Discrete Time Series Processes and Applications in Finance Preface List of Figures. List of Tables 1. Introduction 2.Notation naming and general definitions 3.Stylized facts 4.Empirical mug shots 5.Process Overview 6.Logarithmic versus relative random walks 7.ARCH processes 8.Stochastic volatility processes 9.Regime switching process 10.Price and volatility using high frequency data 11.Time reversal asymmetry 12.Characterizing heteroskedasticity 13.The innovation distributions 14.Leverage effect 15.Processes and market risk evaluation 16.Option pricing 17.Properties of large covariance matrices 18.Multivariate ARCH processes 19.The processes compatible with the stylized facts 20.Further thoughts. Bibliography Index. Most financial and investment decisions are based on considerations of possible future changes and require forecasts on the evolution of the financial world. Time series and processes are the natural tools for describing the dynamic behavior of financial data leading to the required forecasts. This book presents a survey of the empirical properties of financial time series their descriptions by means of mathematical processes and some implications for important financial applications used in many areas like risk evaluation option pricing or portfolio construction. The statistical tools used to extract information from raw data are introduced. Extensive multiscale empirical statistics provide a solid benchmark of stylized facts (heteroskedasticity long memory fat tails leverage…) in order to assess various mathematical structures that can capture the observed regularities. The author introduces a broad range of processes and evaluates them systematically against the benchmark summarizing the successes and limitations of these models from an empirical point of view. The outcome is that only multiscale ARCH processes with long memory discrete multiplicative structures and non normal innovations are able to capture correctly the empirical properties. In particular only a discrete time series framework allows to capture all the stylized facts in a process whereas the stochastic calculus used in the continuum limit is too constraining. The present volume offers various applications and extensions for this class of processes including high frequency volatility estimators market risk evaluation covariance estimation and multivariate extensions of the processes. The book discusses many practical implications and is addressed to practitioners and quants in the financial industry as well as to academics including graduate (Master or PhD level) students. The prerequisites are basic statistics and some elementary financial mathematics. Gilles Zumbach has worked for several institutions including banks hedge funds and service providers and continues to be engaged in research on many topics in finance. His primary areas of interest are volatility ARCH processes and financial applications.