Calibrating Jump Diffusion Models using Differential Evolution

Determining the correct parameter values to be used in a Jump-Diffusion model is not a trivial process (as outlined here).  In this blog post we will be using the biologically inspired differential evolution technique to calibrate a Jump-Diffusion model using Continue reading Calibrating Jump Diffusion Models using Differential Evolution

Calibrating Financial Models using a Non-Parametric Technique

Traditionally, asset returns have been modeled using diffusion processes. Diffusion processes assume that the sample path of the process being modeled is continuous. However, empirical evidence suggests that there are jumps that occur in asset returns, such as those that Continue reading Calibrating Financial Models using a Non-Parametric Technique

A Test for the Presence of Jumps in Financial Markets using Neural Networks in R

Modelling of financial markets is usually undertaken using stochastic processes. Stochastic processes are collection of random variables indexed, for our purposes, by time. Examples of stochastic processes used in finance include GBM, OU, Heston Model and Jump Diffusion processes.  For Continue reading A Test for the Presence of Jumps in Financial Markets using Neural Networks in R

Understanding the EM Algorithm

Maximum Likelihood Estimation (MLE) is often the preferred method when it comes to the estimation of statistical models. It is preferred due to the "nice" properties that the estimators from this estimation algorithm have (e.g. estimators are asymptotically normal). This MLE Continue reading Understanding the EM Algorithm