# Vidyamurthy g 2004 pairs trading with options

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.

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 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. 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.