Abstract Recent advances in high-frequency financial trading have made light propagation delays between geographically separated exchanges relevant. Here we show that there exist optimal locations from which to coordinate the statistical arbitrage of pairs of spacelike separated securities, and calculate a representative map of such locations on Earth. Furthermore, trading local securities along chains of such intermediate locations results in a novel econophysical effect, in which the relativistic propagation of tradable information is effectively slowed or stopped by arbitrage. Discuss this paper
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Diversified Statistical Arbitrage: Dynamically Combining Mean Reversion and Momentum Strategies by James Velissaris Abstract: This paper presents a quantitative investment strategy that is capable of producing strong risk-adjusted returns in both up and down markets. The strategy combines mean reversion and momentum investment strategies to construct a diversified statistical arbitrage approach. The mean reversion strategy decomposes stock returns into market and idiosyncratic return components using principal component analysis. The momentum strategy uses technical trading rules to trade momentum at the industry sector level. Dynamic portfolio optimization is utilized to rebalance exposures as the market environment evolves. The combined strategy was able to generate strong risk-adjusted returns in 2008 as the market declined, and in 2009 as the market rallied. The strategy has proven to be robust across two very different market environments in 2008 and 2009.
Keywords: Arbitrage, Principal Component Analysis, Statistical Arbitrage, Quantitative Finance Discuss this paper
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by Attilio Meucci Abstract: We introduce the multivariate Ornstein-Uhlenbeck and discuss how it generalizes a vast class of continuous-time and discrete-time multivariate processes. Relying on the simple geometrical interpretation of the dynamics of the Ornstein-Uhlenbeck process we introduce cointegration and its relationship to statistical arbitrage. We illustrate an application to swap contract strategies. Fully documented code illustrating the theory and the applications is available at MATLAB Central.
Keywords: alpha, z-score, signal, half-life, vector-autoregression (VAR), moving average (MA), VARMA, stationary, unit-root, mean-reversion, Levy processes Discuss this paper
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Presentation by Yoann BOURGEOIS and Marc MINKO IInnttrroodduuccttiioonn Single stocks in the Equity Market generally are not stationary. But, their yields, in many cases are. From the econometrical point of view, they are generally told to be Integrated of order 1. Cointegration is a mathematical theory that helps to handle the problem generated by non-stationary data. With the help of this theory, we propose to build linear combinations of these single stocks that are stationary. Such combinations can be traded and are called synthetic assets. Eventually, these stationary assets have the mean reversion property and we will use this property in order to set up arbitrage strategies Discuss this paper
This paper models the impact of statistical arbitrageurs on stock prices and trading volume when the drift of the dividend process is unknown to the hedge fund. The learning process of statistical arbitrageurs leads to an optimal trading strategy that can be upwardsloping in prices. The presence of privatly informed investors makes the equilibrium price dependent the history of trading volume and prices, and the optimal trading strategy of statistical arbitrageurs can be a positive feedback strategy for certain parameters and histories.
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Giovanni Montana, Kostas Triantafyllopoulos, Theodoros Tsagaris A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in real-time. When multiple co-evolving data streams are observed, an important task is to determine how these streams depend on each other, accounting for dynamic dependence patterns without imposing any restrictive probabilistic law governing this dependence. In this paper we argue that flexible least squares (FLS), a penalized version of ordinary least squares that accommodates for time-varying regression coefficients, can be deployed successfully in this context. Our motivating application is statistical arbitrage, an investment strategy that exploits patterns detected in financial data streams. We demonstrate that FLS is algebraically equivalent to the well-known Kalman filter equations, and take advantage of this equivalence to gain a better understanding of FLS and suggest a more efficient algorithm. Promising experimental results obtained from a FLS-based algorithmic trading system for the S&P 500 Futures Index are reported
One of the inefficiencies observed on the financial markets is a momentum effect. This inefficiency can be exploited by a trading strategy. Most of the empirical studies of momentum effect were made on the US stock market. In this thesis we test the momentum effect on the European markets, in particular, on the Swiss, French and German and elaborate a portfolio optimisation strategy, which would enable us to realise positive returns on the momentum portfolios. To implement this we use cumulative returns as an indicator of Discuss this paper
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Joe Ganley Giorgio Trebeschi World markets in the 1990s appear to have been subject to greater turbulence and to more shocks than hitherto. At the same time we observe a wide variety of market structures and trading platforms. This raises the question of whether, for a common shock, markets will respond differently. In particular, is it possible to rank the performance of different market structures during turbulent trading conditions? We examine the performance of four equity markets Discuss this paper
Outline 1 General Ideas 2 Bond market terminologies 3 Dynamic term structure models 4 Model design and estimation 5 Statistical arbitrage trading Discuss this paper
This article introduces the concept of a statistical arbitrage opportunity (SAO). In a finite-horizon economy, a SAO is a zero-cost trading strategy for which (i) the expected payoff is positive, and (ii) the conditional expected payoff in each final state of the economy is nonnegative. Unlike a pure arbitrage opportunity, a SAO can have negative payoffs provided that the average payoff in each final state is nonnegative. If the pricing kernel in the economy is path independent, then no SAOs can exist. Furthermore, ruling out SAOs imposes a novel martingale-type restriction on the dynamics of securities prices. The important properties of the restriction are that it (1) is model-free, in the sense that it requires no parametric assumptions about the true equilibrium model, (2) can be tested in samples affected by selection biases, such as the peso problem, and (3) continues to hold when investors' beliefs are mistaken. The article argues that one can use the new restriction to empirically resolve the joint hyothesis problem present in the traditional tests of the efficient market hypothesis. Discuss this paper
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S. Hogana, R. Jarrowb, M. Teoc*, M. Warachkad Abstract This paper introduces the concept of statistical arbitrage, a long horizon trading opportunity that generates a riskless profit and is designed to exploit persistent anomalies. Statistical arbitrage circumvents the joint hypothesis dilemma of traditional market efficiency tests because its definition is independent of any equilibrium model and its existence is incompatible with market efficiency. We provide a methodology to test for statistical arbitrage and then empirically investigate whether momentum and value trading strategies constitute statistical arbitrage opportunities. Despite adjusting for transaction costs, the influence of small stocks, margin requirements, liquidity buffers for the marking-to-market of short-sales, and higher borrowing rates, we find evidence that these strategies generate statistical arbitrage. Furthermore, their profitability does not appear to decline over time.
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P. J. BOLLAND AND J. T. CONNOR ABSTRACT We present a methodology for modelling real world high frequency financial data. The methodology copes with the erratic arrival of data and is robust to additive outliers in the data set. Arbitrage pricing relationships are formulated into a linear state space representation. Arbitrage opportunities violate these pricing relationships and are analogous to multivariate additive outliers. Robust identification/filtering of arbitrage opportunities in the data is accomplished by Kalman filtering. The state space model used to describe the pricing relationships is general enough to handle both linear and non-linear models. The recursive Kalman equations are adapted to filter tick data, cope with the erratic arrival of observations and produce estimates of all the arbitrage prices on every time step. We demonstrate the methodology with a robust neural network filter applied to foreign exchange triangular arbitrage. Tick data from three markets is used: $/DM, Discuss this paper
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KUN ZHU Abstract Statistical arbitrage is a profit situation arising from pricing inefficiencies between securities. This is usually identified through mathematical modeling techniques. Hogan, Jarrow, and Warachka describe the dynamics of trading profits as a stochastic process. A test for statistical arbitrage can then be based on identification of the parameters of the process. This project implements such a test, and experiments on interest rates of deposits, FRA, and swap contracts from 2002 to 2005 by a defined trading strategy. We observe only one statistical arbitrage opportunity on the market by this trading strategy from test results.
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