stochastic-volatility-models-for-financial-time-series

Stochastic volatility models for financial time series

Filed under: Finance

Stochastic volatility models are used in mathematical finance to describe the evolution of asset returns, which typically exhibit changing variances over time.

Model description

The dataset is  previously analyzed by Harvey et al. (1994), and later by several other authors. The data consist of a time series of daily poundollar exchange rates {z_t} from the period 0181 to 285. The series of interest are the daily mean-corrected returns {y_t}, given by the transformation

The stochastic volatility model allows the variance of y_t to vary smoothly with time. This is achieved by assuming that ,

where

.

Here, the smoothly varying component x_t is assumed to be an autoregression.

Details

• sdv.pdf

Files

• sdv.tpl:  Model file
• sdv.dat: Data file
• sdv.pin: Starting values for the numerical optimizer
• sdv.par: Result file (what you get when you compile and run your model)