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Equa Risk Pricing Hedging And Portfolio Management Using Dynamic Risk Measures And Deep Reinforcement Learning Methods
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Book Synopsis Equa-risk Pricing, Hedging, and Portfolio Management Using Dynamic Risk Measures and Deep Reinforcement Learning Methods by : Saeed Marzban
Download or read book Equa-risk Pricing, Hedging, and Portfolio Management Using Dynamic Risk Measures and Deep Reinforcement Learning Methods written by Saeed Marzban and published by . This book was released on 2021 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pricing and Hedging Financial Derivatives with Reinforcement Learning Methods by : Alexandre Carbonneau
Download or read book Pricing and Hedging Financial Derivatives with Reinforcement Learning Methods written by Alexandre Carbonneau and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies the problem of pricing and hedging financial derivatives with reinforcement learning. Throughout all four papers, the underlying global hedging problems are solved using the deep hedging algorithm with the representation of global hedging policies as neural networks. The first paper, "Equal Risk Pricing of Derivatives with Deep Hedging'', shows how the deep hedging algorithm can be applied to solve the two underlying global hedging problems of the equal risk pricing framework for the valuation of European financial derivatives. The second paper, "Deep Hedging of Long-Term Financial Derivatives'', studies the problem of global hedging very long-term financial derivatives which are analogous, under some assumptions, to options embedded in guarantees of variable annuities. The third paper, "Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments'', studies derivative prices generated by the equal risk pricing framework for long-term options when shorter-term options are used as hedging instruments. The fourth paper, "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures'', investigates the use of non-translation invariant risk measures within the equal risk pricing framework.
Book Synopsis Deep Reinforcement Learning for Option Pricing and Hedging Under Dynamic Expectile Risk Measures by : Saeed Marzban
Download or read book Deep Reinforcement Learning for Option Pricing and Hedging Under Dynamic Expectile Risk Measures written by Saeed Marzban and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Asset Pricing by : Wayne Ferson
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Book Synopsis Multi-agent Deep Reinforcement Learning and GAN-based Market Simulation for Derivatives Pricing and Dynamic Hedging by : Samson Qian
Download or read book Multi-agent Deep Reinforcement Learning and GAN-based Market Simulation for Derivatives Pricing and Dynamic Hedging written by Samson Qian and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in computing capabilities have enabled machine learning algorithms to learn directly from large amounts of data. Deep reinforcement learning is a particularly powerful method that uses agents to learn by interacting with an environment of data. Although many traders and investment managers rely on traditional statistical and stochastic methods to price assets and develop trading and hedging strategies, deep reinforcement learning has proven to be an effective method to learn optimal policies for pricing and hedging. Machine learning removes the need for various parametric assumptions about underlying market dynamics by learning directly from data. This research examines the use of machine learning methods to develop a data-driven method of derivatives pricing and dynamic hedging. Nevertheless, machine learning methods like reinforcement learning require an abundance of data to learn. We explore the implementation of a generative adversarial network-based approach to generate realistic market data from past historical data. This data is used to train the reinforcement learning framework and evaluate its robustness. The results demonstrate the efficacy of deep reinforcement learning methods to price derivatives and hedge positions in the proposed systematic GAN-based market simulation framework.
Book Synopsis Hedging Market Exposures by : Oleg V. Bychuk
Download or read book Hedging Market Exposures written by Oleg V. Bychuk and published by John Wiley & Sons. This book was released on 2011-06-28 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identify and understand the risks facing your portfolio, how to quantify them, and the best tools to hedge them This book scrutinizes the various risks confronting a portfolio, equips the reader with the tools necessary to identify and understand these risks, and discusses the best ways to hedge them. The book does not require a specialized mathematical foundation, and so will appeal to both the generalist and specialist alike. For the generalist, who may not have a deep knowledge of mathematics, the book illustrates, through the copious use of examples, how to identify risks that can sometimes be hidden, and provides practical examples of quantifying and hedging exposures. For the specialist, the authors provide a detailed discussion of the mathematical foundations of risk management, and draw on their experience of hedging complex multi-asset class portfolios, providing practical advice and insights. Provides a clear description of the risks faced by managers with equity, fixed income, commodity, credit and foreign exchange exposures Elaborates methods of quantifying these risks Discusses the various tools available for hedging, and how to choose optimal hedging instruments Illuminates hidden risks such as counterparty, operational, human behavior and model risks, and expounds the importance and instability of model assumptions, such as market correlations, and their attendant dangers Explains in clear yet effective terms the language of quantitative finance and enables a non-quantitative investment professional to communicate effectively with professional risk managers, "quants", clients and others Providing thorough coverage of asset modeling, hedging principles, hedging instruments, and practical portfolio management, Hedging Market Exposures helps portfolio managers, bankers, transactors and finance and accounting executives understand the risks their business faces and the ways to quantify and control them.
Download or read book Deep Hedging written by Hans Buehler and published by . This book was released on 2019 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods.We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show in section 4 that the set of constrained trading strategies used by our algorithm is large enough to ∈-approximate any optimal solution.Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available.We illustrate our approach by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard “complete market” solution.This is the "stochastic analysis" version of the paper. A version in machine learning notation is available here "https://ssrn.com/abstract=3355706" https://ssrn.com/abstract=3355706.
Book Synopsis Neutral and Indifference Portfolio Pricing, Hedging and Investing by : Srdjan Stojanovic
Download or read book Neutral and Indifference Portfolio Pricing, Hedging and Investing written by Srdjan Stojanovic and published by Springer Science & Business Media. This book was released on 2011-09-28 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is written for quantitative finance professionals, students, educators, and mathematically inclined individual investors. It is about some of the latest developments in pricing, hedging, and investing in incomplete markets. With regard to pricing, two frameworks are fully elaborated: neutral and indifference pricing. With regard to hedging, the most conservative and relaxed hedging formulas are derived. With regard to investing, the neutral pricing methodology is also considered as a tool for connecting market asset prices with optimal positions in such assets. Srdjan D. Stojanovic is Professor in the Department of Mathematical Sciences at University of Cincinnati (USA) and Professor in the Center for Financial Engineering at Suzhou University (China).
Download or read book Risk Modeling written by Terisa Roberts and published by John Wiley & Sons. This book was released on 2022-09-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
Book Synopsis Pricing, Hedging and Optimally Designing Derivatives via Minimization of Risk Measures by : Pauline M. Barrieu
Download or read book Pricing, Hedging and Optimally Designing Derivatives via Minimization of Risk Measures written by Pauline M. Barrieu and published by . This book was released on 2009 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: The question of pricing and hedging a given contingent claim has a unique solution in a complete market framework. When some incompleteness is introduced, the problem becomes however more difficult. Several approaches have been adopted in the literature to provide a satisfactory answer to this problem, for a particular choice criterion. In this paper, in order to price and hedge a non-tradable contingent claim, we first start with a (standard) utility maximization problem and end up with an equivalent risk measure minimization.This hedging problem can be seen as a particular case of a more general situation of risk transfer between different agents, one of them consisting of the financial market. In order to provide constructive answers to this general optimal risk transfer problem, both static and dynamic approaches are considered. When considering a dynamic framework, our main purpose is to find a trade-off between static and very abstract risk measures as we are more interested in tractability issues and interpretations of the dynamic risk measures we obtain rather than the ultimate general results. Therefore, after introducing a general axiomaticapproach to dynamic risk measures, we relate the dynamic version of convex risk measures to BSDEs.
Book Synopsis Deep Reinforcement Learning Portfolio Management System with Adjustable Risk Preference by :
Download or read book Deep Reinforcement Learning Portfolio Management System with Adjustable Risk Preference written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Book Synopsis Emerging Trends in Smart Societies by : Worakamol Wisetsri
Download or read book Emerging Trends in Smart Societies written by Worakamol Wisetsri and published by Taylor & Francis. This book was released on 2024-06-24 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging Trends in Smart Societies: Interdisciplinary Perspectives” captures the essence of the groundbreaking initiative heralded by the inaugural International Conference on Humanities for Smart Societies 2023 (HMSS 23). This milestone event convenes a global cohort of scholars, policymakers, and thinkers, transcending geographical confines via a pioneering virtual platform. The book crystallizes the convergence of diverse disciplines – from humanities to management – fostering an exchange of innovative ideas vital for sustainable, digitally transformed societies. By orchestrating cross-disciplinary dialogues, this anthology unveils novel solutions and holistic approaches to contemporary challenges.
Book Synopsis Robust Equity Portfolio Management by : Woo Chang Kim
Download or read book Robust Equity Portfolio Management written by Woo Chang Kim and published by John Wiley & Sons. This book was released on 2015-11-30 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.
Book Synopsis Market Risk Analysis, Pricing, Hedging and Trading Financial Instruments by : Carol Alexander
Download or read book Market Risk Analysis, Pricing, Hedging and Trading Financial Instruments written by Carol Alexander and published by Wiley. This book was released on 2008-06-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading market risk academic, Professor Carol Alexander, Pricing, Hedging and Trading Financial Instruments forms part three of the Market Risk Analysis four volume set. This book is an in-depth, practical and accessible guide to the models that are used for pricing and the strategies that are used for hedging financial instruments, and to the markets in which they trade. It provides a comprehensive, rigorous and accessible introduction to bonds, swaps, futures and forwards and options, including variance swaps, volatility indices and their futures and options, to stochastic volatility models and to modelling the implied and local volatility surfaces. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Duration-Convexity approximation to bond portfolios, and portfolio immunization; Pricing floaters and vanilla, basis and variance swaps; Coupon stripping and yield curve fitting; Proxy hedging, and hedging international securities and energy futures portfolios; Pricing models for European exotics, including barriers, Asians, look-backs, choosers, capped, contingent, power, quanto, compo, exchange, ‘best-of’ and spread options; Libor model calibration; Dynamic models for implied volatility based on principal component analysis; Calibration of stochastic volatility models (Matlab code); Simulations from stochastic volatility and jump models; Duration, PV01 and volatility invariant cash flow mappings; Delta-gamma-theta-vega mappings for options portfolios; Volatility beta mapping to volatility indices.
Book Synopsis Portfolio Risk Analysis by : Gregory Connor
Download or read book Portfolio Risk Analysis written by Gregory Connor and published by Princeton University Press. This book was released on 2010-03-15 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio risk forecasting has been and continues to be an active research field for both academics and practitioners. Almost all institutional investment management firms use quantitative models for their portfolio forecasting, and researchers have explored models' econometric foundations, relative performance, and implications for capital market behavior and asset pricing equilibrium. Portfolio Risk Analysis provides an insightful and thorough overview of financial risk modeling, with an emphasis on practical applications, empirical reality, and historical perspective. Beginning with mean-variance analysis and the capital asset pricing model, the authors give a comprehensive and detailed account of factor models, which are the key to successful risk analysis in every economic climate. Topics range from the relative merits of fundamental, statistical, and macroeconomic models, to GARCH and other time series models, to the properties of the VIX volatility index. The book covers both mainstream and alternative asset classes, and includes in-depth treatments of model integration and evaluation. Credit and liquidity risk and the uncertainty of extreme events are examined in an intuitive and rigorous way. An extensive literature review accompanies each topic. The authors complement basic modeling techniques with references to applications, empirical studies, and advanced mathematical texts. This book is essential for financial practitioners, researchers, scholars, and students who want to understand the nature of financial markets or work toward improving them.
Book Synopsis Goals-Based Wealth Management by : Jean L. P. Brunel
Download or read book Goals-Based Wealth Management written by Jean L. P. Brunel and published by John Wiley & Sons. This book was released on 2015-02-20 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a more active role in strategic asset allocation Goals-Based Wealth Management is a manual for protecting and growing client wealth in a way that changes both the services and profitability of the firm. Written by a 35-year veteran of international wealth education and analysis, this informative guide explains a new approach to wealth management that allows individuals to take on a more active role in the allocation of their assets. Coverage includes a detailed examination of the goals-based approach, including what works and what needs to be revisited, and a clear, understandable model that allows advisors to help individuals to navigate complex processes. The companion website offers ancillary readings, practice management checklists, and assessments that help readers secure a deep understanding of the key ideas that make goals-based wealth management work. The goals-based wealth management approach was pioneered in 2002, but has seen a slow evolution and only modest refinements largely due to a lack of wide-scale adoption. This book takes the first steps toward finalizing the approach, by delineating the effective and ineffective aspects of traditional approaches, and proposing changes that could bring better value to practitioners and their clients. Understand the challenges faced by the affluent and wealthy Examine strategic asset allocation and investment policy formulation Learn a model for dealing with the asset allocation process Learn why the structure of the typical advisory firm needs to change High-net-worth individuals face very specific challenges. Goals-Based Wealth Management focuses on how those challenges can be overcome while adhering to their goals, incorporating constraints, and working within the individual's frame of reference to drive strategic allocation of their financial assets.
