Risk Analytics using Parallel Processing

In this article we will understand the application of parallel computing for Risk Analytics. We introduced ourselves to the concept of parallel processing in my previous article that can be found here.

Risk Management is an area of finance that has been evolving over the years. Risk analytics is no stranger to innovation. There are periodic enhancements that market participants come up for measuring risk more efficiently. Sophisticated mathematical and statistical techniques are applied for developing models for risk analytics. With the rise of big-data, the use of technology in finance has risen manifold. Parallel computing is one such approach that has been adopted by the industry to make an efficient use of the available computation power of contemporary processors/systems.

In my earlier post on parallel computing, we had explored the use of parallel computation for option pricing. In this post we will extend this further to calculate the Monte Carlo Value at Risk (VaR) of an option position by leveraging the strength of parallel computing.

In this article we will understand the basics of VaR and subsequently implement a parallel computing engine for calculation of VaR of an option contract.

Risk Measurement:

There are multiple techniques for measurement for VaR. We will implement the Monte Carlo approach for VaR that is driven by fast processing power for parallel computing.

Parallel computing for VaR:

We will assume our option to be European Call option. We will use an option pricing model for valuing the option today. Further, we will use simulations to generate multiple scenarios in our case 1 million paths. Subsequently, we will compute the VaR of the option trade. Lets understand the implementation of this algorithm below!

Below are the steps for implementing this algorithm using Python:

  1. Import the required libraries to be used for this implementation along with the required details pertaining to the option trade.

2. Pricing the option today

3. We simulate 1 million asset price paths using parallel processing. Subsequently we compute the discounted value of the option payoff

4. We calculate the simulated P&L and then look up the VaR of the option position

Way Ahead !

Python continues to be in the forefront at the moment with its rich set of ever-increasing libraries. However, Julia programming seems to be another language that the industry is exploring. Although Julia programming is a relatively new language, it is expected to be a viable alternative to Python in the future. It is still too early to comment on the industry adoption of Julia computing, however, if it provides a faster implementation and is backed by a strong set of libraries that may get added over years to come, then this may be a language to watch for!

Founder: FinQuest Institute; www.finquestinstitute.com

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