IBM Obtains Patent for Estimating Volatility in High-Frequency Trading
January 13, 2012
Is gauging the swings of prices in stocks important to high-frequency trading?
Apparently, IBM thinks so.
This could explain why it just obtained a patent for a method for estimating current volatility of price of a security based on high-frequency trading and pricing data. The International Business Machines Corporatioreceived U.S. Patent US7865332 for a “scaled exponential smoothing for real time histogram” on January 4, 2012.
“This might give an IBM an edge with its low-latency trading solutions,” says John Bollinger, president and founder of Bollinger Capital Management and the creator of Bollinger Bands, a tool for assessing expected stock action. “But I’m surprised IBM patented this. If Harry Markowitz had patented his model, it’s likely only a few people would have ever used it.”
A Nobel Memorial Prize winner in Economic Sciences, Markowitz developed the HM Model or Mean Variance Model which is designed to show investors how to reduce risk.
It also could prove beneficial to traders.”It is useful to be able to predict impending stock volatility levels - as it enables trading firms to tune the selection and configuration of automated trading strategies to be the most effective in the market conditions,” says Dr. John C. Bates, chief technology officer, Progress Software.
“For instance, a trader may use market volatility to tune the way algorithms participate in the market, based, for example, on the relative movements of share prices versus their sector, index, future, or the balance of buy and sell orders in the market,” Bates notes. “An algorithm can respond differently depending on whether a share price is developing favorable or adverse short-term momentum. If, for example, a stock is favorably or adversely mispriced, based on your real-time analysis, an algorithm might increase or decrease its trade sizes accordingly. “
The patent covers:
1. a method comprising:
receiving high frequency trading and pricing data for a security;
estimating current volatility of price of said security based on said high frequency trading and pricing data;
forecasting future volatility of said price using two or more volatility forecasting models;
back-testing each of said two or more models out-of-sample;
ranking said two or more models in terms of reliability of each of said models, over a recent period of time, for said security; and
reporting volatility forecasts of each of said models to a user, along with each model's reliability ranking.
2. A method as in claim 1, further comprising reporting a current volatility estimate for said security.
3. A method as in claim 1, wherein current volatility is estimated using historical volatility estimation.
4. A method as in claim 1, wherein current volatility is estimated using implied volatility estimation.
5. A method as in claim 1, wherein bid-ask bounce, missing trades, and overnight closes are taken into account when estimating current volatility of price of said security based on said high frequency trading and pricing data.
