Advanced Analytics with PySpark

Chapter 8. Estimating Financial Risk

Is there a way to approximate how much you can expect to lose when investing in financial markets? This is the quantity that the financial statistic value at risk (VaR) seeks to measure. VaR is a simple measure of investment risk that tries to provide a reasonable estimate of the maximum probable loss in value of an investment portfolio over a particular time period. A VaR statistic depends on three parameters: a portfolio, a time period, and a probability. For example, a VaR value of $1 million with a 5% probability and two weeks indicates the belief that the portfolio stands only a 5% chance of losing more than $1 million over two weeks.

Since its development soon after the stock market crash of 1987, VaR has seen widespread use across financial services organizations. The statistic plays a vital role in the management of these institutions by helping to determine the risk characteristics of their strategies.

Many of the most sophisticated approaches to estimating this statistic rely on computationally intensive simulations of markets under random conditions. The technique behind these approaches, called the Monte Carlo simulation, involves posing thousands or millions of random market scenarios and observing how they tend to affect a portfolio. These scenarios are referred to as trials. PySpark is an ideal tool for Monte Carlo simulations. PySpark can leverage thousands of cores to run random trials and aggregate their results. As a general-purpose ...