Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Estimating And Forecasting Structural Breaks In Financial Time Series PDF full book. Access full book title Estimating And Forecasting Structural Breaks In Financial Time Series.
| Author | : Luc Bauwens |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
| Author | : David E. Rapach |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 691 |
| Release | : 2008-02-29 |
| Genre | : Business & Economics |
| ISBN | : 044452942X |
Forecasting in the presence of structural breaks and model uncertainty are active areas of research with implications for practical problems in forecasting. This book addresses forecasting variables from both Macroeconomics and Finance, and considers various methods of dealing with model instability and model uncertainty when forming forecasts.
| Author | : Torben Gustav Andersen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1045 |
| Release | : 2009-04-21 |
| Genre | : Business & Economics |
| ISBN | : 3540712976 |
The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.
| Author | : Zhongfang He |
| Publisher | : |
| Total Pages | : 268 |
| Release | : 2009 |
| Genre | : |
| ISBN | : 9780494609750 |
This thesis consists of three essays in empirical finance and macroeconomics. The first essay proposes a new structural-break vector autoregressive model for predicting real output growth by the nominal yield curve. The model allows for the possibility of both in-sample and out-of-sample breaks in parameter values and uses information in historical regimes to make inference on out-of-sample breaks. A Bayesian estimation and forecasting procedure is developed which accounts for the uncertainty of both structural breaks and model parameters. I discuss dynamic consistency when forecasting recursively and provide a solution. Applied to monthly US data, I find strong evidence of breaks in the predictive relation between the yield curve and output growth. Incorporating the possibility of structural breaks improves out-of-sample forecasts of output growth.The third essay proposes a new tilt stochastic volatility model which extends the existing volatility models by modeling the asymmetric correlation between return and volatility innovations in a unified and flexible framework. The Efficient Importance Sampling (EIS) procedure is adapted to estimate the model. Simulation studies show that the Maximum Likelihood (ML)-EIS estimation of the model is accurate. The new model is applied to the CRSP daily returns. I find the extensions are significant and incorporating them improves the accuracy of volatility estimates.The second essay proposes a sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks. We use particle filtering techniques that allow for fast and efficient updates of posterior quantities and forecasts in real-time. The method conveniently deals with the path dependence problem that arises in these type of models. The performance of the method is shown to work well using simulated data. Applied to daily NASDAQ returns, we find strong evidence of structural breaks in the long-run variance of returns. Models with flexible return distributions such as t-innovations or with jumps indicate fewer breaks than models with normal return innovations and are favored by the data.
| Author | : Francesco Ravazzolo |
| Publisher | : Rozenberg Publishers |
| Total Pages | : 198 |
| Release | : 2007 |
| Genre | : |
| ISBN | : 9051709145 |
Believing in a single model may be dangerous, and addressing model uncertainty by averaging different models in making forecasts may be very beneficial. In this thesis we focus on forecasting financial time series using model averaging schemes as a way to produce optimal forecasts. We derive and discuss in simulation exercises and empirical applications model averaging techniques that can reproduce stylized facts of financial time series, such as low predictability and time-varying patterns. We emphasize that model averaging is not a "magic" methodology which solves a priori problems of poorly forecasting. Averaging techniques have an essential requirement: individual models have to fit data. In the first section we provide a general outline of the thesis and its contributions to previ ous research. In Chapter 2 we focus on the use of time varying model weight combinations. In Chapter 3, we extend the analysis in the previous chapter to a new Bayesian averaging scheme that models structural instability carefully. In Chapter 4 we focus on forecasting the term structure of U.S. interest rates. In Chapter 5 we attempt to shed more light on forecasting performance of stochastic day-ahead price models. We examine six stochastic price models to forecast day-ahead prices of the two most active power exchanges in the world: the Nordic Power Exchange and the Amsterdam Power Exchange. Three of these forecasting models include weather forecasts. To sum up, the research finds an increase of forecasting power of financial time series when parameter uncertainty, model uncertainty and optimal decision making are included.
| Author | : Yaein Baek |
| Publisher | : |
| Total Pages | : 176 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
Instability of parametric models is a common problem in many fields of economics. In econometrics, these changes in the underlying data generating process are referred to as structural breaks. Although there is an extensive literature on estimation and statistical tests of structural breaks, existing methods fail to adequately capture a break. This dissertation consists of three papers on developing econometric methods for structural breaks and forecasting. The first chapter develops a new method in estimating the location of a structural break in a linear model and provide theoretical results and empirical applications of the estimator. In finite sample the conventional least-squares estimates a break occurred at either ends of the sample with high probability, regardless of the true break point. I suggest an estimator of the break point that resolves this pile up issue and thus, provide a more accurate estimate of the break. The second chapter constructs a statistical test to test existence of a structural break when the direction of the parameter shift is known. In practice it is likely that a researcher is interested in testing for a structural break in a particular direction because the direction is known, such as policy change or historical data. We incorporate this information in constructing three tests that have higher power when direction is correctly specified. The last chapter proposes a multi-period forecasting method that is robust to model misspecification. When we are interested in obtaining long horizon ahead forecasts, the direct forecast method is more favorable than the iterated forecast because it is more robust to misspecification. However, direct forecast estimates tend to have jagged shapes across horizons. I use a mechanism analogous to ridge regression on the direct forecast model to maintain robustness while smoothing out erratic estimates.
| Author | : Terence C. Mills |
| Publisher | : Cambridge University Press |
| Total Pages | : 411 |
| Release | : 2008-03-20 |
| Genre | : Business & Economics |
| ISBN | : 1139470817 |
Terence Mills' best-selling graduate textbook provides detailed coverage of research techniques and findings relating to the empirical analysis of financial markets. In its previous editions it has become required reading for many graduate courses on the econometrics of financial modelling. This third edition, co-authored with Raphael Markellos, contains a wealth of material reflecting the developments of the last decade. Particular attention is paid to the wide range of nonlinear models that are used to analyse financial data observed at high frequencies and to the long memory characteristics found in financial time series. The central material on unit root processes and the modelling of trends and structural breaks has been substantially expanded into a chapter of its own. There is also an extended discussion of the treatment of volatility, accompanied by a new chapter on nonlinearity and its testing.
| Author | : Tae-hwy Lee |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
| Author | : Eric Zivot |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 632 |
| Release | : 2013-11-11 |
| Genre | : Business & Economics |
| ISBN | : 0387217630 |
The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.
| Author | : Massimo Guidolin |
| Publisher | : Academic Press |
| Total Pages | : 435 |
| Release | : 2018-05-29 |
| Genre | : Business & Economics |
| ISBN | : 0128134100 |
Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher)
