Value at risk models for volatile emerging markets equity portfolios (original) (raw)
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Quarterly Review of Economics and Finance 2010 Va R in Emerging Markets
This paper investigates the issue of market risk quantification for emerging and developed market equity portfolios. A very wide spectrum of popular and widely used in practice Value at Risk (VaR) models are evaluated and compared with Extreme Value Theory (EVT) and adaptive filtered models, during normal, crises, and post-crises periods. The results are interesting and indicate that despite the documented differences between emerging and developed markets, the most successful VaR models are common for both asset classes. Furthermore, in the case of the (fatter tailed) emerging market equity portfolios, most VaR models turn out to yield conservative risk forecasts, in contrast to developed market equity portfolios, where most models underestimate the realized VaR. VaR estimation during periods of financial turmoil seems to be a difficult task, particularly in the case of emerging markets and especially for the higher loss quantiles. VaR models seem to be affected less by crises periods in the case of developed markets. The performance of the parametric (non-parametric) VaR models improves (deteriorates) during post-crises periods due to the inclusion of extreme events in the estimation sample.
Economic Research-Ekonomska Istraživanja, 2015
The aim of this paper is to investigate the performance of Value at Risk (VaR) models in selected Central and Eastern European (CEE) emerging capital markets. Daily returns of Croatian (CROBEX), Czech (PX50), Hungarian (BUX) and Romanian (BET) stock exchange indices are analysed for the period January, 2000 -February, 2012, while daily returns of the Serbian (BELEX15) index is examined for the period September, 2005 -February, 2012. In recent years there has been much research conducted into VaR in developed markets, while papers dealing with VaR calculation in CEE are rare. Furthermore, VaR models created and suited for liquid and welldeveloped markets that assume normal distribution are less reliable for capital markets in emerging economies, such as Central and Eastern European Union member and candidate states. Since capital markets in European emerging economies are highly volatile, less liquid and strongly dependent on the unexpected external shocks, market risk estimation based on normality assumption in CEE countries is more problematic. This motivates us to implement GARCH-type methods that involve time varying volatility and heavy tails of the empirical distribution of returns. We test the hypothesis that using the assumption of heavy tailed distribution it is possible to forecast market risk more precisely, especially in times of crisis, than under the assumption of normal distribution or using historical simulations method. Our backtesting results for the last 500 observations are based on the Kupiec POF and Christoffersen independence test. They show that GARCH-type models with t error distribution in most analysed cases give better VaR estimation than GARCH type models with normal errors in the case of a 99% confidence level, while in the case of a 95% confidence level it is the opposite. The results of backtesting analysis for the crisis period (after the collapse of Lehman Brothers) show that GARCH-type models with t-distribution of residuals provide better VaR estimates compared with GARCH-type models with normal distribution, historical simulations and RiskMetrics methods. The RiskMetrics method in the most cases underestimates market risk.
Evaluation of Value at Risk in Emerging Markets
International Journal of Financial Management, 2017
Financial institutions have witnessed numerous episodes of financial crises all over the world during the last four decades. The researchers, academicians and policy makers in the field of finance studied these episodes extensively and to mitigate the risk involved in these crises have proposed several measures in the financial literature, but Value at Risk (VaR) has emerged as a more popular risk measurement technique. Although a number of studies have been undertaken in this area of research for developed markets but very few studies have been conducted in developing and emerging market economies. This study makes an attempt to evaluate the performance of VaR in emerging markets namely Brazil, Russia, India and China by considering Historical, Monte Carlo and GARCH Simulations to calculate VaR for the period 1998 to 2015. The study found that GJRGARCH Simulation is more suitable for Brazil and China while Historical Simulation for Russian and Indian Stock Markets based on the backtesting experiment..in market behaviour, it becomes vital to measure the level of risk for potential investors and agents even after knowing its presence, in order to survive in the global competitive market in a dynamic manner. Unlike the matured financial markets, the emerging financial markets are characterized with insufficient liquidity, the small scale of trading and asymmetrical and low number of trading days with certain securities (Andjelić, Djaković and Radišić, 2010). In the recent times, the emerging markets have been playing a crucial role due to greater potential in terms of economic growth and investment opportunities. However, the emerging stock markets are relatively young markets and have not developed sufficiently so as to identify all information that affects the stock prices and therefore, do not respond quickly to the publicly disclosed information (Benaković and Posedel, 2010). After the financial instabilities during 70's and advent of derivative markets, floating exchange rates led to development of several risk measurement methods. Among these Value-at-Risk (VaR) has emerged as a popular measure for assessing the market risk of the portfolio among the trading community. It can be defined as the maximum potential loss of a specific portfolio for a given time horizon. Increasing availability of the financial data and rapid advances in computer technology led to the development of various VaR models that can be applied for the risk management profession. The application of VaR models and comparing their relative performance gained a momentum in the field of financial economics. However, there is no common model that can give best forecasts of these models see for example,
Value at risk in emerging markets: Empirical evidence from twelve countries
2006
This study focuses on the relative performance of three Value-at-Risk (VaR) estimation methodologies. The daily stock market index returns of twelve different emerging markets are used for the empirical analysis. In addition to the well-known methodologies, such as the historical simulation and GARCH-based ones, the extreme value theory (EVT) is also used to estimate the daily VaR. In this paper, we focus on EVT because it studies the non-linear estimation of the tails and we expect to find many extreme events when analysing the return distributions in these twelve emerging markets. We focus on the negative extreme events rather than on the positive ones. The daily VaR is forecasted at three different quantile levels: 90%, 97.5%, 99.9%; and competing methodologies are back-tested accordingly. The results indicate that the historical simulation and GARCH-based methodologies work better at lower quantile levels than they do at higher quantile levels, while VaR estimated using EVT is m...
2007
Stock markets, particularly those from the developing countries, are characterized by high volatility which conventional models fail to capture fully, potentially leading to high losses. Value at Risk (VaR) models signified an important step to estimate losses of financial assets and portfolios. However, the stylized fact that financial returns exhibit fat tails, implies that conventional VaR models (parametrics and non-parametrics models) show important limitations because they fail to take into account the right statistical distributions to capture the frequency and severity of extreme values; the normal distribution is insufficient for this purpose. Extreme Value Theory (EVT) overcomes this limitation because it provides a framework to formally study the extreme behavior of stock markets returns and quantifies the possible losses experienced during financial instabilities and turbulences without making any assumptions about the underlying distribution of returns. This study uses ...
Frontiers in Finance and Economics, 2011
Conventional Value-at-risk (VaR) models tend to underestimate stock market losses, as they assume normality and fail to capture the frequency and severity of extreme fluctuations, Extreme value theory (EVT) overcomes this limitation by providing a framework in which to analyze the extreme behavior of stock-markets returns and by quantifying possible losses during financial turbulences. This study uses the c-quantile of a fat-tailed distribution for VaR analysis. An innovation in the present work is the application of EVT not only to the left tail of the returns distribution but also to its right tail, while assessing long and short positions. A generalized extreme value distribution (GEVD) is used to analyze the two largest stock markets from Latin America, Brazil and Mexico; a conditional VaR (CVaR) model is applied to determine risk exposure from investing in those markets, with daily index data for the period 1970-2004. The results confirm the presence of fat tails in both markets as a result of the excess of kurtosis; the empirical evidence shows that VaR and CVaR based on EVT yield more precise and robust information about financial risk than conventional parametric estimations.
Evaluating predictive performance of value‐at‐risk models in emerging markets: a reality check
Journal of Forecasting, 2006
We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions-unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the conditional distribution function. By using the reality check test of White , we compare the predictive power of alternative VaR models in terms of the empirical coverage probability and the predictive quantile loss for the stock markets of five Asian economies that suffered from the 1997-1998 financial crisis. The results based on these two criteria are largely compatible and indicate some empirical regularities of risk forecasts. The Riskmetrics model behaves reasonably well in tranquil periods, while some extreme value theory (EVT)-based models do better in the crisis period. Filtering often appears to be useful for some models, particularly for the EVT models, though it could be harmful for some other models. The CaViaR quantile regression models of have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the three periods before, during and after the crisis.
Value-at-risk: Applying the extreme value approach to Asian markets in the recent financial turmoil
Pacific-Basin Finance Journal, 2000
. Value-at-risk VaR measures are generated using extreme value theory by modelling the tails of the return distributions of six Asian financial markets during the recent volatile market conditions. The maxima and minima of these return series were found to be satisfactorily modelled within an extreme value framework and the value at risk measures generated within this structure were found to be different to those generated by variancecovariance and historical methods, particularly for markets characterised by high degrees of leptokurtosis such as Malaysia and Indonesia. q 2000 Published by Elsevier Science B.V. All rights reserved. JEL classification: G15; G18
A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro
Business Systems Research Journal, 2015
Background: The concept of value at risk gives estimation of the maximum loss of financial position at a given time for a given probability. The motivation for this analysis lies in the desire to devote necessary attention to risks in Montenegro, and to approach to quantifying and managing risk more thoroughly. Objectives: This paper considers adequacy of the most recent approaches for quantifying market risk, especially of methods that are in the basis of extreme value theory, in Montenegrin emerging market before and during the global financial crisis. In particular, the purpose of the paper is to investigate whether extreme value theory outperforms econometric and quantile evaluation of VaR in emerging stock markets such as Montenegrin market. Methods/Approach: Daily return of Montenegrin stock market index MONEX20 is analyzed for the period January, 2004 - February, 2014. Value at Risk results based on GARCH models, quantile estimation and extreme value theory are compared. Resu...
South East European Journal of Economics and Business, 2015
The study evaluated the sensitivity of the Value- at- Risk (VaR) and Expected Shortfalls (ES) with respect to portfolio allocation in emerging markets with an index portfolio of a developed market. This study utilised different models for VaR and ES techniques using various scenario-based models such as Covariance Methods, Historical Simulation and the GARCH (1, 1) for the predictive ability of these models in both relatively stable market conditions and extreme market conditions. The results showed that Expected Shortfall has less risk tolerance than VaR based on the same scenario-based market risk measures