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Dynamic factor GARCH: Multivariate volatility forecast for a large number of series
Alessi, Lucia; Barigozzi, Matteo; Capasso, Marco - 2007
multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are … dynamic factors governing the data set. A time-varying correlation GARCH model applied on the estimated dynamic factors finds … Factor GARCH model outperforms the univariate GARCH. …
Persistent link: https://www.econbiz.de/10010328519
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Generalized dynamic factor model + GARCH exploiting multivariate information for univariate prediction
Alessi, Lucia (contributor); Barigozzi, Matteo (contributor) - 2007
GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the … a GARCH.We compare GDFM+GARCH and standard GARCH performance on two samples up to 171 series, providing one …-step-ahead volatility predictions of returns. The GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust …
Persistent link: https://www.econbiz.de/10003321460
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Dynamic factor GARCH : multivariate volatility forecast for a large number of series
Alessi, Lucia (contributor); Barigozzi, Matteo (contributor) - 2007
multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are … dynamic factors governing the data set. A time-varying correlation GARCH model applied on the estimated dynamic factors finds … Factor GARCH model outperforms the univariate GARCH. -- Dynamic Factors ; Multivariate GARCH ; Covolatility Forecasting …
Persistent link: https://www.econbiz.de/10003376231
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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
validate this result. The last twenty eight days out-of-sample forecast adjudged Power-GARCH (1, 1, 1) in student's t error …
Persistent link: https://www.econbiz.de/10011489480
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Value-at-risk predictive performance : a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
Serrano Bautista, Ramona; Núñez Mora, José Antonio - In: Journal of economics, finance & administrative science 26 (2021) 52, pp. 197-221
Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed …
Persistent link: https://www.econbiz.de/10012813839
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Combination of multivariate volatility forecasts
Amendola, Alessandra; Storti, Giuseppe - 2009
volatility predictor, the results of an application to tactical asset allocation are presented. -- Multivariate GARCH ; forecast …
Persistent link: https://www.econbiz.de/10003796201
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Combination of multivariate volatility forecasts
Amendola, Alessandra; Storti, Giuseppe - 2009
This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite...
Persistent link: https://www.econbiz.de/10010263760
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Forecasting Expected Shortfall : Should We Use a Multivariate Model for Stock Market Factors?
Fortin, Alain-Philippe - 2019
Is univariate or multivariate modelling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead Expected Shortfall of a portfolio invested in the Fama-French and momentum factors. Apply ingextensive tests and...
Persistent link: https://www.econbiz.de/10012898954
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Financial Volatility Forecasting
Vafopoulos, Michalis N. - 2011
The volatility of equity and foreign exchange market is an important input to portfolio selection and to asset pricing models. Many investment decisions and valuation of derivatives frequently rely on predictions of volatility. In this paper we review the existing empirical literature in...
Persistent link: https://www.econbiz.de/10013122403
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Forecasting Realized Intra-Day Volatility and Value at Risk : Evidence From a Fractional Integrated Asymmetric Power ARCH Skewed-T Model
Degiannakis, Stavros Antonios - 2018
Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an asymmetric Autoregressive Conditional Heteroscedasticity...
Persistent link: https://www.econbiz.de/10012910129