Evaluating the Predictive Accuracy of Volatility Models

By Jose A. Lopez. 2001

242 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1616bfbdfa52c764bb7fb4aadeee6005d4fa6a58

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Citations in the corpus

Evaluating Volatility and Correlation Forecasts (2009, unknown): “VaR-based evaluation and comparison of volatility forecast has been examined in [44, 32, 26, 43], amongst others.”
A Practical Guide to Forecasting Financial Market Volatility (2005, methodology): “2 HESTON PRICE AND BLACK–SCHOLES IMPLIED In this section, we analyse possible BS implied bias by simulating a series of Heston option prices with parameter values similar to those in Bakshi, Cao and Chen (1997), Nandi (1998), Das and Sundaram (1999), Bates (2000), Lin, Strong and Xu (2001), Fiorenti”
Forecasting Stock Market Volatility with Regime-Switching GARCH Models (2005, background): “However, the evaluation of the quality of competing volatility models can be very difficult because, as remarked by both Bollerslev et al. (1994) and Lopez (2001), there does not exist a unique criterion capable of selecting the best model.”
Volatility forecasting of crude oil market: A new hybrid method (2018, methodology): “As discussed by Lopez (2001), it is not obvious which loss function is more appropriate for the evaluation of volatility models.”
A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? (2005, unknown): “(1994), Diebold and Lopez (1996) and Lopez (2001).”
Garch forecasting performance under different distribution assumptions (2006, methodology): “Articles that do address this question are Hamilton and Susmel (1994), Lopez (2001) and Franses and Ghijsels (1999).”
Comparing Density Forecasts via Weighted Likelihood Ratio Tests (2007, unknown): “Bayesian estimation of the LIN model In the LIN model we give and h = 2 conditionally conjugate priors: v N( ;H 1 ) (19) s h v 2 (20) i.”
Forecasting Volatility in Financial Markets: A Review (2003, unknown): “As shown in Lopez (2001), while 2 t is an unbiased estimator of σ 2 t , it is very imprecise due to its asymmetric distribution .”
Forecasting crude oil price volatility (2018, unknown): “1994 and Lopez 2001 ) , we follow Hansen and Lunde ( 2005 ) in computing six di¤erent loss functions for forecast evaluation .”
Evaluating Covariance Matrix Forecasts in a Value-at-Risk Framework (2000, unknown): “For our analysis, we chose =0.94, which is a commonly used value; see J.P. Morgan (1996) as well as Walter and Lopez (2000).9 The second category of covariance matrix forecasts consists of multivariate GARCH models.”