Optimal trading strategies for Lévy-driven Ornstein–Uhlenbeck processes

Endres, Sylvia; Stübinger, Johannes

doi:10.1080/00036846.2019.1566688

Cited by 27 publications

(15 citation statements)

References 75 publications

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“…RAN however, blindly picks an odd out of the three outcomes-thus risky bets are by far the most compared to the other strategies. In conjunction with Table 4, we find that strategies that favour low-rik bets result in highest returns over the long run, wich is in line with literature [47][48][49].…”

Section: Financial Analysissupporting

confidence: 89%

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

2019

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In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

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Section: Financial Analysissupporting

confidence: 89%

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

2019

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“…In our application, to determine the threshold ν n = ∆ β n we choose β as the upper limit of β ∈ (0, 1/2) in line with Mancini (2009), Cont and Mancini (2011) and Endres and Stübinger (2017). The time interval is ∆ n = 1 250•391 (Cont andMancini 2011, Liu et al 2017) for our minute-by-minute data.…”

Section: Formation Periodmentioning

confidence: 99%

“…More precisely, we use the introduced algorithm to identify the optimal number of regimes. In each regime, a Lévydriven OU process is applied, reflecting leptokurtosis and discontinuities -both empirical features of return series (Bertram 2009, Aït-Sahalia and Jacod 2014, Göncü and Akyildirim 2016b, Endres and Stübinger 2017, Kou et al 2017. The optimal pairs for the trading period are selected based on mean-reversion speed, volatility, and jump behavior.…”

Section: Introductionmentioning

confidence: 99%

A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns

Endres

Stübinger

2019

Quantitative Finance

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This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any stochastic process and provides a complete set of parameter estimations. We demonstrate its performance in a simulation study-the algorithm achieves promising results for the general class of Lévy-driven Ornstein-Uhlenbeck processes with regime switches. In our empirical back-testing study, we apply our regime classification algorithm to propose a high-frequency pair selection and trading strategy. The results show statistically and economically significant returns with an annualized Sharpe ratio of 3.92 after transaction costs-results remain stable even in recent years. We compare our strategy with existing quantitative trading frameworks and find its results to be superior in terms of risk and return characteristics. The algorithm takes full advantage of its flexibility and identifies various regime patterns over time that are well-documented in the literature.

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“…Only two studies in this context have been published yet. They key work is provided by Bertram (2010a) -the spread X t is explained by the two-factor model of equation (41). The author derives the optimal analytic pairs trading strategy under the framework described in subsection 2.1.2, i.e., trades are entered and exited when the spread crosses trading thresholds a and m, a < m. For zero drift, i.e., 0, and independent Browian motions {W t } t 0 and {Z t } t 0 , the author calculates analytic solutions for the expected return…”

Section: Two-factor Modelmentioning

confidence: 99%

Review of stochastic differential equations in statistical arbitrage pairs trading

Endres¹

2020

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The use of stochastic differential equations offers great advantages for statistical arbitrage pairs trading. In particular, it allows the selection of pairs with desirable properties, e.g., strong mean-reversion, and it renders traditional rules of thumb for trading unnecessary. This study provides an exhaustive survey dedicated to this field by systematically classifying the large body of literature and revealing potential gaps in research. From a total of more than 80 relevant references, five main strands of stochastic spread models are identified, covering the ‘Ornstein–Uhlenbeck model’, ‘extended Ornstein–Uhlenbeck models’, ‘advanced mean-reverting diffusion models’, ‘diffusion models with a non-stationary component’, and ‘other models’. Along these five main categories of stochastic models, we shed light on the underlying mathematics, hereby revealing advantages and limitations for pairs trading. Based on this, the works of each category are further surveyed along the employed statistical arbitrage frameworks, i.e., analytic and dynamic programming approaches. Finally, the main findings are summarized and promising directions for future research are indicated.

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Optimal trading strategies for Lévy-driven Ornstein–Uhlenbeck processes

Cited by 27 publications

References 75 publications

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns

Review of stochastic differential equations in statistical arbitrage pairs trading

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