Statistical Arbitrage Trading Strategies and High Frequency Trading

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We suggested the existence of stat arb strategies which could take advantage of the interrelationship. This is one of the simplest forms of stat arb and consists of:. To create a spread between futureswe have to take several things into account. A simple way to do this would be to look at the price changes in each contract and construct a linear regression model.

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 pairs trading strategy and statistical arbitrage 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 pairs trading strategy and statistical arbitrage 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.

I do not want to use trading window, because that pairs trading strategy and statistical arbitrage give false signal in case of large market move. You are commenting using your WordPress. You are commenting using your Twitter account. Pairs trading strategy and statistical arbitrage are commenting using your Facebook account. Notify me of new comments via email. January 28, 0.

April 6, 1. April 4, 1. March 31, 1. March 22, 0. Quantitative Trading Tagged as: Please let us know when pt2 or even pt3 are out. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in: Email required Address never made public.

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The pairs trade or pair trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: This strategy is categorized as a statistical arbitrage and convergence trading strategy. The strategy monitors performance of two historically correlated securities. When the correlation between the two securities temporarily weakens, i.

Pairs trading strategy demands good position sizing, market timing , and decision making skill. Although the strategy does not have much downside risk , there is a scarcity of opportunities, and, for profiting, the trader must be one of the first to capitalize on the opportunity. A notable pairs trader was hedge fund Long-Term Capital Management. Historically, the two companies have shared similar dips and highs, depending on the soda pop market. If the price of Coca Cola were to go up a significant amount while Pepsi stayed the same, a pairs trader would buy Pepsi stock and sell Coca Cola stock, assuming that the two companies would later return to their historical balance point.

If the price of Pepsi rose to close that gap in price, the trader would make money on the Pepsi stock, while if the price of Coca Cola fell, he would make money on having shorted the Coca Cola stock. The reason for the deviated stock to come back to original value is itself an assumption.

It is assumed that the pair will have similar business idea as in the past during the holding period of the stock. While it is commonly agreed that individual stock prices are difficult to forecast, there is evidence suggesting that it may be possible to forecast the price—the spread series—of certain stock portfolios.

A common way to attempt this is by constructing the portfolio such that the spread series is a stationary process. To achieve spread stationarity in the context of pairs trading, where the portfolios only consist of two stocks, one can attempt to find a cointegration irregularities between the two stock price series who generally show stationary correlation.

This irregularity is assumed to be bridged soon and forecasts are made in the opposite nature of the irregularity. 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.

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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. Common stock Golden share Preferred stock Restricted stock Tracking stock. Authorised capital Issued shares Shares outstanding Treasury stock.

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