Equa Risk Pricing Hedging And Portfolio Management Using Dynamic Risk Measures And Deep Reinforcement Learning Methods
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Pricing and Hedging Financial Derivatives with Reinforcement Learning Methods
| Author | : Alexandre Carbonneau |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
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.
Risk and Portfolio Analysis
| Author | : Henrik Hult |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 343 |
| Release | : 2012-07-20 |
| Genre | : Mathematics |
| ISBN | : 146144103X |
Investment and risk management problems are fundamental problems for financial institutions and involve both speculative and hedging decisions. A structured approach to these problems naturally leads one to the field of applied mathematics in order to translate subjective probability beliefs and attitudes towards risk and reward into actual decisions. In Risk and Portfolio Analysis the authors present sound principles and useful methods for making investment and risk management decisions in the presence of hedgeable and non-hedgeable risks using the simplest possible principles, methods, and models that still capture the essential features of the real-world problems. They use rigorous, yet elementary mathematics, avoiding technically advanced approaches which have no clear methodological purpose and are practically irrelevant. The material progresses systematically and topics such as the pricing and hedging of derivative contracts, investment and hedging principles from portfolio theory, and risk measurement and multivariate models from risk management are covered appropriately. The theory is combined with numerous real-world examples that illustrate how the principles, methods, and models can be combined to approach concrete problems and to draw useful conclusions. Exercises are included at the end of the chapters to help reinforce the text and provide insight. This book will serve advanced undergraduate and graduate students, and practitioners in insurance, finance as well as regulators. Prerequisites include undergraduate level courses in linear algebra, analysis, statistics and probability.
Hedging Market Exposures
| Author | : Oleg V. Bychuk |
| Publisher | : John Wiley & Sons |
| Total Pages | : 322 |
| Release | : 2011-06-28 |
| Genre | : Business & Economics |
| ISBN | : 111808537X |
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.
Empirical Asset Pricing
| Author | : Wayne Ferson |
| Publisher | : MIT Press |
| Total Pages | : 497 |
| Release | : 2019-03-12 |
| Genre | : Business & Economics |
| ISBN | : 0262039370 |
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.
Deep Hedging
| Author | : Hans Buehler |
| Publisher | : |
| Total Pages | : 32 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
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.
Neutral and Indifference Portfolio Pricing, Hedging and Investing
| Author | : Srdjan Stojanovic |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 274 |
| Release | : 2011-08-30 |
| Genre | : Mathematics |
| ISBN | : 0387714189 |
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).
Alternative Data and Artificial Intelligence Techniques
| Author | : Qingquan Tony Zhang |
| Publisher | : Springer Nature |
| Total Pages | : 340 |
| Release | : 2022-10-31 |
| Genre | : Business & Economics |
| ISBN | : 3031116127 |
This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.
Risk Modeling
| Author | : Terisa Roberts |
| Publisher | : John Wiley & Sons |
| Total Pages | : 214 |
| Release | : 2022-09-20 |
| Genre | : Business & Economics |
| ISBN | : 111982494X |
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.
