scholarly journals Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramona Serrano Bautista ◽  
José Antonio Nuñez Mora
Keyword(s):  
At Risk ◽  
Latin America ◽  
Stock Markets ◽  
Value At Risk ◽  
Predictive Performance ◽  
Market Risk ◽  
Global Market ◽  
Garch Models ◽  
Content Type ◽  
Emerging Stock Markets

PurposeThis paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.Design/methodology/approachMany VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).FindingsThe results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.Originality/valueAn important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.

scholarly journals Modelling and Forecasting the Volatility of Thin Emerging Stock Markets: the Case of Bulgaria

2009 ◽  
Vol 12 (4) ◽  
pp. 47-60 ◽  
Author(s):  
Plamen Patev ◽  
Nigokhos Kanaryan ◽  
Katerina Lyroudi
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Value At Risk ◽  
Market Risk ◽  
Return Volatility ◽  
Volatility Clustering ◽  
Emerging Stock Markets ◽  
The Subject ◽  
Basic Characteristics ◽  
Modelling And Forecasting

Modern Portfolio Theory associates the stock market risk with the volatility of return. Volatility is measured by the variance of the returns’ distribution. However, the investment community does not accept this measure, since it weights equally deviations of the average returns, whereas most investors determine the risk on the basis of small or negative returns. In the last few years the measure Value at Risk (VaR) has been established and adopted widely by practitioners. The issue of modelling and forecasting thin emerging stock markets’ risk is still open. The subject of this present paper is the risk of the Bulgarian stock market. The aim of this research is to give the investment community a model for assessing and forecasting the Bulgarian stock market risk. The result of this research shows that the SOFIX index has basic characteristics that are observed in most of the emerging stock markets, namely: high risk, significant autocorrelation, non-normality and volatility clustering. Three models have been applied to assess and estimate the Bulgarian stock market risk: RiskMetrics, EWMA with t-distributed innovations and EWMA with GED distributed innovations. The results revealed that the EWMA with t-distributed innovations and the EWMA with GED distributed innovations evaluate the risk of the Bulgarian stock market adequately.

Selection of Value-at-Risk models for MENA Islamic indices

2020 ◽  
Vol 11 (9) ◽  
pp. 1689-1708
Author(s):  
Wassim Ben Ayed ◽  
Ibrahim Fatnassi ◽  
Abderrazak Ben Maatoug
Keyword(s):  
At Risk ◽  
Risk Management ◽  
Value At Risk ◽  
Prediction Models ◽  
Market Risk ◽  
Parametric Models ◽  
Arch Model ◽  
Var Models ◽  
Content Type ◽  
Cent Level

Purpose The purpose of this study is to investigate the performance of Value-at-Risk (VaR) models for nine Middle East and North Africa Islamic indices using RiskMetrics and VaR parametric models. Design/methodology/approach The authors test the performance of several VaR models using Kupiec and Engle and Manganelli tests at 95 and 99 per cent levels for long and short trading positions, respectively, for the period from August 10, 2006 to December 14, 2014. Findings The authors’ findings show that the VaR under Student and skewed Student distribution are preferred at a 99 per cent level VaR. However, at 95 per cent level, the VaR forecasts obtained under normal distribution are more accurate than those generated using models with fat-tailed distributions. These results suggest that VaR is a good tool for measuring market risk. The authors support the use of RiskMetrics during calm periods and the asymmetric models (Generalized Autoregressive Conditional Heteroskedastic and the Asymmetric Power ARCH model) during stressed periods. Practical implications These results will be useful to investors and risk managers operating in Islamic markets, because their success depends on the ability to forecast stock price movements. Therefore, because a few Islamic financial institutions use internal models for their capital calculations, the regulatory committee should enhance market risk disclosure. Originality/value This study contributes to the knowledge in this area by improving our understanding of market risk management for Islamic assets during the stress periods. Then, it highlights important implications regarding financial risk management. Finally, this study fills a gap in the literature, as most empirical studies dealing with evaluating VaR prediction models have focused on quantifying the model risk in the conventional market.

Impact of non-normal return and market capitalization on estimation of VaR

2015 ◽  
Vol 7 (3) ◽  
pp. 222-242
Author(s):  
Pankaj Sinha ◽  
Shalini Agnihotri
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Empirical Distribution ◽  
Parametric Method ◽  
Market Risk ◽  
Content Type ◽  
Market Capitalization ◽  
Stock Indices ◽  
Relationship Of ◽  
The Relationship

Purpose – This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. Design/methodology/approach – In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method. Findings – It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms. Research limitations/implications – This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc. Practical implications – This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy. Originality/value – This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

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