How Predictable are Equity Covariance Matrices? Evidence from High Frequency Data for Four Markets
48 Pages Posted: 31 Jul 2012
Date Written: July 24, 2012
Abstract
Most pricing and hedging models rely on the long run temporal stability of a sample covariance matrix. Using a large dataset of equity prices from four countries, the US, UK, Japan and Germany, we test the rolling stability of realized sample covariance matrices using two complementary approaches; a standard covariance equality test and a novel matrix loss function approach. Our results present a pessmistic outlook for equilibrium models that require the covariance of assets returns to mean revert in the long run. We find that whilst, a daily first order Wishart autoregression is the best covariance matrix generating candidate, this non-mean reverting process cannot capture all of the time series variation in the covariance generating process.
Keywords: Realized Covariance, Microstructure, Wishart Distribution
JEL Classification: G14, G15, G17
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Modeling and Forecasting Realized Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
Modeling and Forecasting Realized Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Realized Exchange Rate Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Exchange Rate Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Exchange Rate Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Stock Return Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
By Torben G. Andersen, Tim Bollerslev, ...
-
Range-Based Estimation of Stochastic Volatility Models
By Sassan Alizadeh, Michael W. Brandt, ...
-
By Torben G. Andersen, Tim Bollerslev, ...
-
By Torben G. Andersen, Tim Bollerslev, ...