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

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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

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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

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A trader works on the floor of the New York Stock Exchange shortly after the closing bell in New York, July 13, 2015. REUTERS/Lucas Jackson

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→