A Nonparametric Test of Serial Independence for Time Series and Residuals

AbstractThis paper presents nonparametric tests of independence that can be used to test the independence of p random variables, serial independence for time series, or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression residuals are discussed

GARCH option pricing: A semiparametric approach

Option pricing based on GARCH models is typically obtained under the assumption that the random innovations are standard normal (normal GARCH models). However, these models fail to capture the skewness and the leptokurtosis in financial data. We propose a new method to compute option prices using a nonparametric density estimator for the distribution of the driving noise. We investigate the pricing performances of this approach using two different risk neutral measures: the Esscher transform pioneered by Gerber and Shiu [Gerber, H.U., Shiu, E.S.W., 1994a. Option pricing by Esscher transforms (with discussions). Trans. Soc. Actuar. 46, 99-91], and the extended Girsanov principle introduced by Elliot and Madan [Elliot, R.J., Madan, D.G., 1998. A discrete time equivalent martingale 9 measure. Math. Finance 8, 127-152]. Both measures are justified by economic arguments and are consistent with Duan's [Duan, J.-C., 1995. The GARCH option pricing model. Math. Finance 5, 13-32] local risk neutral valuation relationship (LRNVR) for normal GARCH models. The main advantage of the two measures is that one can price derivatives using skewed or heavier tailed innovations distributions to model the returns. An empirical study regarding the European Call option valuation on S&P500 Index shows: (i) under both risk neutral measures our semiparametric algorithm performs better than the existing normal GARCH models if we allow for a leverage effect and (ii) the pricing errors when using the Esscher transform are quite small even though our estimation procedure is based only on historical return data.

A Nonparametric Test of Serial Independence for Time Series and Residuals

This paper presents nonparametric tests of independence that can be used to test the independence of p random variables, serial independence for time series, or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression residuals are discussed.independence serial independence empirical processes pseudo-observations residuals weak convergence Cramer-von Mises statistics

A Nonparametric Test Of Serial Independence For Time Series And Residuals

: This paper presents nonparametric test of independence that can be use to test the independence of p random vectors, serial independence for time series or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression residuals are discussed. AMS 1990 subject classifications: Primary 62G10, 60F05, Secondary 62E20. Key words and phrases: independence, serial independence, empirical processes, pseudo-observations, residuals, weak convergence, Cram'er-von Mises statistics. 2 Proposed running head: Nonparametric test of independence Galley proofs should be sent to: Kilani Ghoudi D'epartement de math'ematiques et d'informatique Universit'e du Qu'ebec `a Trois-Rivi`eres Case postale 500 Trois-Rivi`eres (Qu'ebec) Canada G9A 5H7 tel: 1 (819) 376 5170 ex. 3814 fax: 1 (819) 376 5185 e-mail: [email protected] The final manuscript will be submitted electronically in LaTeX format. 3 1. Introduction Testing f..

A COMPARISON OF PRICING KERNELS FOR GARCH OPTION PRICING WITH GENERALIZED HYPERBOLIC DISTRIBUTIONS

Under discrete-time GARCH models markets are incomplete so there is more than one price kernel for valuing contingent claims. This motivates the quest for selecting an appropriate price kernel. Different methods have been proposed for the choice of a price kernel. Some of them can be justified by economic equilibrium arguments. This paper studies risk-neutral dynamics of various classes of Generalized Hyperbolic GARCH models arising from different price kernels. We discuss the properties of these dynamics and show that for some special cases, some pricing kernels considered here lead to similar risk neutral GARCH dynamics. Real data examples for pricing European options on the S&P 500 index emphasize the importance of the choice of a price kernel.Option pricing, risk neutral valuation, Generalized Hyperbolic GARCH, extended Girsanov principle, Esscher transform, mean correcting martingale measure, Radon-Nikodym derivative