Year: 2012 Vol.: 61 No.: 2
Authors: Wendell Q. Campano
Volatility in time series data is often accounted into the model by postulating a conditionally heteroskedastic variance. In-sample prediction maybe satisfactory but the out-sample prediction is usually problematic. A test for presence of volatility through a nonparametric method is proposed. An estimation procedure for the stationary part of the model by integrating block bootstrap and AR-sieve into the forward search algorithm is also provided. Simulation studies indicated high power for the nonparametric procedure in detecting local volatilities. On the other hand, the estimation method generated robust estimates of the parameters of the time series model in the presence of temporary volatility.
Keywords: block bootstrap; AR-sieve; forward search algorithm; nonparametric test; volatility