Forecasting multivariate VaR and ES using MC-GARCH-Copula model

Badaye, Hemant Kumar; Narsoo, Jason

doi:10.1108/jrf-06-2019-0114

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Most recently, Badaye and Narsoo (2020) present a novel methodology to explore the performance of several multivariate VaR and ES models in order to estimate the risk of an equally weighted portfolio of one minute intraday frequency observations for five foreign currencies; they employ the multiplicative component MC-GARCH model on each return series and by modelling the dependence structure using copulas. VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation.…”

Section: Desempeño De Ocho De Mayor Capitalizaciómentioning

confidence: 99%

Chicago and Mexico Futures Markets Asymmetries and Hedging

Bautista¹,

Ortíz

2020

EFR

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This work investigates the hedging performance of futures contracts in two asymmetric markets, peso/dollar traded at the Mexican derivatives market (MexDer); and dollar/ peso traded in the Chicago Mercantile Exchange (CME). Value at Risk and Expected Shortfall enhanced by GARCH (1,1) modeling was applied. The left and right tails of the futures return series are examined, for both short and long positions. The period analyzed comprises from October 2016 to June 2017, partitioned in three subperiods; the results obtained for each market are compared, and finally their statistical validity is tested applying Kupiec backtesting. Overall, hedging in the CME is more effective, albeit the MexDer outperforms that market several times. However, all metrics (with and without GARCH modeling added) show important weakness below the 99 percent confidence level.

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Section: Desempeño De Ocho De Mayor Capitalizaciómentioning

confidence: 99%

Chicago and Mexico Futures Markets Asymmetries and Hedging

Bautista¹,

Ortíz

2020

EFR

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“…However, the risk forecasting performance of this model is not explored in their study. In a recent study, Badaye and Narsoo (2020) apply the MCS-GARCH model to determine the risk of a portfolio of five currency pairs. With 1-minute frequency data, this study determines the most suitable copula to be used with MCS-GARCH while estimating multivariate VaR and ES.…”

Section: Literature Reviewmentioning

confidence: 99%

Idiosyncrasies of Intraday Risk in Emerging and Developed Markets: Efficacy of the MCS-GARCH Model and Extreme Value Theory

Banerjee

Paul

2020

Global Business Review

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This article aims to study the effectiveness of the multi-step multiplicative component generalized autoregressive conditional heteroscedasticity (MCS-GARCH) model in forecasting intraday value at risk (VaR) and expected shortfall (ES) for both emerging and developed markets. The intraday VaR- and ES-forecasting performance of the MCS-GARCH model is compared with that of three other models. Five-minute returns data of stock market indices from four different countries are used in this study: India, China, the USA and the UK. The VaR and ES quantiles are estimated with the help of conditional extreme value theory (EVT) framework as well as with simple normal distribution assumption. The backtesting of VaR and ES suggest that the conditional EVT models provide superior forecasts compared to their GARCH counterparts. However, the accuracy of these models differs between emerging and developed markets. The multi-step MCS-GARCH or its EVT counterpart fails to outperform other competing models consistently, especially in emerging markets. The inability of the MCS-GARCH model in forecasting intraday risk accurately suggests that the multiple steps in the estimation of this model may lead to the errors-in-variables problem. The results of this study are unique in their suggestion that multi-step models may not be a better option in the estimation of VaR and ES.

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“…With this approach, the problem of risk measures modeling is reduced to the estimation of a model for variance and finding static risk measures for its residuals using parametric or nonparametric methods. Examples of this approach are described in [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Quintile regression based approach for dynamical VaR and CVaR forecasting using metalog distribution

Zrazhevsky

Zrazhevska

2021

SRIT

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The paper proposes a new method of dynamic VaR and CVaR (ES) risk measures forecasting. Quantile linear GARCH model is chosen as the main forecasting model for time series quantiles. To build a forecast, the values of quantiles are approximated by the metalog distribution, which makes it possible to use analytical formulas to evaluate risk measures. The method of VaR and CVaR forecasting is formulated as a step-by-step algorithm. At the first stage, an initial model is built to obtain variance estimates. The predicted variance values obtained from the constructed model are used at the second stage to find the QLGARCH model coefficients by solving the minimization problem. At the third stage, the QLGARCH models are estimated on a non uniform quantile grid. The obtained predicted values of quantiles are used to estimate the approximating metalog distribution. The investigated theory is applied to VaR and CVaR forecasting for time series of daily log return of the DJI index.

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Forecasting multivariate VaR and ES using MC-GARCH-Copula model

Cited by 7 publications

References 20 publications

Chicago and Mexico Futures Markets Asymmetries and Hedging

Chicago and Mexico Futures Markets Asymmetries and Hedging

Idiosyncrasies of Intraday Risk in Emerging and Developed Markets: Efficacy of the MCS-GARCH Model and Extreme Value Theory

Quintile regression based approach for dynamical VaR and CVaR forecasting using metalog distribution

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