Analyzing A South African Financial News Twitter Corpus using a Topic Model

Over the past decade there has been an increase in the amount of digital information that is available. In particular, there is now vasts amount of data that is available on social media platform such as twitter and Facebook that Continue reading Analyzing A South African Financial News Twitter Corpus using a Topic Model

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

Introduction to Diffusion and Jump Diffusion Processes

This post is the first part in a series of posts where we will be discussing jump diffusion models. In particular, we will first introduce diffusion and jump diffusion processes (part 1/3), then we will look at how to asses if Continue reading Introduction to Diffusion and Jump Diffusion Processes