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  • Search: subject:"integrated generalized autoregressive score models"
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Year of publication
Subject
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Value-at-Risk (VaR) 6 dynamic volatilities 5 integrated generalized autoregressive score models 5 ARCH model 3 ARCH-Modell 3 Estimation 3 Estimation theory 3 Exponential Weighted Moving Average (EWMA) 3 Exponentially Weighted Moving Average (EWMA) 3 Forecasting model 3 Prognoseverfahren 3 Risikomaß 3 Risk measure 3 Schätztheorie 3 Schätzung 3 Stochastic process 3 Stochastischer Prozess 3 Time series analysis 3 Volatility 3 Volatilität 3 Zeitreihenanalyse 3 time varying higher order moments 3 VAR model 2 VAR-Modell 2 dynamic higher-order moments 2 Dynamic higher-order moments 1 Dynamic volatilities 1 Integrated generalized autoregressive score models 1 Portfolio selection 1 Portfolio-Management 1
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Online availability
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Free 5 Undetermined 1
Type of publication
All
Book / Working Paper 5 Article 1
Type of publication (narrower categories)
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Working Paper 4 Arbeitspapier 2 Graue Literatur 2 Non-commercial literature 2 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 5 Undetermined 1
Author
All
Lucas, André 6 Zhang, Xin 6
Institution
All
Tinbergen Instituut 1
Published in...
All
Discussion paper / Tinbergen Institute 1 International journal of forecasting 1 Sveriges Riksbank Working Paper Series 1 Sveriges Riksbank working paper series 1 Tinbergen Institute Discussion Paper 1 Tinbergen Institute Discussion Papers 1
Source
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ECONIS (ZBW) 3 EconStor 2 RePEc 1
Showing 1 - 6 of 6
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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/10011442899
Saved in:
Cover Image
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
Saved in:
Cover Image
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. We update parameters using the score of the forecasting distribution rather than squared lagged observations. This allows the parameter...
Persistent link: https://www.econbiz.de/10010491323
Saved in:
Cover Image
Score Driven exponentially Weighted Moving Average and Value-at-Risk Forecasting
Lucas, André; Zhang, Xin - Tinbergen Instituut - 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. We update parameters using the score of the forecasting distribution rather than squared lagged observations. This allows the parameter...
Persistent link: https://www.econbiz.de/10011257169
Saved in:
Cover Image
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
Saved in:
Cover Image
Score-driven exponentially weighted moving averages and Value-at-Risk forecasting
Lucas, André; Zhang, Xin - In: International journal of forecasting 32 (2016) 2, pp. 293-302
Persistent link: https://www.econbiz.de/10011596763
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