A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio

Aida Karmous, Heni Boubaker and Lotfi Belkacem

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: In this paper, we have integrated numerous stylized facts, namely co-jumps, long memory, measurement error and leverage effects, in dynamic factor multivariate stochastic volatility (DfMSV) model for realized covariance measures using high frequency data. The aim is to examine the performance of these new models in reproducing characteristic features of foreign currency exchange series, which gather these stylized facts, and capturing the most of the information provided by financial time series. The dynamic conditional correlation model (DCC-DfMSV) combining Wishart autoregressive model (WAR) was applied to capture the selected stylized fact. The results proved that the DCC-DfMSV outperforms the asymmetric-DCC (ADCC), and fractionally integrated matrix-exponential-DCC (FIEDCC) models in forecasting future volatilities series. Along with the DfMSV model, the DCC-DfMSV combined with the overall stylized facts have proved to be better for forecasting portfolio volatility and providing better portfolio optimization, via efficient frontier, than the basic model.

Keywords: Dynamic factor model; Co-jumps; Leverage; Long memory; Dynamic conditional correlation model; Portfolio optimization (search for similar items in EconPapers)
JEL-codes: C14 C32 G11 G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312695

DOI: 10.1016/j.physa.2019.122191

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