An??lise de previs??es de volatilidade para modelos de Valor em Risco (VaR)

• Autor(a) principal: Vargas, Rafael de Morais
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UCB
• Texto Completo: https://bdtd.ucb.br:8443/jspui/handle/tede/2412
• topic: Vari??ncia realizada; Heterogeneous Autoregressive Model of Realized Volatility - HAR-RV; Previs??o de volatilidade; Generalized Autoregressive Score Model - GAS; Previs??o de VaR; Realized variance; Volatility forecast; VaR forecast; Valor em Risco - VaR; CNPQ::CIENCIAS SOCIAIS APLICADAS::ECONOMIA

Given the importance of market risk measures, such as value at risk (VaR), in this paper, we compare traditionally accepted volatility forecast models, in particular, the GARCH family models, with more recent models such as HAR-RV and GAS in terms of the accuracy of their VaR forecasts. For this purpose, we use intraday prices, at the 5-minute frequency, of the S&P 500 index and the General Electric stocks, for the period from January 4, 2010 to December 30, 2013. Based on the tick loss function and the Diebold-Mariano test, we did not find difference in the predictive performance of the HAR-RV and GAS models in comparison with the Exponential GARCH (EGARCH) model, considering daily VaR forecasts at the 1% and 5% significance levels for the return series of the S&P 500 index. Regarding the return series of General Electric, the 1% VaR forecasts obtained from the HAR-RV models, assuming a t-Student distribution for the daily returns, are more accurate than the forecasts of the EGARCH model. In the case of the 5% VaR forecasts, all variations of the HAR-RV model perform better than the EGARCH. Our empirical study provides evidence of the good performance of HAR-RV models in forecasting value at risk.