In this paper we compare two basic approaches to forecast volatility in the German stockmarket. The first approach uses various univariate time series techniques while the secondapproach makes use of volatility implied in option prices. The time series models include thehistorical mean model, the exponentially weighted moving average (EWMA) model, fourARCH-type models and a stochastic volatility (SV) model. Based on the utilization of volatilityforecasts in option pricing and Value-at-Risk (VaR), various forecast horizons and forecast errormeasurements are used to assess the ability of volatility forecasts. We show that the modelrankings are sensitive to the error measurements as well as the forecast horizons. The resultindicates that it is difficult to state which method is the clear winner. However, when optionpricing is the primary interest, the SV model and implied volatility should be used. On the otherhand, when VaR is the objective, the ARCH-type models are useful. Furthermore, a tradingstrategy suggests that the time series models are not better than the implied volatility inpredicting volatility.
Forecasting Volatility, ARCH Model, SV Model, Implied Volatility, VaR, Germany
Finance | Finance and Financial Management
City or Country
The University of Auckland
BLUHM, Hagen H. W. and YU, Jun.
Forecasting volatility: Evidence from the German stock market. (2001). 173-193. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/2123