Open AccessPosted Content
Evaluating Density Forecasts with Applications to Financial Risk Management
TLDR
In this paper, a simple and operational framework for density forecast evaluation is developed for asset returns in environments with time-varying volatility, and several extensions to the framework are discussed.Abstract:
Density forecasting is increasingly more important and commonplace, forexample in financial risk management, yet little attention has been given to theevaluation of density forecasts. We develop a simple and operational frameworkfor density forecast evaluation. We illustrate the framework with adetailed application to density forecasting of asset returns in environments withtime-varying volatility. Finally, we discuss several extensions.read more
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Modeling and Forecasting Realized Volatility
TL;DR: In this article, the authors provide a general framework for integration of high-frequency intraday data into the measurement, modeling and forecasting of daily and lower frequency volatility and return distributions.
Journal ArticleDOI
Multivariate GARCH Models: A Survey
TL;DR: In this article, the most important developments in multivariate ARCH-type modeling are surveyed, including model specifications, inference methods, and the main areas of application in financial econometrics.
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Probabilistic forecasts, calibration and sharpness
TL;DR: In this paper, a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration is proposed, which is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest.
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25 years of time series forecasting
Jan G. De Gooijer,Rob J. Hyndman +1 more
TL;DR: A review of the past 25 years of research into time series forecasting can be found in this paper, where the authors highlight results published in journals managed by the International Institute of Forecasters.
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Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this article, a review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered.
References
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Posted Content
Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
Journal ArticleDOI
An Introduction to the Bootstrap
TL;DR: Statistical theory attacks the problem from both ends as discussed by the authors, and provides optimal methods for finding a real signal in a noisy background, and also provides strict checks against the overinterpretation of random patterns.
ReportDOI
Comparing Predictive Accuracy
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
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ARCH modeling in finance: A review of the theory and empirical evidence
TL;DR: An overview of some of the developments in the formulation of ARCH models and a survey of the numerous empirical applications using financial data can be found in this paper, where several suggestions for future research, including the implementation and tests of competing asset pricing theories, market microstructure models, information transmission mechanisms, dynamic hedging strategies, and pricing of derivative assets, are also discussed.