Forecasting value-at-risk and expected shortfall for emerging markets using FIGARCH models

Alex Sandro Monteiro De Moraes (), Antonio Carlos Figueiredo Pinto () and Marcelo Klotzle
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Alex Sandro Monteiro De Moraes: Pontifícia Universidade Católica do Rio de Janeiro
Antonio Carlos Figueiredo Pinto: Pontifícia Universidade Católica do Rio de Janeiro

Brazilian Review of Finance, 2015, vol. 13, issue 3, 394-437

Abstract: 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 formultiple 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.

Keywords: Expected shortfall; long-memory; volatility forecast; multiple steps ahead forecast; value-at-risk (search for similar items in EconPapers)
JEL-codes: G1 G10 (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:brf:journl:v:13:y:2015:i:3:p:394-437

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