Download or read book Regime switching Advantage in Statistical Arbitrage Strategies Conditioned on Time Series Momentum and Volatility in Leveraged Exchange Traded Funds written by Nisheeth Saini and published by . This book was released on 2019 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: The phenomena of volatility decay (also known as time decay) and path dependence in leveraged exchange traded funds (ETF) markets have been documented in the literature. This dissertation examined whether it is possible to exploit these market conditions for leveraged ETF (LETF) trading using statistical arbitrage (StatArb) strategies. The study proposed a regime switching model tailored for LETF markets to predict volatility and time-series momentum in the behavior of the underlying indexes of the LETFs. The study then used this model to test short pair trading strategies on a varied set of commodity LETFs to see if theoretical intuitions informed by these analyses were empirically supported by data. The study also introduced the concept of lag relative expected volatility (LREV) based on inductive learning in a binary classification framework to model upward shocks in expected volatility on any given trading day. The results of this study showed that an active short pair trading strategy in commodity LETFs, conditioned on momentum and volatility, outperforms an unconditioned and passive sell-and-hold StatArb trading strategy on a risk-adjusted basis. This outperformance was, however, found to be present in Sortino ratios only. The study did not find any evidence of outperformance for the active trading strategy in either Sharpe ratios or absolute returns. The results also provided further evidence that LETFs tracking equity indexes are poor candidates for active StatArb trading strategies due to low volatility. Further, the results also indicated that any incremental deterioration in the efficiency of LETF products in rapidly fluctuating markets appears to be mostly attributable to systemic jumps in the implied volatility and less due to any incremental inefficiency in their daily rebalancing process. This finding may be of interest to the regulators. Lastly, the study also provided evidence from the LETF markets for an inverse relationship between volatility and momentum, as established in some recent studies.
Download or read book A Regime Switching Relative Value Arbitrage Rule written by Michael Bock and published by . This book was released on 2008 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: The relative value arbitrage rule (quot;pairs tradingquot;) is a well-established speculative investment strategy on financial markets, dating back to the 1980s. Based on relative mispricing between a pair of stocks, pairs trading strategies create excess returns if the spread between two normally comoving stocks is away from its equilibrium path and is assumed to be mean reverting. To overcome the problem of detecting temporary in contrast to longer lasting deviations from spread equilibrium, this paper bridges the literature on Markov regime-switching and the scientific work on statistical arbitrage.
Download or read book Statistical Arbitrage Opportunities Between Commodity Futures and Commodity Currency Futures written by Jan-Philipp Weber and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces two algorithmic statistical arbitrage trading strategies based on the fixed hedge ratio of the Engle-Granger cointegration regression and the daily forward-looking hedge ratio of the Kalman filter. Both trading strategies have the objective to exploit short-term deviations from the stochastic long-term equilibrium between country-specific commodity currency futures and commodity futures of Australia, Canada, New Zealand and South Africa based on daily futures prices in the time period from 2005 until 2013. The empirical results suggest that the cointegration relationship between commodity currency futures and commodity futures is highly unstable and switches between a non-cointegrated and a cointegrated regime over time. The error correction models show that commodity futures are weakly exogenous and that commodity currency futures mainly react to short-term deviations from the long-term equilibrium. In addition, the Kalman filter reveals that the pair-specific hedge ratios are highly sensitive over time. The thesis demonstrates that both trading strategies are suitable to exploit statistical arbitrage opportunities based on different combinations between the trading threshold and convergence target. However, the profitability of both trading strategies declined out-of-sample owed to the regime switches in the cointegration relationship and the smaller size of the price anomalies. Further research should focus on the time-varying properties of the hedge ratios and the causes for the regime switches in the cointegration relationship including the implementation of non-linear, respectively regime switching models. Also the pair-specific holdings of the portfolios could be optimised and the performance of the trading strategies tested on high frequency data.
Download or read book Mixture Time Series Models and Their Applications in Volatility Estimation and Statistical Arbitrage Trading written by Xixin Cheng and published by . This book was released on 2008 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing Market Efficiency Using Statistical Arbitrage with Applications to Momentum and Value Strategies written by S. Hogan and published by . This book was released on 2003 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces the concept of statistical arbitrage, a long horizon trading opportunity that generates a riskless profit and is designed to exploit persistent anomalies. The authors provide a methodology to test for statistical arbitrage and then empirically investigate whether momentum and value trading strategies constitute statistical arbitrage opportunities.
Download or read book Stochastic Control and Deep Learning Approaches to High dimensional Statistical Arbitrage written by Jorge Guijarro Ordonez and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The central problem of this dissertation is the mathematical study of statistical arbitrage in the case of a high-dimensional number of assets, which is analyzed from two complementary approaches. In the first part of the dissertation, we consider the problem from a stochastic control perspective that extends and combines the Avellaneda and Lee model for statistical arbitrage with the classical Merton framework for portfolio theory. In our framework, given a high-dimensional number of assets and a mean-reverting stochastic model for the dynamics of their residuals through a statistical factor model, an investor must decide how to trade the original assets to maximize the expected utility of her terminal wealth in a finite time horizon, while taking into account market frictions and common statistical arbitrage constraints like dollar neutrality. We study continuous-time and discrete-time versions of the trading problem with both exponential utility and a mean-variance objective, and we prove the existence of interpretable analytic or semi-analytic optimal trading strategies through the study of the corresponding Hamilton-Jacobi-Bellman partial differential equations. We supplement this theoretical study with extensive Monte Carlo simulations that provide further insight about the qualitative behavior of the found optimal strategies under different parameter regimes. In the second part of the dissertation, we complement the previous study with a general deep-learning framework that mitigates two limitations of the stochastic control approach: strong modeling assumptions on the residual dynamics, and solving the high-dimensional Hamilton-Jacobi-Bellman equations for more realistic objective functions, models, and constraints. To this end, we frame the residual modeling and trading problems as a double optimal control problem, that we solve numerically by restricting the controls to a series of functional classes that range from classical parametric models to the most advanced neural network architectures adapted to our problem. We test these methods by conducting an extensive out-of-sample empirical study with high-capitalization U.S. equity data over the main families of factor models, which provides a comprehensive analysis of the importance of the different elements of a statistical arbitrage strategy and the gains from machine learning methods.
Download or read book An Adaptive Econometric System for Statistical Arbitrage written by Gabriel Jan Andries Visagie and published by . This book was released on 2017 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical arbitrage -- Pairs trading -- Volatility modelling -- Algorithmic trading.
Download or read book Momentum Market Regime and Stocks Options Trading Strategies written by PRM Awoga CPA (Oluwaseyi (Tony).) and published by . This book was released on 2019 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research project seeks to examine the relationship between momentum, stocks and options trading strategies. First, we examine a simple momentum trading strategy for stocks. These discussions are then extended and applied to options trading. Further, we explore how changes in economic conditions can cause trading performances to change from long-term averages and techniques that can be used to mitigate the impact of volatility and regime-shifts on trading performance.
Download or read book Leveraged Exchange Traded Funds written by Peter Miu and published by Palgrave Macmillan. This book was released on 2016-01-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leveraged Exchange-Traded Funds (LETFs) are publicly-traded funds that promise to provide daily returns that are in a multiple (positive or negative) of the returns on an index. To meet that promise, the funds use leverage, which is typically obtained through derivatives such as futures contracts, forward contracts, and total-return swaps. As of the end of 2012, there were over 250 LETFs in North America with total assets of approximately $32.24 billion. While the amount of assets held by these funds is still small, their popularity continues to grow as their trading volume is significantly larger and much more dynamic than traditional, non-leveraged ETFs. This comprehensive guide to LETFs provides high-level practitioners and researchers with a detailed reference tool for navigating the market and making informed investment decisions. Written from a measured analytical perspective, Miu and Charupat use clear and concise explanations of all important aspects of LETFs, focusing on such key elements as structure, pricing, performance, regulations, taxation, and trading strategies. The first two chapters set the stage for the book by identifying exactly what LETFs are and how they are regulated. The following chapters then look to bridge theory with practice to dive deep into the mechanics, portfolio rebalancing techniques, and daily compounding effects that make investing in these funds so lucrative.
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Download or read book Handbook of Finance written by Frank J. Fabozzi and published by . This book was released on 2008-10-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Handbook of Finance is a comprehensive 3-Volume Set that covers both established and cutting-edge theories and developments in finance and investing. Edited by Frank Fabozzi, this set includes valuable insights from global financial experts as well as academics with extensive experience in this field. Organized by topic, this comprehensive resource contains complete coverage of essential issues—from portfolio construction and risk management to fixed income securities and foreign exchange—and provides readers with a balanced understanding of today’s dynamic world of finance. A brief look at each volume: Volume I: Financial Markets and Instruments skillfully covers the general characteristics of different asset classes, derivative instruments, the markets in which financial instruments trade, and the players in those markets. Volume II: Investment Management and Financial Management focuses on the theories, decisions, and implementations aspects associated with both financial management and investment management. Volume III Valuation, Financial Modeling, and Quantitative Tools contains the most comprehensive coverage of the analytical tools, risk measurement methods, and valuation techniques currently used in the field of finance."
Download or read book Trading Volatility written by Colin Bennett and published by . This book was released on 2014-08-17 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This publication aims to fill the void between books providing an introduction to derivatives, and advanced books whose target audience are members of quantitative modelling community. In order to appeal to the widest audience, this publication tries to assume the least amount of prior knowledge. The content quickly moves onto more advanced subjects in order to concentrate on more practical and advanced topics. "A master piece to learn in a nutshell all the essentials about volatility with a practical and lively approach. A must read!" Carole Bernard, Equity Derivatives Specialist at Bloomberg "This book could be seen as the 'volatility bible'!" Markus-Alexander Flesch, Head of Sales & Marketing at Eurex "I highly recommend this book both for those new to the equity derivatives business, and for more advanced readers. The balance between theory and practice is struck At-The-Money" Paul Stephens, Head of Institutional Marketing at CBOE "One of the best resources out there for the volatility community" Paul Britton, CEO and Founder of Capstone Investment Advisors "Colin has managed to convey often complex derivative and volatility concepts with an admirable simplicity, a welcome change from the all-too-dense tomes one usually finds on the subject" Edmund Shing PhD, former Proprietary Trader at BNP Paribas "In a crowded space, Colin has supplied a useful and concise guide" Gary Delany, Director Europe at the Options Industry Council
Download or read book Algorithmic Trading written by Ernie Chan and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Algorithmic TRADING “Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers.” —DAREN SMITH, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management “Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses.” —ROGER HUNTER, Mathematician and Algorithmic Trader
Download or read book Trend Following with Managed Futures written by Alex Greyserman and published by John Wiley & Sons. This book was released on 2014-08-25 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: An all-inclusive guide to trend following As more and more savvy investors move into the space, trend following has become one of the most popular investment strategies. Written for investors and investment managers, Trend Following with Managed Futures offers an insightful overview of both the basics and theoretical foundations for trend following. The book also includes in-depth coverage of more advanced technical aspects of systematic trend following. The book examines relevant topics such as: Trend following as an alternative asset class Benchmarking and factor decomposition Applications for trend following in an investment portfolio And many more By focusing on the investor perspective, Trend Following with Managed Futures is a groundbreaking and invaluable resource for anyone interested in modern systematic trend following.
Download or read book Market Tremors written by Hari P. Krishnan and published by Springer Nature. This book was released on 2021-09-14 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the Global Financial Crisis, the structure of financial markets has undergone a dramatic shift. Modern markets have been “zombified” by a combination of Central Bank policy, disintermediation of commercial banks through regulation, and the growth of passive products such as ETFs. Increasingly, risk builds up beneath the surface, through a combination of excessive leverage and crowded exposure to specific asset classes and strategies. In many cases, historical volatility understates prospective risk. This book provides a practical and wide ranging framework for dealing with the credit, positioning and liquidity risk that investors face in the modern age. The authors introduce concrete techniques for adjusting traditional risk measures such as volatility during this era of unprecedented balance sheet expansion. When certain agents in the financial network behave differently or in larger scale than they have in the past, traditional portfolio theory breaks down. It can no longer account for toxic feedback effects within the network. Our feedback-based risk adjustments allow investors to size their positions sensibly in dangerous set ups, where volatility is not providing an accurate barometer of true risk. The authors have drawn from the fields of statistical physics and game theory to simplify and quantify the impact of very large agents on the distribution of forward returns, and to offer techniques for dealing with situations where markets are structurally risky yet realized volatility is low. The concepts discussed here should be of practical interest to portfolio managers, asset allocators, and risk professionals, as well as of academic interest to scholars and theorists.
Download or read book Multi Period Trading Via Convex Optimization written by Stephen Boyd and published by . This book was released on 2017-07-28 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.
Download or read book The Current State of Quantitative Equity Investing written by Ying L. Becker and published by CFA Institute Research Foundation. This book was released on 2018-05-10 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative equity management techniques are helping investors achieve more risk efficient and appropriate investment outcomes. Factor investing, vetted by decades of prior and current research, is growing quickly, particularly in in the form of smart-beta and ETF strategies. Dynamic factor-timing approaches, incorporating macroeconomic and investment conditions, are in the early stages but will likely thrive. A new generation of big data approaches are rendering quantitative equity analysis even more powerful and encompassing.
