A Note on Model Uncertainty for Statistical Arbitrage PDF Download
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Author: Daisuke Yoshikawa
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this paper, we consider an optimal stopping problem that addresses model uncertainty. Model uncertainty is the uncertainty affecting the model assumptions, e.g., the assumed form of the probability distribution, the parameters embedded in the probability distribution. The result presented in this paper shows the explicit form of the boundary indicating the optimal stopping time, assuming the portfolio value as an Ornstein-Uhlenbeck process. Furthermore, the boundary can be regulated depending on the ambiguity of the estimated model. Thus, the boundary is more robust than a boundary derived without taking into account model uncertainty. In this sense, the application of our method might make statistical arbitrage more robust, because the trading code for statistical arbitrage is often based on the statistical test which might lead the incorrect estimation. We also show that the value function for the optimal stopping problem that addresses model uncertainty has a consistent structure with the certainty equivalent, which is used to derive the risk premium in the context of the expected utility with risk rather than model uncertainty.
Author: Daisuke Yoshikawa
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this paper, we consider an optimal stopping problem that addresses model uncertainty. Model uncertainty is the uncertainty affecting the model assumptions, e.g., the assumed form of the probability distribution, the parameters embedded in the probability distribution. The result presented in this paper shows the explicit form of the boundary indicating the optimal stopping time, assuming the portfolio value as an Ornstein-Uhlenbeck process. Furthermore, the boundary can be regulated depending on the ambiguity of the estimated model. Thus, the boundary is more robust than a boundary derived without taking into account model uncertainty. In this sense, the application of our method might make statistical arbitrage more robust, because the trading code for statistical arbitrage is often based on the statistical test which might lead the incorrect estimation. We also show that the value function for the optimal stopping problem that addresses model uncertainty has a consistent structure with the certainty equivalent, which is used to derive the risk premium in the context of the expected utility with risk rather than model uncertainty.
Author: Bo Hu
Publisher:
ISBN:
Category :
Languages : en
Pages : 62
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Book Description
I develop a model of statistical arbitrage trading in an environment with "fat-tailed" information. If risk-neutral arbitrageurs are uncertain about the variance of fat-tail shocks and if they implement max-min robust optimization, they will choose to ignore a wide range of pricing errors. Although model risk hinders their willingness to trade, arbitrageurs can capture the most pro table opportunities because they follow a linear momentum strategy beyond the inaction zone. This is equivalent to a machine-learning algorithm called LASSO. Arbitrageurs can also amass market power due to conservative trading under this strategy. Their uncoordinated exercise of robust control facilitates tacit collusion, protecting their pro ts from being competed away even if their number goes to in infinity. In an extended model where an insider strategically interacts with those arbitrageurs, the insider can induce them to trade too aggressively, giving herself a reversal trading opportunity. Doing so distorts price informativeness and threatens market stability.
Author: Andrew Pole
Publisher: John Wiley & Sons
ISBN: 1118160738
Category : Business & Economics
Languages : en
Pages : 230
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Book Description
While statistical arbitrage has faced some tough times?as markets experienced dramatic changes in dynamics beginning in 2000?new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole?s own research and experience running a statistical arbitrage hedge fund for eight years?in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading?this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.
Author: Theo K. Dijkstra
Publisher: Springer Science & Business Media
ISBN: 3642615643
Category : Mathematics
Languages : en
Pages : 149
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Book Description
In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.
Author: Vladimir Ostrovski
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
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Book Description
We study the stability of the no-arbitrage property under model uncertainty. We measure model uncertainty with the total variation distance of underlying probability distributions. We show that sufficiently small changes of the underlying probability distribution preserve the no-arbitrage property of the financial market model.
Author: Michael Isichenko
Publisher: John Wiley & Sons
ISBN: 1119821215
Category : Business & Economics
Languages : en
Pages : 306
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Book Description
Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.
Author: Seoungbyung Park
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Massimo Morini
Publisher: John Wiley & Sons
ISBN: 0470977612
Category : Business & Economics
Languages : en
Pages : 452
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Book Description
A guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications.
Author: Andrew Neil Burgess
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Erik Larsson
Publisher:
ISBN:
Category :
Languages : en
Pages : 62
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Book Description
The momentum effect is a systematic inefficiency in the market that can be exploited by a trading strategy. This conclusion is supported by theoretical and empirical evidence. But the academic research that tries to quantify the performance of this kind of strategy often relies on a methodology that is too simplistic. The question arises what performance a trader realistically could achieve in relation to the results presented in academic journals. To answer this, we have written a computer program to run simulations with the added realism of transaction costs and more advanced trading rules based on a wider array of data than classic methodology allows. This has been done on Swedish stocks between 1995 and 2001. We then compare the simulation based on our own advanced model with a simulation that emulates a simplistic methodology.It is found that the negative impact on return of including transaction costs is outweighed by the lower risk attributed to our more advanced trading rules, as indicated by e.g. Sharpe and standard measures of risk. We can thus conclude that the momentum effect might be even more attractive as a basis for a trading strategy than have been suggested in prior academic research.As an academic paper, we think that the methodology (our simulation platform) used to obtain the conclusion in our thesis is more important than the conclusion itself. It is evident that a good evaluation of any trading strategy requires more realistic simulations than is commonplace in academia today.
