# Stochastic volatility models for financial time series
ADMB Files
Code: sdv.tpl Data: sdv.dat Initial values: sdv.pin All required files (DOS): sdv.zip All required files (linux): sdv.tar.gz Results: sdv.par
Running ADMB-executables
In a DOS window Under linux Command line option: `-ilmn 5`.
Results: Computation times
ADMB-RE: 22 seconds. BUGS : 1.7 hours ; (Meyer & Yu, 2000, Table 1).
Navigation
ADMB-RE home Otter Research
| ### Model description Stochastic volatility models are used in mathematical finance to describe the evolution of asset returns, which typically exhibit changing variances over time. As an illustration we use a dataset previously analyzed by Harvey et al. (1994), and later by several other authors. The data consist of a time series of daily pound/dollar exchange rates {zt} from the period 01/10/81 to 28/6/85. The series of interest are the daily mean-corrected returns {yt}, given by the transformation yt = log(zt)-log(zt-1) - average[logzi-logzi-1]. The stochastic volatility model allows the variance of yt to vary smoothly with time. This is achieved by assuming that yt ~ N(0,st), where st = exp{-0.5(mx+xt)}. Here, the smoothly varying component {xt} is assumed to follow an autoregression xt = b xt-1 + et, where et ~ N(0,s2). Further details about the model can be found here: sdv.pdf.