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Forecasting Implied Volatility Smile Surface Via Deep Learning And Attention Mechanism
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Book Synopsis Forecasting Implied Volatility Smile Surface Via Deep Learning and Attention Mechanism by : Shengli Chen
Download or read book Forecasting Implied Volatility Smile Surface Via Deep Learning and Attention Mechanism written by Shengli Chen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The implied volatility smile surface is the basis of option pricing, and the dynamic evolution of the option volatility smile surface is difficult to predict. In this paper, attention mechanism is introduced into LSTM, and a volatility surface prediction method combining deep learning and attention mechanism is pioneeringly established. LSTM's forgetting gate makes it have strong generalization ability, and its feedback structure enables it to characterize the long memory of financial volatility. The application of attention mechanism in LSTM networks can significantly enhance the ability of LSTM networks to select input features. This paper considers the discrete points of the implied volatility smile surface as an overall prediction target, extracts the daily, weekly, and monthly option implied volatility as input features and establishes a set of LSTM-Attention deep learning systems. Using the dropout mechanism in training reduces the risk of over-fitting. For the prediction results, we use arbitrage-free smoothing to form the final implied volatility smile surface. This article uses the S&P 500 option market to conduct an empirical study. The research shows that the error curve of the LSTM-attention prediction system converges, and the prediction of the implied volatility surface is more accurate than other predicting system. According to the implied volatility surface of the 3-year rolling forecast, the BS formula is used to pricing the option contract, and then a time spread strategy and a butterfly spread strategy are constructed respectively. The experimental results show that the two strategies constructed using the predicted implied volatility surfaces have higher returns and sharp ratios than that the volatility surfaces are not predicted. This paper confirms that the use of AI to predict the implied volatility surface has theoretical and economic value. The research method provides a new reference for option pricing and strategy.
Book Synopsis Multi-step Forecast of the Implied Volatility Surface Using Deep Learning by : Nikita Medvedev
Download or read book Multi-step Forecast of the Implied Volatility Surface Using Deep Learning written by Nikita Medvedev and published by . This book was released on 2019 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Learning Volatility by : Blanka Horvath
Download or read book Deep Learning Volatility written by Blanka Horvath and published by . This book was released on 2019 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a consistent neural network based calibration method for a number of volatility models-including the rough volatility family-that performs the calibration task within a few milliseconds for the full implied volatility surface.The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data.
Book Synopsis Forecasting Implied Volatility Surfaces by : Francesco Audrino
Download or read book Forecasting Implied Volatility Surfaces written by Francesco Audrino and published by . This book was released on 2007 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Option-Implied Risk-Neutral Distributions and Risk Aversion by : Jens Carsten Jackwerth
Download or read book Option-Implied Risk-Neutral Distributions and Risk Aversion written by Jens Carsten Jackwerth and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Analytically Tractable Stochastic Stock Price Models by : Archil Gulisashvili
Download or read book Analytically Tractable Stochastic Stock Price Models written by Archil Gulisashvili and published by Springer. This book was released on 2012-09-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Asymptotic analysis of stochastic stock price models is the central topic of the present volume. Special examples of such models are stochastic volatility models, that have been developed as an answer to certain imperfections in a celebrated Black-Scholes model of option pricing. In a stock price model with stochastic volatility, the random behavior of the volatility is described by a stochastic process. For instance, in the Hull-White model the volatility process is a geometric Brownian motion, the Stein-Stein model uses an Ornstein-Uhlenbeck process as the stochastic volatility, and in the Heston model a Cox-Ingersoll-Ross process governs the behavior of the volatility. One of the author's main goals is to provide sharp asymptotic formulas with error estimates for distribution densities of stock prices, option pricing functions, and implied volatilities in various stochastic volatility models. The author also establishes sharp asymptotic formulas for the implied volatility at extreme strikes in general stochastic stock price models. The present volume is addressed to researchers and graduate students working in the area of financial mathematics, analysis, or probability theory. The reader is expected to be familiar with elements of classical analysis, stochastic analysis and probability theory.
Book Synopsis Commodity Price Dynamics by : Craig Pirrong
Download or read book Commodity Price Dynamics written by Craig Pirrong and published by Cambridge University Press. This book was released on 2011-10-31 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Commodities have become an important component of many investors' portfolios and the focus of much political controversy over the past decade. This book utilizes structural models to provide a better understanding of how commodities' prices behave and what drives them. It exploits differences across commodities and examines a variety of predictions of the models to identify where they work and where they fail. The findings of the analysis are useful to scholars, traders and policy makers who want to better understand often puzzling - and extreme - movements in the prices of commodities from aluminium to oil to soybeans to zinc.
Book Synopsis How I Became a Quant by : Richard R. Lindsey
Download or read book How I Became a Quant written by Richard R. Lindsey and published by John Wiley & Sons. This book was released on 2011-01-11 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for How I Became a Quant "Led by two top-notch quants, Richard R. Lindsey and Barry Schachter, How I Became a Quant details the quirky world of quantitative analysis through stories told by some of today's most successful quants. For anyone who might have thought otherwise, there are engaging personalities behind all that number crunching!" --Ira Kawaller, Kawaller & Co. and the Kawaller Fund "A fun and fascinating read. This book tells the story of how academics, physicists, mathematicians, and other scientists became professional investors managing billions." --David A. Krell, President and CEO, International Securities Exchange "How I Became a Quant should be must reading for all students with a quantitative aptitude. It provides fascinating examples of the dynamic career opportunities potentially open to anyone with the skills and passion for quantitative analysis." --Roy D. Henriksson, Chief Investment Officer, Advanced Portfolio Management "Quants"--those who design and implement mathematical models for the pricing of derivatives, assessment of risk, or prediction of market movements--are the backbone of today's investment industry. As the greater volatility of current financial markets has driven investors to seek shelter from increasing uncertainty, the quant revolution has given people the opportunity to avoid unwanted financial risk by literally trading it away, or more specifically, paying someone else to take on the unwanted risk. How I Became a Quant reveals the faces behind the quant revolution, offering you?the?chance to learn firsthand what it's like to be a?quant today. In this fascinating collection of Wall Street war stories, more than two dozen quants detail their roots, roles, and contributions, explaining what they do and how they do it, as well as outlining the sometimes unexpected paths they have followed from the halls of academia to the front lines of an investment revolution.
Book Synopsis The Democratization of Artificial Intelligence by : Andreas Sudmann
Download or read book The Democratization of Artificial Intelligence written by Andreas Sudmann and published by transcript Verlag. This book was released on 2019-10-31 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?
Book Synopsis Financial Signal Processing and Machine Learning by : Ali N. Akansu
Download or read book Financial Signal Processing and Machine Learning written by Ali N. Akansu and published by John Wiley & Sons. This book was released on 2016-04-21 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.
Book Synopsis Lecturing Birds on Flying by : Pablo Triana
Download or read book Lecturing Birds on Flying written by Pablo Triana and published by John Wiley & Sons. This book was released on 2009-06-09 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: LECTURING BIRDS ON FLYING For the past few decades, the financial world has often displayed an unreasonable willingness to believe that "the model is right, the market is wrong," in spite of the fact that these theoretical machinations were largely responsible for the stock market crash of 1987, the LTCM crisis of 1998, the credit crisis of 2008, and many other blow-ups, large and small. Why have both financial insiders (traders, risk managers, executives) and outsiders (academics, journalists, regulators, the public) consistently demonstrated a willingness to treat quantifications as gospel? Nassim Taleb first addressed the conflicts between theoretical and real finance in his technical treatise on options, Dynamic Hedging. Now, in Lecturing Birds on Flying, Pablo Triana offers a powerful indictment on the trustworthiness of financial theory, explaining—in jargon-free plain English—how malfunctions in these quantitative machines have wreaked havoc in our real world. Triana first analyzes the fundamental question of whether financial markets can in principle really be solved mathematically. He shows that the markets indeed cannot be tamed with equations, presenting a long and powerful list of obstacles to prove his point: maverick unlawful human actions rule the markets, unexpected and unimaginable events shape the markets, and historical data is not necessarily a trustworthy guide to the future of the markets. The author then examines the sources of origin of many prevalent theories and mathematical dictums. He details how the field of financial economics evolved from a descriptive discipline to an abstract one dedicated to technically concocting professors' own versions of how such a world should work. He goes on to explain how Wall Street and other financial centers became eager employers of scientists, and how scientists became eager employees of financial firms. Triana concludes with an in-depth discussion of the most significant historical episodes of theory-caused real-life market malaise, with a strong emphasis on the current credit crisis. In the end, Lecturing Birds on Flying calls for the radical substitution of good old-fashioned common sense in place of mathematical decision-making and the restoration to financial power of those who are completely unchained to the iron ball of classroom-obtained qualifications.
Book Synopsis FX Options and Structured Products by : Uwe Wystup
Download or read book FX Options and Structured Products written by Uwe Wystup and published by John Wiley & Sons. This book was released on 2017-06-30 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Guidance to Excelling in the FX Market Once you have a textbook understanding of money market and foreign exchange products, turn to FX Options and Structured Products, Second Edition, for the beyond-vanilla options strategies and traded deals proven superior in today’s post-credit crisis trading environment. With the thoroughness and balance of theory and practice only Uwe Wystup can deliver, this fully revised edition offers authoritative solutions for the real world in an easy-to-access format. See how specific products actually work through detailed case studies featuring clear examples of FX options, common structures and custom solutions. This complete resource is both a wellspring of ideas and a hands-on guide to structuring and executing your own strategies. Distinguish yourself with a valued skillset by: Working through practical and thought-provoking challenges in more than six dozen exercises, all with complete solutions in a companion volume Gaining a working knowledge of the latest, most popular products, including accumulators, kikos, target forwards and more Getting close to the everyday realities of the FX derivatives market through new, illuminating case studies for corporates, municipalities and private banking FX Options and Structured Products, Second Edition is your go-to road map to the exotic options in FX derivatives.
Book Synopsis Applied Corporate Finance by : Aswath Damodaran
Download or read book Applied Corporate Finance written by Aswath Damodaran and published by John Wiley & Sons. This book was released on 2014-10-27 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aswath Damodaran, distinguished author, Professor of Finance, and David Margolis, Teaching Fellow at the NYU Stern School of Business, has delivered the newest edition of Applied Corporate Finance. This readable text provides the practical advice students and practitioners need rather than a sole concentration on debate theory, assumptions, or models. Like no other text of its kind, Applied Corporate Finance, 4th Edition applies corporate finance to real companies. It now contains six real-world core companies to study and follow. Business decisions are classified for students into three groups: investment, financing, and dividend decisions.
Book Synopsis An Engine, Not a Camera by : Donald MacKenzie
Download or read book An Engine, Not a Camera written by Donald MacKenzie and published by MIT Press. This book was released on 2008-08-29 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: In An Engine, Not a Camera, Donald MacKenzie argues that the emergence of modern economic theories of finance affected financial markets in fundamental ways. These new, Nobel Prize-winning theories, based on elegant mathematical models of markets, were not simply external analyses but intrinsic parts of economic processes. Paraphrasing Milton Friedman, MacKenzie says that economic models are an engine of inquiry rather than a camera to reproduce empirical facts. More than that, the emergence of an authoritative theory of financial markets altered those markets fundamentally. For example, in 1970, there was almost no trading in financial derivatives such as "futures." By June of 2004, derivatives contracts totaling $273 trillion were outstanding worldwide. MacKenzie suggests that this growth could never have happened without the development of theories that gave derivatives legitimacy and explained their complexities. MacKenzie examines the role played by finance theory in the two most serious crises to hit the world's financial markets in recent years: the stock market crash of 1987 and the market turmoil that engulfed the hedge fund Long-Term Capital Management in 1998. He also looks at finance theory that is somewhat beyond the mainstream—chaos theorist Benoit Mandelbrot's model of "wild" randomness. MacKenzie's pioneering work in the social studies of finance will interest anyone who wants to understand how America's financial markets have grown into their current form.
Book Synopsis Financial Risk Forecasting by : Jon Danielsson
Download or read book Financial Risk Forecasting written by Jon Danielsson and published by John Wiley & Sons. This book was released on 2011-04-20 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
Book Synopsis Logistics Management and Strategy by : Alan Harrison
Download or read book Logistics Management and Strategy written by Alan Harrison and published by Pearson UK. This book was released on 2019 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning in Finance by : Matthew F. Dixon
Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
