Search Results for "one-day-ahead"

Financial Risk Management with Bayesian Estimation of GARCH Models

Financial Risk Management with Bayesian Estimation of GARCH Models

Theory and Applications

  • Author: David Ardia
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540786573
  • Category: Business & Economics
  • Page: 206
  • View: 7370
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This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.

ARCH Models for Financial Applications

ARCH Models for Financial Applications

  • Author: Evdokia Xekalaki,Stavros Degiannakis
  • Publisher: John Wiley & Sons
  • ISBN: 9780470688021
  • Category: Mathematics
  • Page: 558
  • View: 3750
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Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features: Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique. Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation. Uses empirical examples to demonstrate how the recent developments in ARCH can be implemented. Provides step-by-step instructive examples, using econometric software, such as Econometric Views and the [email protected] module for the Ox software package, used in Estimating and Forecasting ARCH Models. Accompanied by a CD-ROM containing links to the software as well as the datasets used in the examples. Aimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.

Nonlinear Time Series Analysis of Business Cycles

Nonlinear Time Series Analysis of Business Cycles

  • Author: Costas Milas,Philip Rothman,Dick van Dijk
  • Publisher: Emerald Group Publishing
  • ISBN: 044451838X
  • Category: Business & Economics
  • Page: 435
  • View: 8342
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The business cycle has long been the focus of empirical economic research. Until recently statistical analysis of macroeconomic fluctuations was dominated by linear time series methods. Over the past 15 years, however, economists have increasingly applied tractable parametric nonlinear time series models to business cycle data; most prominent in this set of models are the classes of Threshold AutoRegressive (TAR) models, Markov-Switching AutoRegressive (MSAR) models, and Smooth Transition AutoRegressive (STAR) models. In doing so, several important questions have been addressed in the literature, including: 1. Do out-of-sample (point, interval, density, and turning point) forecasts obtained with nonlinear time series models dominate those generated with linear models? 2. How should business cycles be dated and measured? 3. What is the response of output and employment to oil-price and monetary shocks? 4. How does monetary policy respond to asymmetries over the business cycle? 5. Are business cycles due more to permanent or to transitory negative shocks? 6. Is the business cycle asymmetric, and does it matter? Accordingly, we have compiled and edited a book for the Elsevier economics program comprising 15 original papers on these and related themes. *Contributions to Economic Analysis was established in 1952 *The series purpose is to stimulate the international exchange of scientific information *The series includes books from all areas of macroeconomics and microeconomics

Applied Artificial Higher Order Neural Networks for Control and Recognition

Applied Artificial Higher Order Neural Networks for Control and Recognition

  • Author: Zhang, Ming
  • Publisher: IGI Global
  • ISBN: 1522500642
  • Category: Computers
  • Page: 511
  • View: 574
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In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless. Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.

Artificial Neural Networks - ICANN 2006

Artificial Neural Networks - ICANN 2006

16th International Conference, Athens, Greece, September 10-14, 2006, Proceedings

  • Author: N.A
  • Publisher: Springer Science & Business Media
  • ISBN: 3540388710
  • Category: Artificial intelligence
  • Page: 2036
  • View: 8155
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Integrating Renewables in Electricity Markets

Integrating Renewables in Electricity Markets

Operational Problems

  • Author: Juan M. Morales,Antonio J. Conejo,Henrik Madsen,Pierre Pinson,Marco Zugno
  • Publisher: Springer Science & Business Media
  • ISBN: 1461494117
  • Category: Business & Economics
  • Page: 429
  • View: 8581
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This addition to the ISOR series addresses the analytics of the operations of electric energy systems with increasing penetration of stochastic renewable production facilities, such as wind- and solar-based generation units. As stochastic renewable production units become ubiquitous throughout electric energy systems, an increasing level of flexible backup provided by non-stochastic units and other system agents is needed if supply security and quality are to be maintained. Within the context above, this book provides up-to-date analytical tools to address challenging operational problems such as: • The modeling and forecasting of stochastic renewable power production. • The characterization of the impact of renewable production on market outcomes. • The clearing of electricity markets with high penetration of stochastic renewable units. • The development of mechanisms to counteract the variability and unpredictability of stochastic renewable units so that supply security is not at risk. • The trading of the electric energy produced by stochastic renewable producers. • The association of a number of electricity production facilities, stochastic and others, to increase their competitive edge in the electricity market. • The development of procedures to enable demand response and to facilitate the integration of stochastic renewable units. This book is written in a modular and tutorial manner and includes many illustrative examples to facilitate its comprehension. It is intended for advanced undergraduate and graduate students in the fields of electric energy systems, applied mathematics and economics. Practitioners in the electric energy sector will benefit as well from the concepts and techniques explained in this book.

Stochasticity, Nonlinearity and Forecasting of Streamflow Processes

Stochasticity, Nonlinearity and Forecasting of Streamflow Processes

  • Author: Wen Wang
  • Publisher: IOS Press
  • ISBN: 9781586036218
  • Category: Technology & Engineering
  • Page: 210
  • View: 554
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"Streamflow forecasting is of great importance to water resources management and flood defense. On the other hand, a better understanding of the streamflow process is fundamental for improving the skill of streamflow forecasting. The methods for forecasting streamflows may fall into two general classes: process-driven methods and data-driven methods. Equivalently, methods for understanding streamflow processes may also be broken into two categories: physically-based methods and mathematically-based methods. This publication focuses on using mathematically-based methods to analyze stochasticity and nonlinearity of streamflow processes based on univariate historic streamflow records, and presents data-driven models that are also mainly based on univariate streamflow time series. Six streamflow processes of five rivers in different geological regions are investigated for stochasticity and nonlinearity at several characteristic timescales (i.e., one day, one month, 1/3 month and one year). But only the streamflows of the upper Yellow River in northern China are considered for forecasting."

Proving and Pricing Construction Claims

Proving and Pricing Construction Claims

  • Author: Robert Frank Cushman,Esq Cushman,John D. Carter,Paul J. Gorman,Douglas F. Coppi
  • Publisher: Aspen Publishers Online
  • ISBN: 9780735514454
  • Category: Law
  • Page: 664
  • View: 1555
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The most useful, definitive resource available on every aspect of construction claims, including how to present the claims, how to calculate and prove the amount of damages sustained, and how to prove liability. It even covers the clauses that should be in every construction contract. You'll get comprehensive coverage of all the important issues -- delay claims, differing site conditions claims, claims for lost profit, international claims, and much more. Includes a variety of winning strategies, practice tips, and helpful checklists to minimize damages and maximize collectability.

Predictive Ability of Asymmetric Volatility Models At Medium-Term Horizons

Predictive Ability of Asymmetric Volatility Models At Medium-Term Horizons

  • Author: Turgut Kisinbay
  • Publisher: International Monetary Fund
  • ISBN: 1451900570
  • Category: Business & Economics
  • Page: 38
  • View: 797
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Using realized volatility to estimate conditional variance of financial returns, we compare forecasts of volatility from linear GARCH models with asymmetric ones. We consider horizons extending to 30 days. Forecasts are compared using three different evaluation tests. With data from an equity index and two foreign exchange returns, we show that asymmetric models provide statistically significant forecast improvements upon the GARCH model for two of the datasets and improve forecasts for all datasets by means of forecasts combinations. These results extend to about 10 days in the future, beyond which the forecasts are statistically inseparable from each other.