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:	2004-02
Authors:	Cipollini, Andrea ; Kapetanios, George
Institutions:	School of Economics and Finance, Queen Mary
Subject:	Stochastic volatility \| Factor models \| Principal components

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Extent:	application/pdf
Series:	Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	A revised version is available at the personal homepage of <a href="http://www.econ.qmul.ac.uk/staff/kapetanios/" target="_parent">George Kapetanios</a>. Number 506
Classification:	C32 - Time-Series Models ; C33 - Models with Panel Data ; G12 - Asset Pricing
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005106432