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Asymmetries in conditional mean and variance : modelling stock returns by asMA-asQGARCH
Brännäs, Kurt; Gooijer, Jan G. de - 2000
The asymmetric moving average model (asMA) is extended to allow forasymmetric quadratic conditional heteroskedasticity (asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We introduce a framework fortesting asymmetries in...
Persistent link: https://www.econbiz.de/10011303289
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Score driven exponentially weighted moving averages and value-at-risk forecasting
Lucas, André; Zhang, Xin - 2015
A simple methodology is presented for modeling time variation in volatilities and other higher-order moments using a recursive updating scheme similar to the familiar RiskMetricsTM approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics...
Persistent link: https://www.econbiz.de/10011332948
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Score driven exponentially weighted moving average and value-at-risk forecasting
Lucas, André; Zhang, Xin - 2014
We present a simple new methodology to allow for time-variation in volatilities using a recursive updating scheme similar to the familiar RiskMetrics approach. It exploits the link between exponentially weighted moving average and integrated dynamics of score driven time varying parameter...
Persistent link: https://www.econbiz.de/10010384110
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Dynamic Conditional Correlation : On Properties and Estimation
Aielli, Gian Piero - 2011
We address some issues that arise with the Dynamic Conditional Correlation (DCC) model. We prove that the DCC large system estimator (DCC estimator) can be inconsistent, and that the traditional interpretation of the DCC correlation parameters can lead to misleading conclusions. We then suggest...
Persistent link: https://www.econbiz.de/10013134164
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Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators
Aielli, Gian Piero - 2011
It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...
Persistent link: https://www.econbiz.de/10013125314
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Financial Risk Management with Bayesian Estimation of GARCH Models : Theory and Applications
Ardia, David - 2009
This book presents in detail methodologies for the Bayesian estimation of single-regime and regime-switching GARCH … estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach offers an … the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal …
Persistent link: https://www.econbiz.de/10013156202
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Volatility Estimation and Forecasts Based on Price Durations
Hong, Seok Young - 2020
We investigate price duration variance estimators that have long been neglected in the literature. We show i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and ii) how they are affected by a)...
Persistent link: https://www.econbiz.de/10012855793
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A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation
Tofallis, Chris - 2015
Surveys show that the mean absolute percentage error (MAPE) is the most widely used measure of forecast accuracy in businesses and organizations. It is however, biased: When used to select among competing prediction methods it systematically selects those whose predictions are too low. This is...
Persistent link: https://www.econbiz.de/10013018861
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A penalized two-pass regression to predict stock returns with time-varying risk premia
Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier - 2021
We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...
Persistent link: https://www.econbiz.de/10012487589
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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