GARCH-type models have been very successful in describing the volatility dynamics of financial return series for short periods of time. However, time-varying behaviour of investors, for example, may cause the structure of volatility to change and the assumption of stationarity is no longer plausible. To deal with this issue, the current paper proposes a conditional volatility model with time-varying coefficients based on a multinomial switching mechanism. By giving more weight to either the persistence or shock term in a GARCH model, conditional on their relative ability to forecast a benchmark volatility measure, the switching reinforces the persistent nature of the GARCH model. Estimation of this benchmark volatility targeting or BVT-GARCH model for Dow 30 stocks indicates that the switching model is able to outperform a number of relevant GARCH setups, both in- and out-of-sample, also without any informational advantages.

GARCH, multinomial logit, time varying coefficients
Time-Series Models; Dynamic Quantile Regressions (jel C22), Financial Forecasting (jel G17)
dx.doi.org/10.1016/j.jempfin.2011.01.005, hdl.handle.net/1765/22293
ERIM Top-Core Articles
Academy of Management Journal
Accepted manuscript
Erasmus Research Institute of Management

Frijns, B.P.M, Lehnert, L, & Zwinkels, R.C.J. (2011). Modelling structural changes in the volatility process. Academy of Management Journal, 18(3), 522–532. doi:10.1016/j.jempfin.2011.01.005