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

ID:
38266
Authors:
Alex Sandro Monteiro de Moraes
Antonio Carlos Figueiredo Pinto
Marcelo Cabus Klotzle
Journal:
Revista Brasileira de Finanças
Abstract:
This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating valueat-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 rollingsampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models.
Keywords:
Expected shortfall, long-memory, multiple steps ahead forecast and value-at-risk, volatility forecast
ABNT Citation:
MORAES, A. S. M.; PINTO, A. C. F.; KLOTZLE, M. C. Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH . Revista Brasileira de Finanças, v. 13, n. 3, p. 394-394, 2015.
APA Citation:
Moraes, A. S. M., Pinto, A. C. F., & Klotzle, M. C. (2015). Previsão de value-at-risk e expected shortfall para mercados emergentes usando modelos FIGARCH . Revista Brasileira de Finanças, 13(3), 394-394.
Permalink:
https://www.spell.org.br/documentos/ver/38266/forecasting-value-at-risk-and-expected-shortfall-for-emerging-markets-using-figarch-models/i/en
Document type:
Artigo
Language:
Português
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