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| Author | : Francesco Ravazzolo |
| Publisher | : Rozenberg Publishers |
| Total Pages | : 198 |
| Release | : 2007 |
| Genre | : |
| ISBN | : 9051709145 |
Download Forecasting Financial Time Series Using Model Averaging Book in PDF, ePub and Kindle
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 | : Eric Zivot |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 632 |
| Release | : 2013-11-11 |
| Genre | : Business & Economics |
| ISBN | : 0387217630 |
Download Modeling Financial Time Series with S-PLUS Book in PDF, ePub and Kindle
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 | : Dimitris Korobilis |
| Publisher | : |
| Total Pages | : 27 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Download Factor Model Forecasting Book in PDF, ePub and Kindle
We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different sources of instability in the factors, such as stochastic volatility and structural breaks. Our results indicate that ignoring structural breaks in the loadings can be quite costly in terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the performance of the FCI, by means of discarding irrelevant financial variables during the estimation of the factor.
| Author | : Jonathan H. Wright |
| Publisher | : |
| Total Pages | : 42 |
| Release | : 2003 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
Download Forecasting U.S. Inflation by Bayesian Model Averaging Book in PDF, ePub and Kindle
| Author | : Ruey S. Tsay |
| Publisher | : John Wiley & Sons |
| Total Pages | : 414 |
| Release | : 2013-11-11 |
| Genre | : Mathematics |
| ISBN | : 1118617754 |
Download Multivariate Time Series Analysis Book in PDF, ePub and Kindle
An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
| Author | : Hyun Hak Kim |
| Publisher | : |
| Total Pages | : 37 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Download Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods Book in PDF, ePub and Kindle
In this paper, we empirically assess the predictive accuracy of a large group of models based on the use of principle components and other shrinkage methods, including Bayesian model averaging and various bagging, boosting, LASSO and related methods. Our results suggest that model averaging does not dominate other well designed prediction model specification methods, and that using a combination of factor and other shrinkage methods often yields superior predictions. For example, when using recursive estimation windows, which dominate other “windowing” approaches in our experiments, prediction models constructed using pure principal component type models combined with shrinkage methods yield mean square forecast error “best” models around 70% of the time, when used to predict 11 key macroeconomic indicators at various forecast horizons. Baseline linear models (which “win” around 5% of the time) and model averaging methods (which win around 25% of the time) fare substantially worse than our sophisticated nonlinear models. Ancillary findings based on our forecasting experiments underscore the advantages of using recursive estimation strategies, and provide new evidence of the usefulness of yield and yield-spread variables in nonlinear prediction specification.
| Author | : Jonathan H. Wright |
| Publisher | : |
| Total Pages | : 38 |
| Release | : 2003 |
| Genre | : Foreign exchange |
| ISBN | : |
Download Bayesian Model Averaging and Exchange Rate Forecasts Book in PDF, ePub and Kindle
| Author | : Gebhard Kirchgässner |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 326 |
| Release | : 2012-10-09 |
| Genre | : Business & Economics |
| ISBN | : 3642334350 |
Download Introduction to Modern Time Series Analysis Book in PDF, ePub and Kindle
This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
| Author | : Marian Risse |
| Publisher | : |
| Total Pages | : 43 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Download Using Dynamic Model Averaging in State Space Representation with Dynamic Occam's Window and Applications to the Stock and Gold Market Book in PDF, ePub and Kindle
We combine the Onorante and Raftery (2016) dynamic Occam's window approach with the Raftery et al. (2010) DMA/DMS estimator in state space representation to create forecasts using a data-rich forecasting environment. Our approach is mainly related to economic and financial time series that are subject to periods of high volatility, which increases the necessity of a time varying parameter framework. In a forecasting exercise for the stock and gold markets, we highlight the economic value-added of our approach by applying a simple trading rule to the return series. By combining both assets, we show that our approach performs better when compared to alternative forecasting models such as machine learning algorithms and standard DMA/DMS. Results for the complexity of the forecasting models highlight the advantages of high dimensional forecasting approaches in times of economic uncertainty, such as the recent financial crisis. The economic performance of the trading rule weakens when we consider transaction costs.
| Author | : Stephen J. Taylor |
| Publisher | : World Scientific |
| Total Pages | : 297 |
| Release | : 2008 |
| Genre | : Business & Economics |
| ISBN | : 9812770844 |
Download Modelling Financial Time Series Book in PDF, ePub and Kindle
This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts.This second edition takes into account the remarkable progress made by empirical researchers during the past two decades from 1986 to 2006. In the new Preface, the author summarizes this progress in two key areas: firstly, measuring, modelling and forecasting volatility; and secondly, detecting and exploiting price trends.
