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Multifractal Financial Markets ​explores appropriate models for estimating risk and profiting from market swings, allowing readers to develop enhanced portfolio management skills and strategies. Fractals in finance allow us to understand market instability and persistence. When applied to financial markets, these models produce the requisite amount of data necessary for gauging market risk in order to mitigate loss. This brief delves deep into the multifractal market approach to portfolio management through real-world examples and case studies, providing readers with the tools they need to forecast profound shifts in market activity.
Forecasting volatility is one of the major challenges in the field of finance. Calvet and Fisher present a powerful, new technique for volatility modelling. Their preliminary work has been well-received in the top academic journals and this is the first time they present their research in a comprehensive way.
This book collects high-quality papers on the latest fundamental advances in the state of Econophysics and Management Science, providing insights that address problems concerning the international economy, social development and economic security. This book applies the multi-fractal detrended class method, and improves the method with different filters. The authors apply those methods to a variety of areas: financial markets, energy markets, gold market and so on. This book is arguably a systematic research and summary of various kinds of multi-fractal detrended methods. Furthermore, it puts forward some investment suggestions on a healthy development of financial markets.
From the world-famous inventor of fractal geometry, a revolutionary new theory that turns on its head our understanding of how markets work. Fractal geometry is the mathematics of roughness: how to reduce the outline of a jagged leaf, a rocky coastline or static in a computer connection to a few simple mathematical properties - to make the complex simple. With his fractal tools, Benoit Mandelbrot has got to the bottom of how financial markets really work. He finds they have a shifting sense of time, a unique dimension and a wild kind of behaviour that makes them volatile, dangerous - and also beautiful. In Mandelbrot's fractal models, the complex gyrations of IBM's stock price, the FTSE 100, cotton trading and exchange rates can be reduced to straightforward formulae that yield a much more accurate description of the risks involved.
Benoît Mandelbrot, the father of Fractal Geometry, developed a multifractal model for describing price changes. Despite the commonly used models, such as the Brownian motion, the Mutifractal Model of Asset Return (MMAR) takes into account scale-consistency, long-range dependence and heavy tails, thus having a great flexibility in depicting the real-market peculiarities. In section 2 a review of the mathematics involved into multifractals is presented; Section 3 is addresses to the extension of multifractality towards stochastic processes, introducing the crucial concept of local Holder exponent of a function. Finally, Section 4 deeply analyze the mathematical properties of the scaling function which drives the "wildeness'' of the process. The proof of Theorem 4.4 is unpublished and the generalization of a Mandelbrot's result, which highlights a possible alternative motivation for the presence of heavy tails and a connection with the Extreme Value Theory. Section 5 is devoted to the analysis of the connection between the scaling function, Multifractal Formalism and Large Deviation Theory, suggesting possible ways in order to estimate the quantities involved. Finally in Section 6 the MMAR is presented, listing all the theorems that make it a suitable model for financial modelling.
This highly praised introductory treatment describes the parallels between statistical physics and finance - both those established in the 100-year long interaction between these disciplines, as well as new research results on financial markets. The random-walk technique, well known in physics, is also the basic model in finance, upon which are built, for example, the Black-Scholes theory of option pricing and hedging, plus methods of portfolio optimization. Here the underlying assumptions are assessed critically. Using empirical financial data and analogies to physical models such as fluid flows, turbulence, or superdiffusion, the book develops a more accurate description of financial markets based on random walks. With this approach, novel methods for derivative pricing and risk management can be formulated. Computer simulations of interacting-agent models provide insight into the mechanisms underlying unconventional price dynamics. It is shown that stock exchange crashes can be modelled in ways analogous to phase transitions and earthquakes, and sometimes have even been predicted successfully. This third edition of The Statistical Mechanics of Financial Markets especially stands apart from other treatments because it offers new chapters containing a practitioner's treatment of two important current topics in banking: the basic notions and tools of risk management and capital requirements for financial institutions, including an overview of the new Basel II capital framework which may well set the risk management standards in scores of countries for years to come.
This book covers the latest theories and empirical findings of financial risk, its measurement and management, and its applications in the world of finance.
A leading pioneer in the field offers practical applications of this innovative science. Peters describes complex concepts in an easy-to-follow manner for the non-mathematician. He uses fractals, rescaled range analysis and nonlinear dynamical models to explain behavior and understand price movements. These are specific tools employed by chaos scientists to map and measure physical and now, economic phenomena.
A visual guide to market trading using intermarket analysis and exchange-traded funds With global markets and asset classes growing even more interconnected, intermarket analysis—the analysis of related asset classes or financial markets to determine their strengths and weaknesses—has become an essential part of any trader's due diligence. In Trading with Intermarket Analysis, John J. Murphy, former technical analyst for CNBC, lays out the technical and intermarket tools needed to understand global markets and illustrates how they help traders profit in volatile climates using exchange-traded funds. Armed with a knowledge of how economic forces impact various markets and financial sectors, investors and traders can profit by exploiting opportunities in markets about to rise and avoiding those poised to fall. Trading with Intermarket Analysis provides advice on trend following, chart patterns, moving averages, oscillators, spotting tops and bottoms, using exchange-traded funds, tracking market sectors, and the new world of intermarket relationships, all presented in a highly visual way. Gives readers a visually rich introduction to the world of intermarket analysis, the ultimate tool for beating the markets Provides practical advice on trend following, chart patterns, moving averages, oscillators, spotting tops and bottoms, using exchange-traded funds, tracking market sectors, and intermarket relationships Includes appendices on Japanese candlesticks and point-and-figure charting Comprehensive and easy-to-use, Trading with Intermarket Analysis presents the most important concepts related to using exchange-traded funds to beat the markets in a visually accessible format.
Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.