Download or read book Bayesian Modeling and Forecasting of 24 Hour High Frequency Volatility written by Jonathan R. Stroud and published by . This book was released on 2014 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper estimates models of high frequency index futures returns using 'around the clock' 5-minute returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using financial crisis data from 2007 to 2009, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting from 2009 to 2012. We show that our approach improves realized volatility forecasts by up to 50% over existing benchmarks.

Download or read book Bayesian Model Averaging for Realized Volatility Models written by Robert W. Jones and published by . This book was released on 2018 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research explores statistical methods for forecasting realized volatility for stock market holdings; primarily Stochastic Dierential Equations for the development of various volatility measures and Bayesian Model Averaging for the development and optimization of a linear model capable of predicting said volatility. These methods will be outlined and explained before being applied to high frequency trade data.

Download or read book High Frequency Data Frequency Domain Inference and Volatility Forecasting written by Jonathan H. Wright and published by . This book was released on 1999 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: While it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high frequency data. The method avoids using a tight parametric model, by instead simply fitting a long autoregression to log-squared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.

Download or read book Modelling Volatility in Financial Markets written by Chun Liu and published by . This book was released on 2007 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, I study the dynamics of the volatility process and focus on estimation and forecasting. Recent research uses high frequency intraday data to construct ex post measures of daily volatility including realized volatility (RV). Chapter 1 is the introduction. In Chapter 2, I use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. I focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations I demonstrate that the estimation approach is effective in identifying and dating structural breaks. Applied to daily S & P 500 data, I find strong evidence of a single structural break in log(RV). The main effect of the break is on the long-run mean and variance of log-volatility. Chapter 3 uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include HAR specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and leverage term. The Bayesian model averaging provides very competitive density forecasts and consistent but modest improvements in point forecasts over the benchmarks. Applied to equity and exchange rate volatility over several forecast horizons, the Bayesian model averaging provides the best performance compared to the benchmarks including HAR, AR and simple model averaging models. I discuss the reasons for this, including the importance of using realized power variation as a predictor. In the last chapter, I propose a new joint model of volatility and duration in high frequency framework using tick-by-tick data. This model decomposes the conditional variance into different volatility components associated with different transaction horizons. Using stock market data, I demonstrate its superiority over the traditional GARCH counterpart. In addition, I show that a fat-tailed t-distribution for return innovations and a Burr distribution for duration innovations improve density forecasts, compared with normal and exponential distribution, respectively.

Download or read book Bayesian Dynamic Modeling of High Frequency Integer Price Changes written by Istvan Barra and published by . This book was released on 2018 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price changes. We account for the discrete nature of the data via two different approaches: ordered probit models and discrete distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic patterns. We consider distributions with heavy tails to address occurrences of jumps in tick by tick discrete prices changes. In particular, we introduce a dynamic version of the negative binomial difference model with stochastic volatility. For each model we develop a Markov chain Monte Carlo estimation method that takes advantage of auxiliary mixture representations to facilitate the numerical implementation. This new modeling framework is illustrated by means of tick by tick data for several stocks from the NYSE and for different periods. Different models are compared with each other based on predictive likelihoods.We find evidence in favor of our preferred dynamic negative binomial difference model.

Download or read book Mathematical and Statistical Methods for Actuarial Sciences and Finance written by Marco Corazza and published by Springer Nature. This book was released on 2021-12-13 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page papers, presented at the International Conference eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca’ Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.

Download or read book Modeling and Forecasting Long Range Dependence in Volatility written by Nan Qu and published by . This book was released on 2010 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis conducts three exercises on volatility modeling of financial assets. We are essentially interested in the estimation and forecasting of daily volatility, a measure of the strength of price movements over daily intervals. Two of the exercises are in the realm of high frequency data: modeling and forecasting realized volatility which is constructed from intra-day returns. The other exercise is concerned with discrete stochastic volatility modeling using daily returns. The main focus of each exercise is to represent the high degree of volatility persistence, which is an important stylized fact of daily volatility.In the first exercise, daily realized volatility of the Yen/USD exchange rate is modeled through an autoregressive and moving-average fractionally integrated (ARFIMA) process. We differ from previous studies by averaging across a set of ARFIMA and ARMA models with different orders of autoregressive and moving-average polynomials. The vehicle used to execute this averaging exercise is Bayesian model averaging, through which part of the uncertainty introduced by model selection is integrated out. We examine the practical usefulness of our method by conducting a rolling-sample estimation, and the results indicate the weighted average forecast out-performs that of a single model at long-term horizons by providing smaller mean squared forecast errors.The second exercise is concerned with Bayesian estimation of a long memory stochastic volatility (SV) model. We use a high-order moving-average process to approximate the fractional integration specified for the latent log volatility. As such, the long memory SV model can be expressed in a state-space form, which facilitates the implementation of Markov chain Monte Carlo (MCMC) simulation when parameters and latent volatility are estimated. We update the set of memory parameter and volatility of volatility parameter in one block in the MCMC algorithm, by using the hessian matrix. A Monte Carlo study indicates in general, when the posterior mean is treated as a point estimator of parameters, our Bayesian method compares well with classical methods. Furthermore, the Bayesian estimator tends to outperform the popular frequency quasi maximum likelihood estimator, according to the root mean square error criterion, with small and medium sample size. An empirical analysis of the daily Yen/USD exchange rate spanning 26 years is conducted, and the degree of persistency in volatility is found to be consistent with that from the first exercise when high frequency data are used.In the third exercise, we look at the long memory property from a different angle. There has been a large literature using specifications other than fractional integration to mimic the long memory property in time series analysis, although there are few applications to realized volatility. In this exercise, regime switching models are fitted to daily realized volatility of the JPY/USD exchange rate from 1996 to 2009. Both in-sample fit and out-of-sample forecasting are used to compare across the three types of models, including ARFIMA, regime switching and sum of short memory processes. An extensive recursive estimation over one year suggests that regime switching is superior in capturing the dynamics of the time series examined, and generating more accurate out-of-sample forecasts.

Download or read book Volatility and Correlation written by Riccardo Rebonato and published by John Wiley & Sons. This book was released on 2005-07-08 with total page 864 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Volatility and Correlation 2nd edition: The Perfect Hedger and the Fox, Rebonato looks at derivatives pricing from the angle of volatility and correlation. With both practical and theoretical applications, this is a thorough update of the highly successful Volatility & Correlation – with over 80% new or fully reworked material and is a must have both for practitioners and for students. The new and updated material includes a critical examination of the ‘perfect-replication’ approach to derivatives pricing, with special attention given to exotic options; a thorough analysis of the role of quadratic variation in derivatives pricing and hedging; a discussion of the informational efficiency of markets in commonly-used calibration and hedging practices. Treatment of new models including Variance Gamma, displaced diffusion, stochastic volatility for interest-rate smiles and equity/FX options. The book is split into four parts. Part I deals with a Black world without smiles, sets out the author’s ‘philosophical’ approach and covers deterministic volatility. Part II looks at smiles in equity and FX worlds. It begins with a review of relevant empirical information about smiles, and provides coverage of local-stochastic-volatility, general-stochastic-volatility, jump-diffusion and Variance-Gamma processes. Part II concludes with an important chapter that discusses if and to what extent one can dispense with an explicit specification of a model, and can directly prescribe the dynamics of the smile surface. Part III focusses on interest rates when the volatility is deterministic. Part IV extends this setting in order to account for smiles in a financially motivated and computationally tractable manner. In this final part the author deals with CEV processes, with diffusive stochastic volatility and with Markov-chain processes. Praise for the First Edition: “In this book, Dr Rebonato brings his penetrating eye to bear on option pricing and hedging.... The book is a must-read for those who already know the basics of options and are looking for an edge in applying the more sophisticated approaches that have recently been developed.” —Professor Ian Cooper, London Business School “Volatility and correlation are at the very core of all option pricing and hedging. In this book, Riccardo Rebonato presents the subject in his characteristically elegant and simple fashion...A rare combination of intellectual insight and practical common sense.” —Anthony Neuberger, London Business School

Download or read book Estimating Stochastic Volatility and Jumps Using High Frequency Data and Bayesian Methods written by Milan Fičura and published by . This book was released on 2015 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are comparing two approaches for stochastic volatility and jumps estimation in the EUR/USD time series - the non-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models) using daily returns. We find that both of the methods do identify continuous stochastic volatility similarly, but they do not identify similarly the jump component. Firstly - the jumps estimated using the non-parametric high-frequency estimators are much more numerous than in the case of the Bayesian method using daily data. More importantly - we find that the probabilities of jump occurrences assigned to every day by both of the methods are virtually no rank-correlated (Spearman rank correlation is 0.0148) meaning that the two methods do not identify jumps at the same days. Actually the jump probabilities inferred using the non-parametric approach are not much correlated even with the daily realized variance and the daily squared returns, indicating that the discontinuous price changes (jumps) observed on high-frequencies may not be distinguishable (from the continuous volatility) on the daily frequency. As an additional result we find strong evidence for jump size dependence and jump clustering (based on the self-exciting Hawkes process) of the jumps identified using the non-parametric method (the shrinkage estimator).

Download or read book New Trends in Finance and Accounting written by David Procházka and published by Springer. This book was released on 2016-12-25 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most current trends in the field of finance and accounting from an international perspective. Featuring contributions presented at the 17th Annual Conference on Finance and Accounting at the University of Economics in Prague, this title provides a mix of research methods used to uncover the hidden consequences of accounting convergence in the private (IFRS) and public sectors (IPSAS). Topics covered include international taxation (from both the micro- and macroeconomic level), international investment, monetary economics, risk management, management accounting, auditing, investment capital, corporate finance and banking, among others. The global business environment shapes the international financial flows of finance and the demand for international harmonization of accounting. As such, the field of global finance and accounting has encountered some new challenges. For example, policy-makers and regulators are forced to restructure their tools to tackle with new features of trading at global capital markets and international investment. This book complements this global view of development with country-specific studies, focusing on emerging and transitioning economies, which are affected indirectly and in unforeseen ways. The combination of global perspective and local specifics makes this volume attractive and useful to academics, researchers, regulators and policy-makers in the field of finance and accounting.

Download or read book Time Series Models written by Andrew C. Harvey and published by Financial Times/Prentice Hall. This book was released on 1993 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: A companion volume to The Econometric Analysis of Time series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.

Download or read book Stochastic Volatility Models with Heavy tailed Distributions written by Toshiaki Watanabe and published by . This book was released on 2001 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Volatility Forecasting written by Torben Gustav Andersen and published by . This book was released on 2005 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.

Download or read book Bayesian Inference for Security Price Volatility Incorporating Daily Extreme Prices written by Tongshu Ma and published by . This book was released on 1998 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Volatility Forecasting Using Threshold Heteroskedastic Models of the Intra day Range written by Cathy W. S. Chen and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous variable such as US market information. The model captures aspects such as sign or size asymmetry and heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation experiments. Finally, competing range-based and return-based heteroskedastic models are compared via out-of-sample forecast performance. Applied to six international financial market indices, the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.

Download or read book Handbook of Volatility Models and Their Applications written by Luc Bauwens and published by John Wiley & Sons. This book was released on 2012-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

Download or read book Applied Bayesian Forecasting and Time Series Analysis written by Andy Pole and published by CRC Press. This book was released on 1994-09-01 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.