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Volatility Model Calibration With Convolutional Neural Networks
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Book Synopsis Machine Learning and Data Sciences for Financial Markets by : Agostino Capponi
Download or read book Machine Learning and Data Sciences for Financial Markets written by Agostino Capponi and published by Cambridge University Press. This book was released on 2023-04-30 with total page 742 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Book Synopsis Machine Learning for Risk Calculations by : Ignacio Ruiz
Download or read book Machine Learning for Risk Calculations written by Ignacio Ruiz and published by John Wiley & Sons. This book was released on 2021-12-20 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner’s View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions. This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You’ll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you’ll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used. Review the fundamentals of deep learning and Chebyshev tensors Discover pioneering algorithmic techniques that can create new opportunities in complex risk calculation Learn how to apply the solutions to a wide range of real-life risk calculations. Download sample code used in the book, so you can follow along and experiment with your own calculations Realize improved risk management whilst overcoming the burden of limited computational power Quants, IT professionals, and financial risk managers will benefit from this practitioner-oriented approach to state-of-the-art risk calculation.
Book Synopsis Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track by : Gianmarco De Francisci Morales
Download or read book Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track written by Gianmarco De Francisci Morales and published by Springer Nature. This book was released on with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book MSEA 2023 written by Gaikar Vilas and published by European Alliance for Innovation. This book was released on 2023-07-21 with total page 859 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2nd International Conference on Mathematical Statistics and Economic Analysis (MSEA 2023) was held virtually from 26-28 May 2023 in Nanjing, China. The conference was attended by researchers, teachers, students and engineers in the field of mathematical statistics and economic analysis. Through data statistics and analysis, we can quickly understand the pattern of economic development. This conference combines mathematical statistics and economic analysis, explores the relationship between the two, and provides a platform for experts and scholars in the fields of mathematical statistics and economic analysis to discuss related issues and exchange ideas. Therefore, we hope to create a forum for sharing research results and exploring future research directions, so that participants can learn about the latest research directions, contents and results of mathematical statistics and economic analysis; secondly, we hope that the conference can provide solutions to the major problems facing mathematical statistics and economic analysis, and create a space that encourages discussion and joint development of research, technological development and innovation.
Book Synopsis ECML PKDD 2018 Workshops by : Carlos Alzate
Download or read book ECML PKDD 2018 Workshops written by Carlos Alzate and published by Springer. This book was released on 2019-02-06 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from two workshops held at the 18th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Ireland, in September 2018, namely: MIDAS 2018 – Third Workshop on Mining Data for Financial Applications and PAP 2018 – Second International Workshop on Personal Analytics and Privacy. The 12 papers presented in this volume were carefully reviewed and selected from a total of 17 submissions.
Book Synopsis Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes by : Cornelis W Oosterlee
Download or read book Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes written by Cornelis W Oosterlee and published by World Scientific. This book was released on 2019-10-29 with total page 1310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry.
Book Synopsis Stochastic Calculus for Fractional Brownian Motion and Applications by : Francesca Biagini
Download or read book Stochastic Calculus for Fractional Brownian Motion and Applications written by Francesca Biagini and published by Springer Science & Business Media. This book was released on 2008-02-17 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to present a comprehensive account of the different definitions of stochastic integration for fBm, and to give applications of the resulting theory. Particular emphasis is placed on studying the relations between the different approaches. Readers are assumed to be familiar with probability theory and stochastic analysis, although the mathematical techniques used in the book are thoroughly exposed and some of the necessary prerequisites, such as classical white noise theory and fractional calculus, are recalled in the appendices. This book will be a valuable reference for graduate students and researchers in mathematics, biology, meteorology, physics, engineering and finance.
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.
Download or read book BDEDM 2023 written by Misra Anuranjan and published by European Alliance for Innovation. This book was released on 2023-06-13 with total page 1138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the 2nd International Conference on Big Data Economy and Digital Management (BDEDM 2023) supported by University Malaysia Sabah, Malaysia, held on 6th–8th January 2023 in Changsha, China (virtual conference). The immediate purpose of this Conference was to bring together experienced as well as young scientists who are interested in working actively on various aspects of Big Data Economy and Digital Management. The keynote speeches addressed major theoretical issues, current and forthcoming observational data as well as upcoming ideas in both theoretical and observational sectors. Keeping in mind the “academic exchange first” approach, the lectures were arranged in such a way that the young researchers had ample scope to interact with the stalwarts who are internationally leading experts in their respective fields of research. The major topics covered in the Conference are: Big Data in Enterprise Performance Management, Enterprise Management Modernization, Intelligent Management System, Performance Evaluation and Modeling Applications, Enterprise Technology Innovation, etc.
Book Synopsis Large Deviations and Asymptotic Methods in Finance by : Peter K. Friz
Download or read book Large Deviations and Asymptotic Methods in Finance written by Peter K. Friz and published by Springer. This book was released on 2015-06-16 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topics covered in this volume (large deviations, differential geometry, asymptotic expansions, central limit theorems) give a full picture of the current advances in the application of asymptotic methods in mathematical finance, and thereby provide rigorous solutions to important mathematical and financial issues, such as implied volatility asymptotics, local volatility extrapolation, systemic risk and volatility estimation. This volume gathers together ground-breaking results in this field by some of its leading experts. Over the past decade, asymptotic methods have played an increasingly important role in the study of the behaviour of (financial) models. These methods provide a useful alternative to numerical methods in settings where the latter may lose accuracy (in extremes such as small and large strikes, and small maturities), and lead to a clearer understanding of the behaviour of models, and of the influence of parameters on this behaviour. Graduate students, researchers and practitioners will find this book very useful, and the diversity of topics will appeal to people from mathematical finance, probability theory and differential geometry.
Book Synopsis Algorithms for Minimization Without Derivatives by : Richard P. Brent
Download or read book Algorithms for Minimization Without Derivatives written by Richard P. Brent and published by Courier Corporation. This book was released on 2013-06-10 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: DIVOutstanding text for graduate students and research workers proposes improvements to existing algorithms, extends their related mathematical theories, and offers details on new algorithms for approximating local and global minima. /div
Book Synopsis Novel Methods in Computational Finance by : Matthias Ehrhardt
Download or read book Novel Methods in Computational Finance written by Matthias Ehrhardt and published by Springer. This book was released on 2017-09-19 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the state-of-the-art and open problems in computational finance. It presents a collection of research outcomes and reviews of the work from the STRIKE project, an FP7 Marie Curie Initial Training Network (ITN) project in which academic partners trained early-stage researchers in close cooperation with a broader range of associated partners, including from the private sector. The aim of the project was to arrive at a deeper understanding of complex (mostly nonlinear) financial models and to develop effective and robust numerical schemes for solving linear and nonlinear problems arising from the mathematical theory of pricing financial derivatives and related financial products. This was accomplished by means of financial modelling, mathematical analysis and numerical simulations, optimal control techniques and validation of models. In recent years the computational complexity of mathematical models employed in financial mathematics has witnessed tremendous growth. Advanced numerical techniques are now essential to the majority of present-day applications in the financial industry. Special attention is devoted to a uniform methodology for both testing the latest achievements and simultaneously educating young PhD students. Most of the mathematical codes are linked into a novel computational finance toolbox, which is provided in MATLAB and PYTHON with an open access license. The book offers a valuable guide for researchers in computational finance and related areas, e.g. energy markets, with an interest in industrial mathematics.
Book Synopsis High Frequency Trading and Limit Order Book Dynamics by : Ingmar Nolte
Download or read book High Frequency Trading and Limit Order Book Dynamics written by Ingmar Nolte and published by Routledge. This book was released on 2016-04-14 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the latest research in the areas of market microstructure and high-frequency finance along with new econometric methods to address critical practical issues in these areas of research. Thirteen chapters, each of which makes a valuable and significant contribution to the existing literature have been brought together, spanning a wide range of topics including information asymmetry and the information content in limit order books, high-frequency return distribution models, multivariate volatility forecasting, analysis of individual trading behaviour, the analysis of liquidity, price discovery across markets, market microstructure models and the information content of order flow. These issues are central both to the rapidly expanding practice of high frequency trading in financial markets and to the further development of the academic literature in this area. The volume will therefore be of immediate interest to practitioners and academics. This book was originally published as a special issue of European Journal of Finance.
Book Synopsis Adaptive Learning of Polynomial Networks by : Nikolay Nikolaev
Download or read book Adaptive Learning of Polynomial Networks written by Nikolay Nikolaev and published by Springer Science & Business Media. This book was released on 2006-08-18 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction.
Book Synopsis Computational Methods for Option Pricing by : Yves Achdou
Download or read book Computational Methods for Option Pricing written by Yves Achdou and published by SIAM. This book was released on 2005-07-18 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book allows you to understand fully the modern tools of numerical analysis in finance.
Book Synopsis Long Memory in Economics by : Gilles Teyssière
Download or read book Long Memory in Economics written by Gilles Teyssière and published by Springer Science & Business Media. This book was released on 2006-09-22 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics.
Book Synopsis Stochastic Volatility Modeling by : Lorenzo Bergomi
Download or read book Stochastic Volatility Modeling written by Lorenzo Bergomi and published by CRC Press. This book was released on 2015-12-16 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Packed with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives, including:Which trading issues do we tackle with stochastic volatility? How do we design models and assess their relevance? How do we tell which models are usable and when does c
