Evaluating Volatility and Correlation Forecasts

By Andrew J. Patton, Kevin Sheppard. 2009

230 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/301754ef22ec4084b78c3632f87f994336932baf

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Citations in the corpus

Multivariate High-Frequency-Based Volatility (HEAVY) Models (2012, methodology): “which provides a consistent ranking of volatility models in the sense of Patton (2011) and Patton and Sheppard (2009) as it is robust to noise in the proxy b t+s; see also Laurent et al. (2009). The loss function evaluates the s-step predicted density from model a using the proxy b t+s as data.”
Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “Following the arguments put forth in Patton and Sheppard (2009) and Patton (2011), we use the QLIKE to evaluate the OOS forecast performance of the GARCH-MIDAS model against the MS-GARCH and the nested GARCH.”
A comprehensive review of Value at Risk methodologies (2014, background): “The former issue has been investigated by Bollerslev et al. (2011), Chen and Ghysels (2010), Patton and Sheppard (2009), among others.”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “Simulation results in Patton and Sheppard (2009a), and empirical results in Hansen and Lunde (2005), Patton and Sheppard (2009b) and Patton (2011a) suggest that tests based on MSE have lower power than under QLIKE, and our results are consistent with this: under MSE we continue to find no cases…”
Realising the future: forecasting with high frequency based volatility (HEAVY) models (2010, background): “This loss function has been shown to be robust to certain types of noise in the proxy in Patton (2009) and Patton and Sheppard (2009a).”
On the Forecasting Accuracy of Multivariate GARCH Models (2010, background): “However, as pointed out by Patton and Sheppard (2009) the main drawback of an evaluation of volatility forecasts based on economic criteria is that it generally relies on additional and application-specific assumptions, the ordering may not depend exclusively on the accuracy of the conditional…”
MGARCH models: Trade-off between feasibility and flexibility (2018, background): “The LF has the advantage of belonging to the class of loss functions robust to noises in volatility proxy; see Patton and Sheppard (2009).”