Literatura científica selecionada sobre o tema "Multiplicative Component GARCH"
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Artigos de revistas sobre o assunto "Multiplicative Component GARCH"
Diao, Xundi, e Bin Tong. "Forecasting intraday volatility and VaR using multiplicative component GARCH model". Applied Economics Letters 22, n.º 18 (27 de abril de 2015): 1457–64. http://dx.doi.org/10.1080/13504851.2015.1039696.
Texto completo da fonteNieto, Belén, Alfonso Novales e Gonzalo Rubio. "Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns". Quarterly Journal of Finance 05, n.º 04 (dezembro de 2015): 1550021. http://dx.doi.org/10.1142/s2010139215500214.
Texto completo da fonteSumminga-Sonagadu, Ravi, e Jason Narsoo. "Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH". Risks 7, n.º 1 (23 de janeiro de 2019): 10. http://dx.doi.org/10.3390/risks7010010.
Texto completo da fonteConrad, Christian, e Melanie Schienle. "Testing for an Omitted Multiplicative Long-Term Component in GARCH Models". Journal of Business & Economic Statistics 38, n.º 2 (7 de setembro de 2018): 229–42. http://dx.doi.org/10.1080/07350015.2018.1482759.
Texto completo da fonteEngle, R. F., e M. E. Sokalska. "Forecasting intraday volatility in the US equity market. Multiplicative component GARCH". Journal of Financial Econometrics 10, n.º 1 (28 de dezembro de 2011): 54–83. http://dx.doi.org/10.1093/jjfinec/nbr005.
Texto completo da fonteConrad, Christian, e Onno Kleen. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models". Journal of Applied Econometrics 35, n.º 1 (janeiro de 2020): 19–45. http://dx.doi.org/10.1002/jae.2742.
Texto completo da fonteBadaye, Hemant Kumar, e Jason Narsoo. "Forecasting multivariate VaR and ES using MC-GARCH-Copula model". Journal of Risk Finance 21, n.º 5 (27 de janeiro de 2020): 493–516. http://dx.doi.org/10.1108/jrf-06-2019-0114.
Texto completo da fonteChanda, Ananda, Robert F. Engle e Magdalena Sokalska. "High Frequency Multiplicative Component GARCH". SSRN Electronic Journal, 2005. http://dx.doi.org/10.2139/ssrn.686173.
Texto completo da fonteAbebe, Teshome Hailemeskel, Emmanuel Gabreyohannes Woldesenbet e Belaineh Legesse Zeleke. "Statistical Analysis of Price Volatility of Agricultural Commodities Traded at the Ethiopian Commodity Exchange (ECX) Using Multiplicative GARCH-MIDAS Two-component Model". Global Business Review, 12 de fevereiro de 2020, 097215091989562. http://dx.doi.org/10.1177/0972150919895628.
Texto completo da fonteThanh, Tran Quang, e Trinh Quang Khai. "Comparison of ARIMA and ARIMA/GARCH Models in EVN Traffic Prediction". Journal of Research and Development on Information and Communication Technology, 28 de outubro de 2014, 71. http://dx.doi.org/10.32913/mic-ict-research.v3.n11.304.
Texto completo da fonteTeses / dissertações sobre o assunto "Multiplicative Component GARCH"
Kandji, Baye Matar. "Stochastic recurrent equations : structure, statistical inference, and financial applications". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG004.
Texto completo da fonteWe are interested in the theoretical properties of Stochastic Recurrent Equations (SRE) and their applications in finance. These models are widely used in econometrics, including financial econometrics, to explain the dynamics of various processes such as the volatility of financial returns. However, the probability structure and statistical properties of these models are still not well understood, especially when the model is considered in infinite dimensions or driven by non-independent processes. These two features lead to significant difficulties in the theoretical study of these models. In this context, we aim to explore the existence of stationary solutions and the statistical and probabilistic properties of these solutions.We establish new properties on the trajectory of the stationary solution of SREs, which we use to study the asymptotic properties of the quasi-maximum likelihood estimator (QMLE) of GARCH-type (generalized autoregressive conditional heteroskedasticity) conditional volatility models. In particular, we study the stationarity and statistical inference of semi-strong GARCH(p,q) models where the innovation process is not necessarily independent. We establish the consistency of the QMLE of semi-strong GARCHs without assuming the commonly used condition that the stationary distribution admits a small-order moment. In addition, we are interested in the two-factor volatility GARCH models (GARCH-MIDAS); a long-run, and a short-run volatility. These models were recently introduced by Engle et al. (2013) and have the particularity to admit stationary solutions with heavy-tailed distributions. These models are now widely used but their statistical properties have not received much attention. We show the consistency and asymptotic normality of the QMLE of the GARCH-MIDAS models and provide various test procedures to evaluate the presence of long-run volatility in these models. We also illustrate our results with simulations and applications to real financial data.Finally, we extend a result of Kesten (1975) on the growth rate of additive sequences to superadditive processes. From this result, we derive generalizations of the contraction property of random matrices to products of stochastic operators. We use these results to establish necessary and sufficient conditions for the existence of stationary solutions of the affine case with positive coefficients of SREs in the space of continuous functions. This class of models includes most conditional volatility models, including functional GARCHs
Mazibas, Murat. "Dynamic portfolio construction and portfolio risk measurement". Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3297.
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