nep-ets 2014-08-28 papers

on Econometric Time Series

Issue of 2014‒08‒28
two papers chosen by
Yong Yin
SUNY at Buffalo

Discretization of Lévy semistationary processes with application to estimation By Mikkel Bennedsen; Asger Lunde; Mikko S. Pakkanen
Multivariate Stochastic Volatility with Dynamic Cross Leverage By Trojan, Sebastian

Discretization of Lévy semistationary processes with application to estimation

By:	Mikkel Bennedsen (Aarhus University and CREATES); Asger Lunde (Aarhus University and CREATES); Mikko S. Pakkanen (Aarhus University and CREATES)
Abstract:	Motivated by the construction of the Itô stochastic integral, we consider a step function method to discretize and simulate volatility modulated Lévy semistationary processes. Moreover, we assess the accuracy of the method with a particular focus on integrating kernels with a singularity at the origin. Using the simulation method, we study the finite sample properties of some recently developed estimators of realized volatility and associated parametric estimators for Brownian semistationary processes. Although the theoretical properties of these estimators have been established under high frequency asymptotics, it turns out that the estimators perform well also in a low frequency setting.
Keywords:	Stochastic simulation, discretization, Lévy semistationary processes, stochastic volatility, estimation, finite sample properties
JEL:	C13 C15 C63
Date:	2014–11–08
URL:	http://d.repec.org/n?u=RePEc:aah:create:2014-21&r=ets

Multivariate Stochastic Volatility with Dynamic Cross Leverage

By:	Trojan, Sebastian
Abstract:	WA multivariate stochastic volatility (MSV) model based on a Cholesky-type decomposition of the covariance matrix to model dynamic correlation in the observation and transition error as well as in cross leverage terms is proposed. The empirically relevant asymmetric concept of cross leverage is defined as a nonzero correlation between the ith asset return at time t and the jth log-volatility at time t+1. Volatilities and covariances are modeled separately, which makes an interpretation of leverage parameters straightforward. The model is applied on a three-dimensional portfolio consisting of the S&P 500 sector indices Financials, Industrials and Healthcare, spanning the recent financial crisis 2008/09. During and in the aftermath of market turmoil, increased cross leverage effects, higher unconditional kurtosis and stronger correlated information flow are observed. However, there is risk of overfitting and restricting time variation to elements governing dynamics of the observation error may be advisable.
Keywords:	Multivariate stochastic volatility, dynamic correlation, cross leverage, Cholesky decomposition, nonlinear state space model, Markov chain Monte Carlo, block sampler, particle filter
JEL:	C11 C15 C32 C58
Date:	2014–08
URL:	http://d.repec.org/n?u=RePEc:usg:econwp:2014:24&r=ets

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