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High Frequency vs. Daily Resolution : The Economic Value of Forecasting Volatility Models
Lilla, Francesca - 2017
Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …
Persistent link: https://www.econbiz.de/10012958968
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High frequency vs. daily resolution : the economic value of forecasting volatility models 2nd ed
Lilla, Francesca - 2017
Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …
Persistent link: https://www.econbiz.de/10011674479
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High frequency vs. daily resolution : the economic value of forecasting volatility models
Lilla, Francesca - 2016
Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …
Persistent link: https://www.econbiz.de/10011730304
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High Frequency vs. Daily Resolution : The Economic Value of Forecasting Volatility Models
Lilla, Francesca - 2016
Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …
Persistent link: https://www.econbiz.de/10014124325
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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
Covariance Regression (RCR) methods to produce a framework termed in this work: the EMD-RCR covariance forecasting model. This … will produce a model framework capable of generating multi-time resolution adaptive forecasting models of predictive …
Persistent link: https://www.econbiz.de/10014348857
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Asymmetries in conditional mean and variance : modelling stock returns by asMA-asQGARCH
Brännäs, Kurt; Gooijer, Jan G. de - 2000
variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared … topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk … forecasting. …
Persistent link: https://www.econbiz.de/10011303289
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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction
Duong, Diep; Swanson, Norman R. - 2013
forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen …
Persistent link: https://www.econbiz.de/10009771770
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Testing volatility in Nigeria stock market using GARCH models
Atoi, Ngozi V. - In: CBN journal of applied statistics 5 (2014) 2, pp. 65-93
forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … forecasting model that could guarantee a sound policy decisions. …
Persistent link: https://www.econbiz.de/10011489480
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Realizing Stock Market Crashes : Stochastic Cusp Catastrophe Model of Returns under the Time-Varying Volatility
Baruník, Jozef - 2018
This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily realized volatility from the returns in the first step and...
Persistent link: https://www.econbiz.de/10012938546
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Realizing stock market crashes : stochastic cusp catastrophe model of returns under the time-varying volatility
Baruník, Jozef; Kukačka, Jiří - 2013
This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily realized volatility from the returns in the first step and...
Persistent link: https://www.econbiz.de/10010206135