# Pairs trading strategy and statistical arbitrage

In our case this is not very useful: Moreover the contracts have different minimum tick values. How do we reconcile the two contract specifications and create a useful trading ratio?

One way to normalize the two series would be to convert each price change into dollars. Suppose we are looking at the 5 minute raw price changes. Thus we could price our spread:. This type of spread ignores the correlation coefficient between the contracts 0. Which spread performs better?. Market conditions are the biggest determinant of which spread to chose.

In our experience, the volatility derived spread is better for short term intraday trading, especially when each contract is active and moving around. The correlation derived spread is better for lower volatility periods where you are essentially just scalping the initiating contract and using the hedge as insurance in case of a level change from the market. Specifically, Part II will examine the stability of the relationship between 6J and ZB and show how to create a hedge ratio which is dynamic through time.

This ratio will update as new information becomes available using a Bayes filter. The filter must strike a balance between how quickly it adapts to new information. Adapt too quickly and your hedge ratio will always be perfect for yesterday at the expense of today; adapt too slow and risk being blind-sided by a shift in volatility regimes.

Part III will examine several competing algorithms for determining buy and sell signals. Again we must strike a balance between risk and reward. Fade the spread too quickly and you risk getting run over; fade too conservatively and you risk missing out on profitable trading opportunities.

Still waiting eagerly for part III. When will it be available? I am not able to understand what will be trading signal when using kalman filter. Among those suitable for pairs trading are Ornstein-Uhlenbeck models, [5] [9] autoregressive moving average ARMA models [10] and vector error correction models.

The success of pairs trading depends heavily on the modeling and forecasting of the spread time series. They have found that the distance and co-integration methods result in significant alphas and similar performance, but their profits have decreased over time. Copula pairs trading strategies result in more stable but smaller profits.

Today, pairs trading is often conducted using algorithmic trading strategies on an execution management system. These strategies are typically built around models that define the spread based on historical data mining and analysis.

The algorithm monitors for deviations in price, automatically buying and selling to capitalize on market inefficiencies. The advantage in terms of reaction time allows traders to take advantage of tighter spreads. Trading pairs is not a risk-free strategy. The difficulty comes when prices of the two securities begin to drift apart, i.

Dealing with such adverse situations requires strict risk management rules, which have the trader exit an unprofitable trade as soon as the original setup—a bet for reversion to the mean—has been invalidated.

This can be achieved, for example, by forecasting the spread and exiting at forecast error bounds. A common way to model, and forecast, the spread for risk management purposes is by using autoregressive moving average models. From Wikipedia, the free encyclopedia. This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts , without removing the technical details.

November Learn how and when to remove this template message. Karlsruhe Institute of Technology. Retrieved 20 January An Introduction to the Cointelation Model". A Guide to Financial Data Analysis". University of Sydney, A Stochastic Control Approach". Proceedings of the American Control Conference, Monash University, Working Paper. Primary market Secondary market Third market Fourth market.