Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258225 
Year of Publication: 
2021
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 8 [Article No.:] 141 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-23
Publisher: 
MDPI, Basel
Abstract: 
In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance. We find a quasi closed-form characteristic function and propose analytic approximations, which aid in the pricing of derivatives and calculation of risk measures. Parameters are estimated on three bivariate series, using a two-stage methodology involving method of moments and least squares. Moreover, a scaling factor is added for extra degrees of freedom to match data features. As an application, we consider investment strategies for a portfolio with two risky assets and a risk-free cash account. We calculate value-at-risk (VaR) values at a 95% risk level using both simulation-based and distribution-based methods. A comparison of these VaR values supports the effectiveness of our approximations and the potential for higher dimensions.
Subjects: 
4/2 stochastic volatility model
moment-generating function
principal component analysis
risk management calculations
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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