Dynamic portfolio strategies under a fully correlated jump-diffusion process

Escobar‐Anel, Marcos; Moreno-Franco, Harold A.

doi:10.1007/s10436-019-00350-3

Cited by 1 publication

(1 citation statement)

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“…Inspired by this, principal component stochastic volatility (PCSV) models are built from a linear combination of tractable one-dimensional counterparts. Their applications have been studied in a series of papers since 2010; see, for example, De Col et al (2013); Escobar (2018); Escobar et al (2010); Escobar-Anel and Moreno-Franco (2019).…”

Section: Introductionmentioning

confidence: 99%

Mean-Reverting 4/2 Principal Components Model. Financial Applications

Escobar‐Anel

Gong

2021

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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.

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Section: Introductionmentioning

confidence: 99%