Sometimes, it is cheaper to buy a basket of assets than it is to buy the assets in the basket. Statistical Arbitrage. Statistical Arbitrage Statistical arbitrage exploits the pricing inefficiency between mean-reverting pairs of assets or buckets of assets in a market. A methodology to create statistical arbitrage in stock Index S&P500 is presented. They instead turn to authorised participants (APs), typically investment banks, who transact in the underlying securities. It involves quantitative modelling techniques to find price inefficiencies between assets. Like all trading strategies, statistical arbitrage has its risk. For example, if we were dealing with a CEF that invests in high yield bonds, we could compute the beta of the NAV to a high yield bond ETF (e.g HYG). Statistical arbitrage (stat arb) is a fancy term describing the process of buying assets that are statistically cheap and selling assets that are statistically expensive, hoping to lock in the difference. The correlation coefficient indicates the degree of correlation between the two variables. Rather than making a bet on the overall direction of either stock, we can eliminate idiosyncratic risk by trading the difference between the price of Coca-Cola shares and Pepsi shares. The fair price of this basket is then $\$289 + \$202 = \$491$. Praise for Statistical Arbitrage "In this lucid, intelligent, and highly readable book, Andrew Pole presents the insights of an experienced and successful exponent of statistical arbitrage, with an uncommon mixture of flair, accessibility, and academic precision. In my project I used a z-score of 1 as I noticed that other algorithms that I was competing with were using very low parameters. Stationary series, as a consequence of the definition, are almost always mean-reverting – wider spreads are more likely to be followed by narrower spreads and vice versa. JEL Classification C12, G13, G14. Like all trading strategies, statistical arbitrage has its risk. Funds Step 2: Calculate the spread To ensure that our correlations aren’t spurious, it is desirable for there to exist some fundamental economic reason why the two assets are related. All rights reserved. In statistical arbitrage, the trading basket is used to form a “spread” characterizing the “mis-pricing” of the assets which is stationary, hence mean-reverting. Now, we can perform an Augmented Dickey-Fuller test to determine whether the spread is indeed stationary. Jonathan Tuck, Raphael Abbou, Vin SachidanandaMS&E 448 Group 3: Statistical Arbitrage Strategy June 20202/20 You signed in with another tab or window. Therefore, the discrepancy between the Any momentary supply-demand imbalances can be arbitraged away – for example, if there is suddenly less demand for the ETF, and it trades at a discount to the NAV, the AP can buy ETF shares on the open market and redeem them from the ETF fund manager. Statistical arbitrage seeks to profit from statistical mispricing of one or more assets based on the expected value of these assets. Buy Statistical Arbitrage: Algorithmic Trading Insights and Techniques: 411 (Wiley Finance) 1 by Pole, Andrew (ISBN: 9780470138441) from Amazon's Book Store. An example of this is Anglo Gold and Harmony Gold. Statistical arbitrage trading or pairs trading as it is commonly known is defined as trading one financial instrument or a basket of financial instruments – in most cases to create a value neutral basket. Statistical Arbitrage includes different types of strategies such as pairs trading, index arbitrage, basket trading or delta neutral strategies. Statistical Arbitrage (SA) is a common financial term. Ø While the prices of NV and PLC follow each other closely, there is often a significant spread between the two. Subscribe. Statistical Arbitrage Statistical arbitrage aims to profit from a perceived discrepancy in price between two securities. Using an appropriate colour scheme, we can plot the price and NAV: From 2000 to 2009, the CEF alternates between trading at a discount/premium to NAV, before settling into a persistent discount from 2009 onwards. Investors identify the arbitrage situation through mathematical modeling techniques. In a recent MacroVoices podcast, Eric Peters shared a great insight (emphasis is mine): Our approach to anything that we ever do in markets, is to first ask the question: why do you get paid to do something? Program trading is often used by hedge funds and other institutional investors pursuing index arbitrage or other arbitrage strategies. A perfect positive correlation is when one variable mov… Buy Statistical Arbitrage: Algorithmic Trading Insights and Techniques: 411 (Wiley Finance) 1 by Pole, Andrew (ISBN: 9780470138441) from Amazon's Book Store. It contains a basket of options, including out-of-the-money options. We can verify that this is indeed a contributing factor by observing that there seems to be a broad negative correlation between asset liquidity and the spread. I set up three tests, each with a different number of observations (120, 90, 60), all three tests have to reject the null hypothesis that the pair is not co-integrated. As of the time of writing, AAPL has a price of \$289/share and FB has a price of \$202/share. Conversely, CEFs issue a fixed number of shares when they first IPO, and there is no subsequent creation or redemption. Praise for Statistical Arbitrage "In this lucid, intelligent, and highly readable book, Andrew Pole presents the insights of an experienced and successful exponent of statistical arbitrage, with an uncommon mixture of flair, accessibility, and academic precision. These strategies vary depending on number, types, and weights of instruments in a portfolio and its risk taking capacity. This talk was given by Max Margenot at the Quantopian Meetup in Santa Clara on July 17th, 2017. It is the idea that a co-integrated pair is mean reverting in nature. Cost of transacting – we already touched on this. taking a short position in the 2Year treasury futures - contract, gives rise to a statistical arbitrage. Emmanouil Mavrakis, Christos Alexakis, Statistical Arbitrage Strategies under Different Market Conditions: The Case of the Greek Banking Sector, Journal of Emerging Market Finance, 10.1177/0972652718776858, 17, 2, (159-185), (2018). In order to capture the dynamic of the market time adaptive algorithms have been developed and discussed. In this post, we discuss closed-end funds and why they often trade at a discount to their net asset value. This model is further analyzed and ex-tended by Papanicolaou and Yeo (2017), who discuss risk control and develop an optimization method to allocate the investments given the trading signals. 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