A Practical Guide to Volatility Forecasting through Calm and Storm

2011

155 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/cb1e6e3d7179964b737e6a36f91b8de63e284b0e

Citations in the corpus

• Copula Processes (2010, unknown): “We compare predictions to GARCH(1,1), which has been shown in extensive and recent reviews to be competitive with other GARCH variants, and more sophisticated models [5, 6, 7].”
• Variational Heteroscedastic Gaussian Process Regression (2011, unknown): “For instance, modeling time series with time-varying volatility and stochastic volatility models is an important research field in economics (Brooks et al., 2001; Brownlees et al., 2009; Liu, 2001).”
• Gaussian Process Regression Networks (2011, unknown): “These datasets are especially suited to MGARCH, the most popular multivariate volatility model, and have become a benchmark for assessing GARCH models (Poon & Granger, 2005; Hansen & Lunde, 2005; Brownlees et al., 2009; McCullough & Renfro, 1998; Brooks et al., 2001).”
• Tail Risk and Asset Prices (2014, unknown): “The absence of an increase in measured tail risk during the recent financial crisis may be surprising prima facie, but is potentially consistent with the account of the recent financial crisis by Brownlees, Engle, and Kelly (2011).”
• Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics (2016, unknown): “This result supports previous findings in the GARCH setting described, among others, by Brownlees et al. (2012).”
• The Price of Variance Risk (2015, unknown): “Second, we build on the consumption-based asset pricing literature that has recently focused on the pricing of volatility, including Bansal and Yaron (2004), Drechsler and Yaron (2011), Wachter (2013), Campbell et al. (2014), and Bansal et al.”