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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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Hybrid historical simulation VaR and ES : performance in developed and emerging markets
Zikovic, Sasa; Filer, Randall Keith - 2009
; extreme value theory ; bootstrapping …
Persistent link: https://www.econbiz.de/10003891679
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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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Quantile regression in risk calibration
Chao, Shih-Kang; Härdle, Wolfgang; Wang, Weining - 2012
Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a...
Persistent link: https://www.econbiz.de/10009425497
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Quantile Regression in Risk Calibration
Chao, Shih-Kang - 2017
Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a...
Persistent link: https://www.econbiz.de/10012966323
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Long/Short Equity Risk Premia Parity Portfolios via Implicit Factors in Regularized Covariance Regression
van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023
A time-series basis decomposition and trend extraction technique known as Empirical Mode Decomposition (EMD), designed for multi-scale time-frequency decomposition in non-stationary time-series settings, will be combined with Regularised Covariance Regression (RCR) methods to produce a framework...
Persistent link: https://www.econbiz.de/10014348857