A stochastic variance factor model for large datasets and an application to S&P data

The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest use of the principal component methodology of Stock and Watson (2002) for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard (1994). The method is simple and computationally tractable for very large datasets. We provide theoretical results on this method and apply it to S&P data.

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Year of publication:	2004
Authors:	Cipollini, Andrea ; Kapetanios, George
Publisher:	London : Queen Mary University of London, Department of Economics
Subject:	Stochastischer Prozess \| Volatilität \| Multivariate Analyse \| Faktorenanalyse \| Schätzung \| Aktienindex \| USA \| Stochastic volatility, Factor models, Principal components

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Series:	Working Paper ; 506
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	379287447 [GVK] hdl:10419/62869 [Handle]
Classification:	C32 - Time-Series Models ; C33 - Models with Panel Data ; G12 - Asset Pricing
Source:	EconStor

Persistent link: https://www.econbiz.de/10010289033