Complexity In Financial Time Series

An accessible guide to the multivariate time series toolsused in numerous real-world applications Multivariate Time Series Analysis: With R and FinancialApplications is the much anticipated sequel coming from one ofthe most influential and prominent experts on the topic of timeseries. Through a fundamental balance of theory and methodology,the book supplies readers with a comprehensible approach tofinancial econometric models and their applications to real-worldempirical research. Differing from the traditional approach to multivariate timeseries, the book focuses on reader comprehension by emphasizingstructural specification, which results in simplified parsimoniousVAR MA modeling. Multivariate Time Series Analysis: With R andFinancial Applications utilizes the freely available Rsoftware package to explore complex data and illustrate relatedcomputation and analyses. Featuring the techniques and methodologyof multivariate linear time series, stationary VAR models, VAR MAtime series and models, unitroot process, factor models, andfactor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce thepresented content • User-friendly R subroutines and research presentedthroughout to demonstrate modern applications • Numerous datasets and subroutines to provide readerswith a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbookfor graduate-level courses on time series and quantitative financeand upper-undergraduate level statistics courses in time series.The book is also an indispensable reference for researchers andpractitioners in business, finance, and econometrics.

Many aspects contribute to make financial markets one of the most challenging system to understand. The aim of this thesis is to study some aspects of their complexity by focusing on univariate e multivariate properties of log-returns time-series, namely multifractality and cross-dependence. In this thesis, we started by performing a thorough analysis of the scaling properties of synthetic time-series with different known scaling properties. This enabled us to do two things: find the presence of a strong bias in the estimation of the scaling exponents, and interpret measurement on real data which led us to uncover the true source of the multifractal behaviour of financial log-prices, which has been long debated in the literature. We addressed the presence of the bias by proposing a method which manages to filter out its presence and we validate it by applying it to synthetic time-series with known scaling properties and on empirical ones. We also found that this bias is due to the stability under aggregation of the log-returns which, due to their long memory, are processes which for high aggregation tend to a random variable which displays an exact multifractal scaling. Finally we focused the attention on linking the scaling properties of log-returns to their cross-correlation properties within a given market finding an intriguing non-linear relationship between the two quantities.

The book is motivated by the disruptions introduced by the financial crisis and the many attempts that have followed to propose new ideas and remedies. Assembling contributions by authors from a variety of backgrounds, this collection illustrates the potentials resulting from the marriage of financial economics, complexity theory and an out-of-equilibrium view of the economic world. Challenging the traditional hypotheses that lie behind financial market functioning, new evidence is provided about the hidden factors fuelling bubbles, the impact of agents’ heterogeneity, the importance of endogeneity in the information transmission mechanism, the dynamics of herding, the sources of volatility, the portfolio optimization techniques, the financial innovation and the trend identification in a nonlinear time-series framework. Presenting the advances made in financial market analysis, and putting emphasis on nonlinear dynamics, this book suggests interdisciplinary methodologies for the study of well-known stylised facts and financial abnormalities. This book was originally published as a special issue of The European Journal of Finance.

Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.

This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems.

This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers decomposition methods, autocorrelation methods for univariate time series, volatility and duration modeling for financial time series, and multivariate time series methods, such as cointegration and recursive state space modeling. It also includes numerous practical examples to demonstrate the theory using real-world data, as well as exercises at the end of each chapter to aid understanding. This book serves as a reference text for researchers, students and practitioners interested in time series, and can also be used for university courses on econometrics or computational finance.

The book you hold in your hands is the outcome of the "ISCS 2013: Interdisciplinary Symposium on Complex Systems" held at the historical capital of Bohemia as a continuation of our series of symposia in the science of complex systems. Prague, one of the most beautiful European cities, has its own beautiful genius loci. Here, a great number of important discoveries were made and many important scientists spent fruitful and creative years to leave unforgettable traces. The perhaps most significant period was the time of Rudolf II who was a great supporter of the art and the science and attracted a great number of prominent minds to Prague. This trend would continue. Tycho Brahe, Niels Henrik Abel, Johannes Kepler, Bernard Bolzano, August Cauchy Christian Doppler, Ernst Mach, Albert Einstein and many others followed developing fundamental mathematical and physical theories or expanding them. Thus in the beginning of the 17th century, Kepler formulated here the first two of his three laws of planetary motion on the basis of Tycho Brahe’s observations. In the 19th century, nowhere differentiable continuous functions (of a fractal character) were constructed here by Bolzano along with a treatise on infinite sets, titled “Paradoxes of Infinity” (1851). Weierstrass would later publish a similar function in 1872. In 1842, Doppler as a professor of mathematics at the Technical University of Prague here first lectured about a physical effect to bear his name later. And the epoch-making physicist Albert Einstein – while being a chaired professor of theoretical physics at the German University of Prague – arrived at the decisive steps of his later finished theory of general relativity during the years 1911–1912. In Prague, also many famous philosophers and writers accomplished their works; for instance, playwright arel ape coined the word "robot" in Prague (“robot” comes from the Czech word “robota” which means “forced labor”).

Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.