Clustering of Volatility as a Multiscale Phenomenon
Clustering of Volatility as a Multiscale Phenomenon

Author: Michele Pasquini

Publisher:

Published: 1999

Total Pages: 9

ISBN-13:

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The dynamics of prices in financial markets has been studied intensively both experimentally (data analysis) and theoretically (models). Nevertheless, a complete stochastic characterization of volatility is still lacking. What it is well known is that absolute returns have memory on a long time range, this phenomenon is known as clustering of volatility. In this paper we show that volatility correlations are power-laws with a non-unique scaling exponent. This kind of multiscale phenomenology, which is well known to physicists since it is relevant in fully developed turbulence and in disordered systems, is now pointed out for financial series. Starting from historical returns series, we have also derived the volatility distribution, and the results are in agreement with a log-normal shape. In our study we consider the New York Stock Exchange (NYSE) daily composite index closes (January 1966 to June 1998) and the US Dollar/Deutsch Mark (USD-DM) noon buying rates certified by the Federal Reserve Bank of New York (October 1989 to September 1998).

The Oxford Handbook of Computational Economics and Finance
The Oxford Handbook of Computational Economics and Finance

Author: Shu-Heng Chen

Publisher: Oxford University Press

Published: 2018

Total Pages: 785

ISBN-13: 0199844372

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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.

Financial Economics and Econometrics
Financial Economics and Econometrics

Author: Nikiforos T. Laopodis

Publisher: Taylor & Francis

Published: 2021-12-14

Total Pages: 767

ISBN-13: 1000506053

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Financial Economics and Econometrics provides an overview of the core topics in theoretical and empirical finance, with an emphasis on applications and interpreting results. Structured in five parts, the book covers financial data and univariate models; asset returns; interest rates, yields and spreads; volatility and correlation; and corporate finance and policy. Each chapter begins with a theory in financial economics, followed by econometric methodologies which have been used to explore the theory. Next, the chapter presents empirical evidence and discusses seminal papers on the topic. Boxes offer insights on how an idea can be applied to other disciplines such as management, marketing and medicine, showing the relevance of the material beyond finance. Readers are supported with plenty of worked examples and intuitive explanations throughout the book, while key takeaways, ‘test your knowledge’ and ‘test your intuition’ features at the end of each chapter also aid student learning. Digital supplements including PowerPoint slides, computer codes supplements, an Instructor’s Manual and Solutions Manual are available for instructors. This textbook is suitable for upper-level undergraduate and graduate courses on financial economics, financial econometrics, empirical finance and related quantitative areas.

Control Performance Assessment: Theoretical Analyses and Industrial Practice
Control Performance Assessment: Theoretical Analyses and Industrial Practice

Author: Paweł D. Domański

Publisher: Springer Nature

Published: 2019-09-01

Total Pages: 367

ISBN-13: 3030235939

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This book presents a comprehensive review of currently available Control Performance Assessment methods. It covers a broad range of classical and modern methods, with a main focus on assessment practice, and is intended to help practitioners learn and properly perform control assessment in the industrial reality. Further, it offers an educational guide for control engineers, who are currently in high demand in the industry. The book consists of three main parts. Firstly, a comprehensive review of available approaches is presented and discussed. The classical canon methods are extended with a discussion of nonlinear and complex alternative measures using non-Gaussian statistics, persistence and fractional calculations. Secondly, the methods’ applicability aspects are visualized with the aid of computer simulations, covering the most popular control philosophies used in the process industry. Lastly, a critical review of the methods discussed, on the basis of real-world industrial examples, rounds out the coverage.

Volatility Clustering in the Forex Market - An Interacting Agents Approach
Volatility Clustering in the Forex Market - An Interacting Agents Approach

Author:

Publisher:

Published: 2015

Total Pages:

ISBN-13:

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Financial time series have been shown to exhibit market regularities, so-called stylized facts, which have challenged the rational expectations and efficient market theory. In order to explain those market regularities, behavioral finance economists developed a broad range of agent-based models consisting of agents with heterogeneous expectations on future prices. Agents were not only assumed to have heterogeneous expectations and different trading strategies, they were furthermore assumed to be able to switch between the strategies. The present paper focuses on one particular market regularity, which is volatility clustering of financial time series in the framework of the foreign exchange market. The goal is to explain the phenomenon of volatility clustering from a behavioral finance perspective. In a first step, an overview over common Forex market characteristics is provided, followed by some traditional models of exchange rate determination and the subsequent paradigm shift in the concept of expectations. After having presented the main behavioral explanations on volatility clustering, an agent-based model is introduced, capturing the idea of agent's inertia, as one possible driver of volatility clustering in financial markets. The introduced agent-based model represents an extension of the original model by Frank Westerhoff (2010). The present paper contributes to the behavioral finance literature by enlightening one novel aspect of agent's behavior that may affect price dynamics in financial markets.

More Than You Ever Wanted to Know About the VIX
More Than You Ever Wanted to Know About the VIX

Author: Stefan Rostek

Publisher:

Published: 2014

Total Pages: 30

ISBN-13:

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In this paper, I show that the volatility index VIX is not model-free as soon as the diff usion term is not Brownian motion even when correcting for jumps. In a stock index model that allows for temporary periods of under- or overreaction, such as a multifractional model, a wrong annualization procedure associated with the VIX, may lead to severe mispricings as the three concepts of the VIX, the volatility per annum and (the square-root of) realized variance do not coincide any longer.As a byproduct, this article proves that multifractionality is a parsimonious and economically sound explanation for the observable phenomenon of volatility clustering as it suggests that clusters of abnormally high (low) volatility can be attributed to periods of underreaction (overreaction). In this way, the paper brings together two observable patterns in fi nancial markets: the transitory existence of periods with serially correlated asset returns and the phenomenon of volatility clustering.

Volatility Clustering, Asymmetry and Hysteresis in Stock Returns
Volatility Clustering, Asymmetry and Hysteresis in Stock Returns

Author: Michel Crouhy

Publisher:

Published: 1998

Total Pages:

ISBN-13:

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Encompassing a very broad family of ARCH-GARCH models we show that heteroskedasticity, already well documented for the US market, is a worldwide phenomenon. The AT-GARCH (1,1) model, where volatility rises more in response to bad news than to good news, and where news is considered bad only below a certain level, is found to be a remarkably robust representation of worldwide stock market returns. The residual structure is then captured by extending ATGARCH (1,1) to an hysteresis model, HGARCH, where we model structured memory effects from past innovations. Obviously, this feature relates to the psychology of the markets and the way traders process information. For the French stock market we show that a shock of either sign may affect volatility differently, depending on the recent past being characterized by either all positive or all negative returns. In the same way a longer term trend of either sign may also influence the impact on volatility of current innovations. It is found that bad news is discounted very quickly in volatility, this effect is reinforced when it comes after a negative trend in the stock index. On the opposite, good news has a very small impact on volatility except when it is clustered over a few days, which in this case reduces volatility substantially.

Conditional Probability as a Measure of Volatility Clustering in Financial Time Series
Conditional Probability as a Measure of Volatility Clustering in Financial Time Series

Author: Kan Chen

Publisher:

Published: 2005

Total Pages: 6

ISBN-13:

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Empirical analysis of time series of asset returns has revealed fat tails and volatility clustering which manifests itself as autocorrelations in absolute returns. We provide a quantitative measure of the well-studied phenomenon of volatility clustering in financial time series: We use the conditional probability distribution of the asset return, given the return in the previous time interval. Our analysis of a variety of data reveals a scaling collapse on to universal curve with a power-law tail at large returns. The scale factor provides a direct measure of volatility clustering. We introduce a phenomenological model which captures some of the key features of this scaling.