Modelling multivariate volatilities via latent common factors

Weiming Li, Jing Gao, Kunpeng Li and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Volatility, represented in the form of conditional heteroscedasticity, plays an impor- tant role in controlling and forecasting risks in various financial operations including asset pricing, portfolio allocation, and hedging futures. However, modeling and fore- casting multi-dimensional conditional heteroscedasticity are technically challenging. As the volatilities of many financial assets are often driven by a few common and latent factors, we propose in this paper a dimension reduction method to model a multivariate volatility process and to estimate a lower-dimensional space, to be called the volatility space, within which the dynamics of the multivariate volatility process is confined. The new method is simple to use, as technically it boils down to an eigenanalysis for a non- negative definite matrix. Hence it is applicable to the cases when the number of assets concerned is in the order of thousands (using an ordinary PC/laptop). On the other hand, the model has the capability to cater for complex conditional heteroscedastic- ity behavior for multi-dimensional processes. Some asymptotic properties for the new method are established. We further illustrate the new method using both simulated and real data examples.

Keywords: Eigenanalysis; latent factors; multi-dimensional volatility process; volatility space (search for similar items in EconPapers)
JEL-codes: C1 L81 (search for similar items in EconPapers)
Date: 2016-10-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
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Citations: View citations in EconPapers (2)

Published in Journal of Business and Economic Statistics, 1, October, 2016, 34(4), pp. 564-573. ISSN: 0735-0015

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