance. We propose a new semiparametric model in which the conditional variance process is parametric, while the conditional mean is an arbitrary function of the conditional variance. For monthly S&P 500 excess returns, the relationship between the two moments that we uncover is nonlinear and nonmonotonic. Moreover, we find considerable persistence in the conditional variance as well as a leverage effect as documented by others.

  • "Semiparametric Estimation of a Characteristic-based Factor Model of Stock Returns" (with G. Connor) This paper develops a new estimation procedure for characteristic-based factor models of stock returns. It describes a factor model in which the factor betas are smooth nonlinear functions of observed security characteristics. It develops an estimation procedure that combines nonparametric kernel methods for constructing mimicking portfolios with parametric nonlinear regression to estimate factor returns and factor betas. Factor models are estimated for UK and US common stocks using book-to-price ratio, market capitalization, and dividend yield.

  • "Yield Curve Estimation by Kernel Smoothing" (with E. Mammen, J. Nielsen, and C. Tanggaard) We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is nonparametric and does not assume a particular functional form for the discount function although we do show how to impose various restrictions in the estimation. Our method is based on kernel smoothing and is defined as the minimum of some localized population moment condition. The solution to the sample problem is not explicit and our estimation procedure is iterative, rather like the backfitting method of estimating additive nonparametric models. We establish the asymptotic normality of our methods using the asymptotic representation of our estimator as an infinite series with declining coefficients. The rate of convergence is standard for one dimensional nonparametric regression. We investigate the finite sample performance of our method, in comparison with other well-established methods, in a small simulation experiment.
    Forthcoming in Journal of Econometrics

  • "Nonparametric Estimation of Single Factor Heath-Jarrow-Morton Term Structure Models and a Test for Path Independence" (with T. Nguyen and A. Jeffrey) This paper is under revision

  • "The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series" (with Y. Whang)
    The Journal of Econometrics, 1999

  • "Testing the Capital Asset Pricing Model Efficiently under elliptical symmetry: A semiparametric approach" (with D. Hodgson and K. Vorkink) We develop new tests of the capital asset pricing model which are valid under the assumption that the distribution generating returns is elliptically symmetric; this assumption is necessary and sufficent for the validity of the CAPM. Our test is based on semiparametric efficient estimation procedures for a seemingly unrelated regression model where the multivariate error density is elliptically symmetric. The elliptical symmetry assumption allows us to avoid the curse of dimensionality problem that typically arises in multivariate semiparametric estimation procedures, because the multivariate elliptically symmetric density function can be written as a function of a scalar transformation of the observed multivariate data. The elliptically symmetric family includes a number of thick-tailed distributions and so is potentially relevant in financial applications. Our estimated betas are lower than the GLS estimates; also our parameter estimates are much less consistent with the CAPM restrictions than the corresponding GLS estimates.
    Journal of Applied Econometrics

  • "A GARCH Model of the Implied Volatility of the Swiss Market Index From Option Prices" (with M. Sabbatini) This paper estimates the implied stochastic process of the volatility of the Swiss market index (SMI) from the prices of options written on it. A GARCH(1,1) model is shown to be a good parameterization of the process. Then, using the GARCH option pricing model of Duan (1991), the implied volatility process is estimated by a simulation minimization method from option price data. We find the persistence of volatility shocks implied by options on the SMI to be very close to that estimated from historical data on the index itself. Comparing the performances of the implied GARCH option pricing model to that of the Black and Scholes model it appears that the overall pricing performance of the former is superior. However the large sample standard deviations of the out-of-sample pricing errors suggest that this result should be taken with caution.
    International Journal of Forecasting, 1998