VINE COPULA APPROACH FOR MULTIVARIATE AND MULTI-DAY AHEAD VALUE AT RISK AND EXPECTED SHORTFALL FORECASTING

Iqbal, Robina; Sorwar, Ghulam; Choudhry, Taufiq

Abstract

In this paper, we construct vine copula models for multivariate stock
portfolio returns to estimate one-day-ahead and multi-day ahead Value-at-Risk (VaR) and Expected Shortfall (ES) using Monte Carlo simulation. This is then compared with the VaR and ES using the dynamic conditional correlation (DCC) method. For the multi-day horizon, we use Monte Carlo simulation to simulate the share prices h-days ahead. The simulation-based method allows us to calculate VaR and ES for multivariate data at any horizons of interest and hence to calculate the entire term structure of risk. Using seven stocks from the DAX 30 as a case in point, we demonstrate the overall superiority of the copula-based method over the widely accepted DCC method. VaR and ES back-testing results indicate that vine copula significantly outperforms the DCC approach over a one-day horizon. This performance by the copula-based method is maintained across a multi-day horizon. Our findings suggest that institutions that use copula models to estimate their risk capital will need to set aside less capital to meet regulatory needs, than would otherwise be the case.

Citation

Iqbal, R., Sorwar, G., & Choudhry, T. (2022). VINE COPULA APPROACH FOR MULTIVARIATE AND MULTI-DAY AHEAD VALUE AT RISK AND EXPECTED SHORTFALL FORECASTING. In Advances in Financial Planning and Forecasting (163 - 196). Airiti. https://doi.org/10.6292/AFPF.202212_%2810%29.0007

• Publication Date: Dec 1, 2022
• Deposit Date: Aug 22, 2023
• Publisher: Airiti
• Pages: 163 - 196
• Book Title: Advances in Financial Planning and Forecasting
• DOI: https://doi.org/10.6292/AFPF.202212_%2810%29.0007
• Related Public URLs: Book information