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Predicting extreme VaR : nonparametric quantile regression with refinements from extreme value theory
Schaumburg, Julia - 2010
extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different … management ; extreme value theory ; monotonization ; CAViaR …
Persistent link: https://www.econbiz.de/10003952845
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Varying coefficient GARCH versus local constant volatility modeling : comparison of the predictive power
Polzehl, Jörg (contributor);  … - 2006
Persistent link: https://www.econbiz.de/10003324453
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Splines for Financial Volatility
Audrino, Francesco; Buhlmann, Peter - 2007
We propose a flexible GARCH-type model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B-splines of lagged observations and volatilities. Estimation of such a B-spline basis expansion is constructed within the likelihood framework...
Persistent link: https://www.econbiz.de/10014051065
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Package CovRegpy : Regularised Covariance Regression and Forecasting in Python
van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023
This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...
Persistent link: https://www.econbiz.de/10014253907
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Evaluating density forecasts with an application to stock market returns
Raaij, Gabriela de; Raunig, Burkhard - 2002
Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...
Persistent link: https://www.econbiz.de/10011431370
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Quantile Smoothing in Supply Chain and Logistics Forecasting
Bruzda, Joanna - 2018
In this paper, we re-examine the exponential smoothing model in the context of its use in supply chain and logistics … forecasting when supply chain or logistics costs are measured by the doubly linear (LINLIN) loss function of errors of demand … better quantile forecasts than both standard econometric approaches and popular methods from the practice of logistics …
Persistent link: https://www.econbiz.de/10012932817
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The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk
Dobrev, Dobrislav - 2011
We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...
Persistent link: https://www.econbiz.de/10013128339
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Machine Learning and Forecast Combination in Incomplete Panels
Lahiri, Kajal - 2013
This paper focuses on a number of newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish certain asymptotic properties of these algorithms and compare them with the Bates and Granger (1969) method. We then show that...
Persistent link: https://www.econbiz.de/10013072496
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Empirical Risk Minimization for Time Series : Nonparametric Performance Bounds for Prediction
Brownlees, Christian T.; Llorens-Terrazas, Jordi - 2021
Empirical risk minimization is a standard principle for choosing algorithms in learning theory. In this paper we study …
Persistent link: https://www.econbiz.de/10013216191
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A Simple Model Correction for Modelling and Forecasting (Un)Reliable Realized Volatility
Hizmeri, Rodrigo - 2020
We propose a dilution bias correction approach to deal with the errors-in-variables problem observed in realized volatility (RV) measures. The absolute difference between daily and monthly RV is shown to be proportional to the relative magnitude of the measurement error. Therefore, in...
Persistent link: https://www.econbiz.de/10012829634