Parameter learning with particle filters

Pather, Vegan

Parameter learning with particle filters

Master Thesis

2020

Abstract

Common applications of asset-pricing models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this problem is to recalibrate at times where observed market prices cannot realistically match model prices based on parameter values at those times. This dissertation aims to test the effectiveness of a possible algorithm which can be used in optimally identifying such times. An overview is provided of the recently proposed particle filter with accelerated adaptation which has demonstrated rapid time detection for changes in parameter values and has been applied to regime-shifting and stochastic volatility models. Numerical and graphical evidence of parameter and volatility estimation will be provided under regime-shifting parameters for the Heston (1993) stochastic volatility model. The filter demonstrates rapid adaptation in estimating parameter values and accurate estimation of the volatility process. Furthermore, we provide a discussion for possible extensions towards a metric for optimal recalibration times.

Keywords

Mathematical Finance

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