Value‐at‐risk under market shifts through highly flexible models

• Published date: 01 December 2018
• Date: 01 December 2018
• Author: Sabri Boubaker,Duc Khuong Nguyen,Skander Slim,Ahmed BenSaïda
• DOI: http://doi.org/10.1002/for.2503

Received: 4 December 2016 Revised: 1 August 2017 Accepted: 16 November 2017

DOI: 10.1002/for.2503

RESEARCH ARTICLE

Value-at-risk under market shifts through highly flexible

models

Ahmed BenSaïda1Sabri Boubaker2,3 Duc Khuong Nguyen4Skander Slim1

1HEC Sousse—LaREMFiQ Laboratory,

University of Sousse, Tunisia

2South Champagne Business School,

Troyes, France

3International School, Vietnam National

University, Hanoi, Vietnam

4IPAGBusiness School, Paris, France

Correspondence

Ahmed BenSaïda, IHEC Sousse, BP 40,

Route de la ceinture, Sahloul 3, 4054,

Tunisia .

Email: ahmedbensaida@yahoo.com

Abstract

Managing market risk under unknown future shocks is a critical issue for pol-

icymakers, investors, and professional risk managers. Despite important devel-

opments in market risk modeling and forecasting over recent years, market

participants are still skeptical about the ability of existing econometric designs to

accurately predict potential losses, particularly in the presence of hidden struc-

tural changes. In this paper, we introduce Markov-switching APARCH models

under the skewed generalized tand the generalized hyperbolic distributions

to fully capture the fuzzy dynamics and stylized features of financial market

returns and to generate value-at-risk (VaR) forecasts. Our empirical analysis of

six major stock market indexes shows the superiority of the proposed models

in detecting and forecasting unobservable shocks on market volatility, and in

calculating daily capital charges based on VaR forecasts.

KEYWORDS

APARCH,flexible distribution, volatility, value-at-risk, regime-switching

1INTRODUCTION

Stock markets around the world have, over the last two

decades, experienced large fluctuations and swings, which

considerably increased the risk of investmentin stock port-

folios. The subprime crisis of 2007 in the USA and the sub-

sequent global financial crisis of 2008–2009 made investors

lose confidence in stock markets and fear cross-market

transmission of contagious shocks. Market fluctuations

in times of increasing economic and financial uncer-

tainty also contributed to amplify this tendency (Caldara,

Fuentes-Albero, Gilchrist, & Zakrajs˘ek, 2016; Popp &

Zhang, 2016). These facts naturally call for a better under-

standing of financial market dynamics in the context of

financial globalization and an improvement of financial

models to accurately estimate and forecast market risk.

Failure to do so could lead to huge portfolio losses and

possible financial disasters.

In this paper, we address the issue of market risk fore-

casting through the development of highly flexible models

based on a Markov-switching asymmetric power autore-

gressive conditional heteroskedasticity (APARCH) process

(msAPARCH,henceforth) under the skewed generalized t

(SGT) and generalized hyperbolic (GH) distributions. We

typically show how this type of model can be used to accu-

rately forecast the market risk of a diversified portfolio of

stocks, represented by its value-at-risk (VaR), in the pres-

ence of unobservable shocks that affect market volatility.

The main advantage of the proposed models is the pos-

sibility to capture not only the hidden structural changes

in the conditional volatility processes of portfolio returns,

but also the fuzzy dynamics and stylized facts of financial

market returns.

Our motivation is threefold. First, the VaR has been

recommended by the Basel Committee on Banking Super-

vision (2006), (2009) as a meaningful tool to measure and

monitor market risk, either on the downside or upside

of market movements. The VaR technique can be eas-

ily implemented. It provides a straight way for investors

to estimate the potential loss over a certain time period

790 Copyright © 2018 John Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/for Journalof Forecasting. 2018;37: 790–804.