Library Header Image
LSE Research Online LSE Library Services

Modelling multivariate volatilities: an ad hoc method

Wang, Mingjin and Yao, Qiwei (2005) Modelling multivariate volatilities: an ad hoc method. In: Fan, Jianqing and Li, Gang, (eds.) Contemporary Multivariate Analysis and Experimental Designs: in Celebration of Professor Kai-Tai Fang's 65th Birthday. Series In Biostatistics. World Scientific, Singapore, pp. 87-97. ISBN 9789812561206

Download (362kB) | Preview


Volatility plays an important role in controlling and forecasting risks in various �nancial operations. For a univariate return series, volatility is often represented in terms of conditional variances or conditional standard deviations. Many statistical models have been developed for modelling univariate conditional variance processes. While univariate descriptions are useful and important, problems of risk assessment, asset allocation, hedging in futures markets and options pricing require a multivariate framework, since high volatilities are often observed in the same time periods across di�erent assets. Statistically this boils down to model time-varying conditional variance and covariance matrices of a vector-valued time series. Section 2 below lists some existing statistical models for multivariate volatility processes. We refer to Bauwens, Laurent and Rombouts (2005) for a more detailed survey on this topic. We propose a new and ad hoc method with numerical illustration in section 3. We concludes in section 4 with a brief summary.

Item Type: Book Section
Official URL:
Additional Information: © 2005 World Scientific
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 19 Feb 2009 15:58
Last Modified: 19 Aug 2021 23:27
Projects: 70201007
Funders: Engineering and Physical Sciences Research Council, Natural Science Foundation of China

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics