VaRT :: Analyzes the Dow Jones Industrials using Value-at-Risk / Trending


Value-at-Risk (VaR) is fast becoming the industry risk management standard for both the banking and asset management industry. The Java applet demonstrates how fast VaR can be analyzed and how simply it can be delivered, it goes one step further by adding another tool; VaR Trending (VaR/T) to quantify for illiquid markets during times of financial crisis. Click How to Use to read a detailed description of the VaR/T applet. Click Start to launch the Java applet.

This demonstration has three purposes:

  1. To show how quickly VaR can be analyzed and how simply it can be delivered using Java

  2. To introduce VaR Trending (VaR/T) as a solution for quantifing financial shocks and illiquidity

  3. To demonstrate my risk management, quantitative modeling and programming skills

The instantaneous response times of the applet is achieved through optimized algorithms combined with the effects of Moore's Law. Speed is an essential ingredient for financial modeling and now that microprossesor speeds are so fast and doubling ever 18 months, properly written Java programs can easily compete with programs written in C / C++. Writing in Java enables achievement of the next milestone, reaching a vast audience. The Java language is quickly becoming a competitive advantage in the Information age as it excels both on the client side and server side operating both over a private network and on the Internet.

In 1996, the Basel Committee on Bank Supervision recommended that the minimum capital requirements for market risks should be based on VaR. Furthermore, regulatory capital should equal the 99% one-day VaR, multiplied by 3 times the square root of 10. The square root of 10 represents the losses that could occur over a 10 day holding period during a financial crisis where markets can become illiquid. The introduced VaR/T tool calculates for illiquid periods by using historical data to compute for negatively trending markets. VaR/T searches for negative trends and quantifies the worst 1%, 0.1% and worst case. Illiquidity is accounted for during the downtrend and liquidity is returned at the end of the downtrend with a positively closing market.