SSRN Author: Stefano ColucciStefano Colucci SSRN Content
http://www.ssrn.com/author=1715697
http://www.ssrn.com/rss/en-usFri, 13 Jan 2017 03:23:16 GMTeditor@ssrn.com (Editor)Fri, 13 Jan 2017 03:23:16 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Shrunk Volatility VaR: An Application on US Balanced PortfoliosWe test the naive model to forecast ex-ante Value-at-Risk (VaR) using a shrinkage estimator between realized volatility estimated on past return time series, and implied volatility quoted on the market. Implied volatility is often indicated as the operators expectation about future risk, while the historical volatility straightforwardly represents the realized risk prior to the estimation point, which by deﬁnition is backward looking. Therefore, the VaR prediction strategy uses information both on the expected future risk and on the past estimated risk. We examine the model presented by (Cesarone and Colucci, 2016), called Shrunk Volatility VaR, an we validate it in the multivariate framework in particular on US equity and bonds, empirically comparing its forecasting power with that of four benchmark VaR models. The performance of all VaR models is validated using both statistical accuracy and eﬃciency evaluation tests on 39 equal spaced balanced portfolios over an out-of-sample ...
http://www.ssrn.com/abstract=2896840
http://www.ssrn.com/1557653.htmlThu, 12 Jan 2017 17:18:46 GMTNew: A Quick Tool to Forecast VaR Using Implied and Realized VolatilitiesWe propose here a naive model to forecast ex-ante Value-at-Risk (VaR) using a shrinkage estimator between realized volatility estimated on past return time series, and implied volatility extracted from option pricing data. Implied volatility is often indicated as the operators expectation about future risk, while the historical volatility straightforwardly represents the realized risk prior to the estimation point, which by definition is backward looking. In a nutshell, our prediction strategy for VaR uses information both on the expected future risk and on the past estimated risk.
We examine our model, called Shrinked Volatility VaR, both in the univariate and in the multivariate cases, empirically comparing its forecasting power with that of two benchmark VaR estimation models based on the Historical Filtered Bootstrap and on the RiskMetrics approaches.
The performance of all VaR models analyzed is evaluated using both statistical accuracy tests and efficiency evaluation tests, ...
http://www.ssrn.com/abstract=2714443
http://www.ssrn.com/1460048.htmlThu, 14 Jan 2016 02:38:27 GMT