Predicting Covariance Matrices with Financial Conditions Indexes
We model the impact of financial conditions on asset market volatility and correlation. We propose extensions of (factor-)GARCH models for volatility and DCC models for correlation that allow for including indexes that measure financial conditions. In our empirical application we consider daily stock returns of US deposit banks during the period 1994-2011, and proxy financial conditions by the Bloomberg Financial Conditions Index (FCI) which comprises the money, bond, and equity markets. We find that worse financial conditions are associated with both higher volatility and higher average correlations between stock returns. Especially during crises the additional impact of the FCI indicator is considerable, with an increase in correlations by 0.15. Moreover, including the FCI in volatility and correlation modeling improves Value-at-Risk forecasts, particularly at short horizons.
|bank holding companies, dynamic correlations, financial conditions indexes, volatility modeling|
|Financial Markets and the Macroeconomy (jel E44), Financial Forecasting (jel G17), Pension Funds; Other Private Financial Institutions (jel G23)|
|Tinbergen Institute Discussion Paper Series|
|Discussion paper / Tinbergen Institute|
|Organisation||Erasmus School of Economics|
Opschoor, A, van Dijk, D.J.C, & van der Wel, M. (2013). Predicting Covariance Matrices with Financial Conditions Indexes (No. TI 13-113/III). Discussion paper / Tinbergen Institute (pp. 1–43). Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/40962