scholarly journals Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH

2015 ◽  
Vol 13 (3) ◽  
pp. 394
Author(s):  
Alex Sandro Monteiro De Moraes ◽  
Antonio Carlos Figueiredo Pinto ◽  
Marcelo Cabus Klotzle
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Memory Models ◽  
Garch Models ◽  
Daily Data ◽  
Short Memory ◽  
Forecasting Volatility

This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock indices. We used daily data from 1999 to 2014 and an adaptation of the Monte Carlo simulation to estimate VaR and ES forecasts for multiple steps ahead (1, 10 and 20 days ), using FIGARCH and GARCH models for four errors distributions. The results suggest that, in general, the FIGARCH models improve the accuracy of forecasts for longer horizons; that the error distribution used may influence the decision about the best model; and that only for FIGARCH models the occurrence of underestimation of the true VaR is less frequent with increasing time horizon. However, the results suggest that rolling sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models.

scholarly journals Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Ravi Summinga-Sonagadu ◽  
Jason Narsoo
Keyword(s):  
Loss Function ◽  
Value At Risk ◽  
Garch Model ◽  
Innovation Process ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Garch Models ◽  
Intraday Data ◽  
Distributional Assumptions ◽  
T Distribution

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five distributional assumptions for the innovation process are used to analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in terms of in-sample fit based on various statistical and graphical tests. A more rigorous validation procedure involves testing the predictive power of the models. Therefore, three backtesting procedures were used for the VaR, namely, the Kupiec’s test, a duration-based backtest, and an asymmetric VaR loss function. Similarly, three backtests were employed for the ES: a regression-based backtesting procedure, the Exceedance Residual backtest and the V-Tests. The validation results show that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the Generalised Error Distribution (GED) innovation assumption gave the best fit to the intraday data and gave the best results for the ES forecasts. However, the asymmetric Skewed Student’s-t distribution for the innovation process provided the best results for the VaR forecasts. This paper presents the results of the first empirical study (to the best of the authors’ knowledge) in: (1) forecasting the intraday Expected Shortfall (ES) under different distributional assumptions for the MC-GARCH model; (2) assessing the MC-GARCH model under the Generalised Error Distribution (GED) innovation; (3) evaluating and ranking the VaR predictability of the MC-GARCH models using an asymmetric loss function.

Value-at-risk and expected shortfall: a dual long memory framework

2014 ◽  
Vol 16 (4) ◽  
pp. 416
Author(s):  
Zouheir Mighri ◽  
Faysal Mansouri ◽  
Geoffrey J.D. Hewings
Keyword(s):  
At Risk ◽  
Long Memory ◽  
Value At Risk ◽  
Expected Shortfall

scholarly journals Dynamic value at risk estimation for BELEX15

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Emilija Nikolić-Đorić ◽  
Dragan Đorić
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Long Memory ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Value Theory ◽  
Garch Models ◽  
Measurement Models ◽  
Standardized Residuals

This paper uses RiskMetrics, GARCH and IGARCH models to calculate daily VaR for Belgrade Stock Exchange index BELEX15 returns based on the normal and Student t innovation distribution. In the case of GARCH and IGARCH models VaR values are obtained applying Extreme Value Theory on the standardized residuals. The Kupiec's LR statistics was used to test the accuracy of risk measurement models. The main conclusions are: (1) when modelling value-at-risk it is very important to have a good model for volatility of stock returns; (2) both stationary and integrated GARCH models outperform RiskMetrics in estimating VaR; (3) although long memory volatility is present in the BELEX15 index, IGARCH models cannot outperform GARCH type models in VaR evaluations for this index.

Expected shortfall in the presence of asymmetry and long memory

2017 ◽  
Vol 29 (2) ◽  
pp. 132-151 ◽  
Author(s):  
Thomas Walther
Keyword(s):  
Risk Management ◽  
Stock Market ◽  
Long Memory ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Memory Effects ◽  
Garch Models ◽  
Stock Market Volatility ◽  
Conditional Volatility ◽  
Content Type

Purpose This study aims to analyse the conditional volatility of the Vietnam Index (Ho Chi Minh City) and the Hanoi Exchange Index (Hanoi) with a specific focus on their application to risk management tools such as Expected Shortfall (ES). Design/methodology/approach First, the author tests both indices for long memory in their returns and squared returns. Second, the author applies several generalised autoregressive conditional heteroskedasticity (GARCH) models to account for asymmetry and long memory effects in conditional volatility. Finally, the author back tests the GARCH models’ forecasts for Value-at-Risk (VaR) and ES. Findings The author does not find long memory in returns, but does find long memory in the squared returns. The results suggest differences in both indices for the asymmetric impact of negative and positive news on volatility and the persistence of shocks (long memory). Long memory models perform best when estimating risk measures for both series. Practical implications Short-time horizons to estimate the variance should be avoided. A combination of long memory GARCH models with skewed Student’s t-distribution is recommended to forecast VaR and ES. Originality/value Up to now, no analysis has examined asymmetry and long memory effects jointly. Moreover, studies on Vietnamese stock market volatility do not take ES into consideration. This study attempts to overcome this gap. The author contributes by offering more insight into the Vietnamese stock market properties and shows the necessity of considering ES in risk management. The findings of this study are important to domestic and foreign practitioners, particularly for risk management, as well as banks and researchers investigating international markets.

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