Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood

2004 ◽  
Vol 20 (4) ◽  
pp. 629-645 ◽  
Author(s):  
Gloria González-Rivera ◽  
Tae-Hwy Lee ◽  
Santosh Mishra
Keyword(s):  
At Risk ◽  
Option Pricing ◽  
Utility Function ◽  
Value At Risk ◽  
Reality Check ◽  
Predictive Likelihood ◽  
Forecasting Volatility

scholarly journals Censored Posterior and Predictive Likelihood in Bayesian Left-Tail Prediction for Accurate Value at Risk Estimation

2013 ◽  
Author(s):  
Lukasz T. Gatarek ◽  
Lennart F. Hoogerheide ◽  
Koen Hooning ◽  
H. K. van Dijk
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Left Tail ◽  
Predictive Likelihood

Evaluating predictive performance of value-at-risk models in emerging markets: a reality check

2006 ◽  
Vol 25 (2) ◽  
pp. 101-128 ◽  
Author(s):  
Yong Bao ◽  
Tae-Hwy Lee ◽  
Burak Saltoglu
Keyword(s):  
At Risk ◽  
Emerging Markets ◽  
Value At Risk ◽  
Predictive Performance ◽  
Risk Models ◽  
Reality Check

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.

Extremwerttheorie und Value-at-Risk

2015 ◽  
Vol 44 (5) ◽  
pp. 259-267
Author(s):  
Frank Schuhmacher ◽  
Benjamin R. Auer
Keyword(s):  
At Risk ◽  
Value At Risk
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