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Estimating value-at-risk for portfolios: skewed-EWMA forecasting via copula

Estimating value-at-risk for portfolios: skewed-EWMA forecasting via copula
Estimating value-at-risk for portfolios: skewed-EWMA forecasting via copula
With the increasing complexity of risks, how to estimate the risk of portfolios with complex dependencies is challenging. Recently, Lu and Huang (2007) proposed a skewed-b WMA procedure to calculate value-at-risk (VaR) for individual financial assets, which is derived from an asymmetric Laplace distribution and takes into account both skewness and heavy tails of the return distribution that are adaptive to the time-varying nature in practice by adjusting shape parameter in the distribution. In this paper, we extend the skewed-EWMA procedure to estimating the risk of complex portfolios with dependencies modelled via copula. Monte Carlo simulation procedure that combines copula techniques with skewed-EWMA forecasting is developed. The empirical backtesting evaluation of the VaR forecasting demonstrates that the proposed method can be a useful tool in estimating extreme risks of some complex portfolios.
1442-3065
87-115
Lu, Z.
4aa7d988-ac2b-4150-a586-ca92b8adda95
Li, S.
be6d14e4-0c49-47a8-a6e0-1c99a64e1024
Lu, Z.
4aa7d988-ac2b-4150-a586-ca92b8adda95
Li, S.
be6d14e4-0c49-47a8-a6e0-1c99a64e1024

Lu, Z. and Li, S. (2011) Estimating value-at-risk for portfolios: skewed-EWMA forecasting via copula. Australian Actuarial Journal, 17 (1), 87-115.

Record type: Article

Abstract

With the increasing complexity of risks, how to estimate the risk of portfolios with complex dependencies is challenging. Recently, Lu and Huang (2007) proposed a skewed-b WMA procedure to calculate value-at-risk (VaR) for individual financial assets, which is derived from an asymmetric Laplace distribution and takes into account both skewness and heavy tails of the return distribution that are adaptive to the time-varying nature in practice by adjusting shape parameter in the distribution. In this paper, we extend the skewed-EWMA procedure to estimating the risk of complex portfolios with dependencies modelled via copula. Monte Carlo simulation procedure that combines copula techniques with skewed-EWMA forecasting is developed. The empirical backtesting evaluation of the VaR forecasting demonstrates that the proposed method can be a useful tool in estimating extreme risks of some complex portfolios.

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Published date: 2011
Organisations: Mathematical Sciences

Identifiers

Local EPrints ID: 360474
URI: https://eprints.soton.ac.uk/id/eprint/360474
ISSN: 1442-3065
PURE UUID: 83129b22-c48e-4b47-80fd-bad01f1683c8

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Date deposited: 10 Dec 2013 14:41
Last modified: 18 Jul 2017 03:12

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