Добірка наукової літератури з теми "Temporal heteroscedasticity"
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Статті в журналах з теми "Temporal heteroscedasticity"
Sall’, M. A. "Climate risks: Temporal trends and heteroscedasticity." Russian Meteorology and Hydrology 40, no. 7 (July 2015): 489–94. http://dx.doi.org/10.3103/s1068373915070080.
Повний текст джерелаZhang, Xiaolong. "Inventory control under temporal demand heteroscedasticity." European Journal of Operational Research 182, no. 1 (October 2007): 127–44. http://dx.doi.org/10.1016/j.ejor.2006.06.057.
Повний текст джерелаFouladi, Seyyed Hamed, Ilangko Balasingham, Kimmo Kansanen, and Tor Audun Ramstad. "Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity." IEEE Access 6 (2018): 25336–50. http://dx.doi.org/10.1109/access.2018.2823381.
Повний текст джерелаZhang, Lyuou, Wen Zhou, and Haonan Wang. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity." Journal of Multivariate Analysis 186 (November 2021): 104786. http://dx.doi.org/10.1016/j.jmva.2021.104786.
Повний текст джерелаElshall, Ahmed S., Ming Ye, Guo-Yue Niu, and Greg A. Barron-Gafford. "Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy." Geoscientific Model Development 12, no. 5 (May 23, 2019): 2009–32. http://dx.doi.org/10.5194/gmd-12-2009-2019.
Повний текст джерелаNagahara, Ryu, and Jean-Benoit Morin. "Sensor insole for measuring temporal variables and vertical force during sprinting." Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology 232, no. 4 (January 19, 2018): 369–74. http://dx.doi.org/10.1177/1754337117751730.
Повний текст джерелаLei, Bolin, Boyu Zhang, and Yuping Song. "Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model." Mathematics 9, no. 4 (February 5, 2021): 320. http://dx.doi.org/10.3390/math9040320.
Повний текст джерелаSouza, Eniuce Menezes de, Dário Sodré, Isabella Harumi Yonehara Noma, Cinthia Akemi Tanoshi, and Raissa Bocchi Pedroso. "Trend change estimation for interrupted time series with heteroscedastic and autocorrelated errors: application in syphilis occurrences in Brazil." Acta Scientiarum. Technology 44 (May 25, 2022): e59513. http://dx.doi.org/10.4025/actascitechnol.v44i1.59513.
Повний текст джерелаUddin, Mohammad Ahsan, ASM Maksud Kamal, Shamsuddin Shahid, and Eun-Sung Chung. "Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh." Sustainability 12, no. 23 (November 24, 2020): 9810. http://dx.doi.org/10.3390/su12239810.
Повний текст джерелаGarcía-Pinillos, Felipe, Diego Jaén-Carrillo, Victor Soto Hermoso, Pedro Latorre Román, Pedro Delgado, Cristian Martinez, Antonio Carton, and Luis Roche Seruendo. "Agreement Between Spatiotemporal Gait Parameters Measured by a Markerless Motion Capture System and Two Reference Systems—a Treadmill-Based Photoelectric Cell and High-Speed Video Analyses: Comparative Study." JMIR mHealth and uHealth 8, no. 10 (October 23, 2020): e19498. http://dx.doi.org/10.2196/19498.
Повний текст джерелаДисертації з теми "Temporal heteroscedasticity"
Gomes, Daniel Takata. "Modelos GAS com distribuições estáveis para séries temporais financeiras." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-11012018-171109/.
Повний текст джерелаGARCH models with normal and t-Student conditional distributions are widely used for volatility modeling in financial data. However, such distributions may not be suitable for some heavy-tailed and leptokurtic series. The stable distributions may be more adequate to fit such characteristics, as already exploited in the literature. On the other hand, the recently developed GAS (Generalized Autoregressive Score) models are dynamic models in which the updating mechanism of the time-varying parameters is based on the score function (first derivative of the log-likelihood function). This provides the natural direction for updating the parameters, based on the complete density. We propose a new GAS model with symmetric stable distribution for volatility modeling. The model can be interpreted as a generalization of the GARCH models, since the classic gaussian GARCH model is derived from it by using particular choices of the stable distribution and the model structure. There are no closed analytical expressions for general stable densities in most cases, hence its numeric computation and derivatives are detailed for the sake of complete development of the estimation process. The stationarity conditions and the dependence structure of the model are analysed. Simulation studies, as well as an application to real data, are presented for comparisons between the usual models and the proposed model, illustrating the effectiveness of the latter.
Santos, Julio Cesar Grimalt dos. "Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/13521.
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O objetivo deste estudo é propor a implementação de um modelo estatístico para cálculo da volatilidade, não difundido na literatura brasileira, o modelo de escala local (LSM), apresentando suas vantagens e desvantagens em relação aos modelos habitualmente utilizados para mensuração de risco. Para estimação dos parâmetros serão usadas as cotações diárias do Ibovespa, no período de janeiro de 2009 a dezembro de 2014, e para a aferição da acurácia empírica dos modelos serão realizados testes fora da amostra, comparando os VaR obtidos para o período de janeiro a dezembro de 2014. Foram introduzidas variáveis explicativas na tentativa de aprimorar os modelos e optou-se pelo correspondente americano do Ibovespa, o índice Dow Jones, por ter apresentado propriedades como: alta correlação, causalidade no sentido de Granger, e razão de log-verossimilhança significativa. Uma das inovações do modelo de escala local é não utilizar diretamente a variância, mas sim a sua recíproca, chamada de 'precisão' da série, que segue uma espécie de passeio aleatório multiplicativo. O LSM captou todos os fatos estilizados das séries financeiras, e os resultados foram favoráveis a sua utilização, logo, o modelo torna-se uma alternativa de especificação eficiente e parcimoniosa para estimar e prever volatilidade, na medida em que possui apenas um parâmetro a ser estimado, o que representa uma mudança de paradigma em relação aos modelos de heterocedasticidade condicional.
The objective of this study is to propose the implementation of a statistical model to calculate the volatility not widespread in Brazilian literature, LSM, with its advantages and disadvantages compared to the models commonly used for risk measurement. To estimate the parameters will be used daily prices of Ibovespa in the period from January 2009 to December 2014, and to measure the empirical accuracy of the models out of sample tests will be performed, comparing the VaR obtained for the period from January to December 2014. Explanatory variables were introduced in an attempt to improve the models, and we chose to its corresponding American Ibovespa, the Dow Jones index, for presenting characteristics such as high correlation, causality in the Granger sense, and reason for significant log-likelihood. One of the local scale model innovation is not directly use the variance, but its reciprocal, called 'precision' series, which follows a kind of multiplicative random walk. LSM captured all financial series of stylized facts, and the results were favorable to use, so the model becomes an efficient and economical alternative specification for estimating and predicting volatility, to the extent that only one parameter has to be estimated, which represents a paradigm shift in the models of conditional heteroscedasticity.