Volatility Forecasting with the Multifractal Model of Asset Returns PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Volatility Forecasting with the Multifractal Model of Asset Returns PDF full book. Access full book title Volatility Forecasting with the Multifractal Model of Asset Returns by Terrence Y. Zhang. Download full books in PDF and EPUB format.

Volatility Forecasting with the Multifractal Model of Asset Returns

Volatility Forecasting with the Multifractal Model of Asset Returns PDF Author: Terrence Y. Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 24

Book Description
This paper presents an empirical application of the Multifractal Model of Asset Returns (MMAR) to intraday stock prices, with a goal of generating accurate volatility forecasts. Intraday stock volatility exhibits long tails, persistence, and strong evidence of moment scaling. This allows us to apply the MMAR. A forecasting method for the MMAR is implemented through Monte Carlo simulation, and this forecasting method is compared to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) alternatives over several testing samples. The MMAR significantly outperformed the GARCH models. This suggests that the framework of multifractality has a large potential for further development and application within finance.

Volatility Forecasting with the Multifractal Model of Asset Returns

Volatility Forecasting with the Multifractal Model of Asset Returns PDF Author: Terrence Y. Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 24

Book Description
This paper presents an empirical application of the Multifractal Model of Asset Returns (MMAR) to intraday stock prices, with a goal of generating accurate volatility forecasts. Intraday stock volatility exhibits long tails, persistence, and strong evidence of moment scaling. This allows us to apply the MMAR. A forecasting method for the MMAR is implemented through Monte Carlo simulation, and this forecasting method is compared to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) alternatives over several testing samples. The MMAR significantly outperformed the GARCH models. This suggests that the framework of multifractality has a large potential for further development and application within finance.

Multifractal Volatility

Multifractal Volatility PDF Author: Laurent E. Calvet
Publisher: Academic Press
ISBN: 0080559964
Category : Business & Economics
Languages : en
Pages : 273

Book Description
Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and mathematics and provides a unified treatment of the use of multifractal techniques in finance. A large existing literature (e.g., Engle, 1982; Rossi, 1995) models volatility as an average of past shocks, possibly with a noise component. This approach often has difficulty capturing sharp discontinuities and large changes in financial volatility. Their research has shown the advantages of modelling volatility as subject to abrupt regime changes of heterogeneous durations. Using the intuition that some economic phenomena are long-lasting while others are more transient, they permit regimes to have varying degrees of persistence. By drawing on insights from the use of multifractals in the natural sciences and mathematics, they show how to construct high-dimensional regime-switching models that are easy to estimate, and substantially outperform some of the best traditional forecasting models such as GARCH. The goal of Multifractal Volatility is to popularize the approach by presenting these exciting new developments to a wider audience. They emphasize both theoretical and empirical applications, beginning with a style that is easily accessible and intuitive in early chapters, and extending to the most rigorous continuous-time and equilibrium pricing formulations in final chapters. Presents a powerful new technique for forecasting volatility Leads the reader intuitively from existing volatility techniques to the frontier of research in this field by top scholars at major universities The first comprehensive book on multifractal techniques in finance, a cutting-edge field of research

The Multi-fractal Model of Asset Returns

The Multi-fractal Model of Asset Returns PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

Book Description


Forecasting Volatility in the Financial Markets

Forecasting Volatility in the Financial Markets PDF Author: John L. Knight
Publisher: Butterworth-Heinemann
ISBN: 9780750655156
Category : Business & Economics
Languages : en
Pages : 428

Book Description
This text assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modeling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.

The Markov Switching Multi-fractal Model of Asset Returns

The Markov Switching Multi-fractal Model of Asset Returns PDF Author: Hwa Taek Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 175

Book Description


The Markov Switching Multi-fractal Model of Asset Returns

The Markov Switching Multi-fractal Model of Asset Returns PDF Author: Hwa Taek Lee
Publisher:
ISBN:
Category :
Languages : en
Pages :

Book Description


The Oxford Handbook of Computational Economics and Finance

The Oxford Handbook of Computational Economics and Finance PDF Author: Shu-Heng Chen
Publisher: Oxford University Press
ISBN: 0190877502
Category : Business & Economics
Languages : en
Pages : 785

Book Description
The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.

A Multifractal Model of Assets Returns

A Multifractal Model of Assets Returns PDF Author: Laurent E. Calvet
Publisher:
ISBN:
Category :
Languages : en
Pages : 56

Book Description
This paper investigates the Multifractal Model of Asset Returns, a continuous-time process that incorporates the thick tails and volatility persistence exhibited by many financial time series. The model is constructed by compounding a Brownian Motion with a multifractal time-deformation process. Return moments scale as a power law of the time horizon, a property confirmed for Deutschemark / U.S. Dollar exchange rates and several equity series. The model implies semi-martingale prices and thus precludes arbitrage in a standard two-asset economy. Volatility has long-memory, and the highest finite moment of returns can have any value greater than two. The local variability of the process is characterized by a renormalized probability density of local Houml;lder exponents. Unlike standard models, multifractal paths contain a multiplicity of these exponents within any time interval. We develop an estimation method, and infer a parsimonious generating mechanism for the exchange rate series. Simulated samples replicate the moment-scaling found in the data.

The (Mis)Behaviour of Markets

The (Mis)Behaviour of Markets PDF Author: Benoit B. Mandelbrot
Publisher: Profile Books
ISBN: 1847651550
Category : Business & Economics
Languages : en
Pages : 352

Book Description
This international bestseller, which foreshadowed a market crash, explains why it could happen again if we don't act now. Fractal geometry is the mathematics of roughness: how to reduce the outline of a jagged leaf or static in a computer connection to a few simple mathematical properties. With his fractal tools, Mandelbrot has got to the bottom of how financial markets really work. He finds they have a shifting sense of time and wild behaviour that makes them volatile, dangerous - and beautiful. In his models, the complex gyrations of the FTSE 100 and exchange rates can be reduced to straightforward formulae that yield a much more accurate description of the risks involved.

A Multifractal Model of Asset Returns

A Multifractal Model of Asset Returns PDF Author: Benoit B. Mandelbrot
Publisher:
ISBN:
Category :
Languages : en
Pages : 33

Book Description
This paper presents the quot;multifractal model of asset returnsquot; (quot;MMARquot;), based upon the pioneering research into multifractal measures by Mandelbrot (1972, 1974). The multifractal model incorporates two elements of Mandelbrot's past research that are now well known in finance. First, the MMAR contains long-tails, as in Mandelbrot (1963), which focused on Levy-stable distributions. In contrast to Mandelbrot (1963), this model does not necessarily imply infinite variance. Second, the model contains long-dependence, the characteristic feature of fractional Brownian Motion (FBM), introduced by Mandelbrot and van Ness (1968). In contrast to FBM, the multifractal model displays long dependence in the absolute value of price increments, while price increments themselves can be uncorrelated. As such, the MMAR is an alternative to ARCH-type representations that have been the focus of empirical research on the distribution of prices for the past fifteen years. The distinguishing feature of the multifractal model is multiscaling of the return distribution's moments under time-rescalings. We define multiscaling, show how to generate processes with this property, and discuss how these processes differ from the standard processes of continuous-time finance. The multifractal model implies certain empirical regularities, which are investigated in a companion paper.