This paper analyzes the out-of-sample performance of two common extensions of the Black-Scholes framework, namely a GARCH and a stochastic volatility option pricing model.

This paper compares the quality of commonly used predictors for the volatility of stock returns. The techniques studied are moving averages of squared returns, GARCH and stochastic volatility models, and the implied volatility. We use a variety of econometric criteria to assess the forecasting...

The search for deterministic chaos in economic and financial time series has attracted much interest over the past decade. Evidence of chaotic structures is usually blurred, however, by large random components in the time series. In the first part of this paper, a sophisticated algorithm for...

One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density...

Motivated by previous findings that discretization of financial time series can effectively filter the data and reduce the noise, this experimental study compares the trading performance of predictive models based on different modelling paradigms in a realistic setting. Different methods ranging...

In this paper we investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. The main argument is that careful quantization can reduce the noise in the time series to make model...