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Relevant bibliographies by topics / Volatilité multivariée

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Academic literature on the topic 'Volatilité multivariée'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Volatilité multivariée"

1

Asai, Manabu, and Michael McAleer. "Asymmetric Multivariate Stochastic Volatility." Econometric Reviews 25, no. 2-3 (September 2006): 453–73. http://dx.doi.org/10.1080/07474930600712913.

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Hoti, Suhejla, Michael McAleer, and Laurent L. Pauwels. "Multivariate volatility in environmental finance." Mathematics and Computers in Simulation 78, no. 2-3 (July 2008): 189–99. http://dx.doi.org/10.1016/j.matcom.2008.01.038.

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Maasoumi, Esfandiar, and Michael McAleer. "Multivariate Stochastic Volatility: An Overview." Econometric Reviews 25, no. 2-3 (September 2006): 139–44. http://dx.doi.org/10.1080/07474930600712806.

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Asai, Manabu, Michael McAleer, and Jun Yu. "Multivariate Stochastic Volatility: A Review." Econometric Reviews 25, no. 2-3 (September 2006): 145–75. http://dx.doi.org/10.1080/07474930600713564.

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Caporin, Massimiliano, and Paolo Paruolo. "Proximity-Structured Multivariate Volatility Models." Econometric Reviews 34, no. 5 (November 10, 2014): 559–93. http://dx.doi.org/10.1080/07474938.2013.807102.

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Dovonon, Prosper, Sílvia Gonçalves, and Nour Meddahi. "Bootstrapping realized multivariate volatility measures." Journal of Econometrics 172, no. 1 (January 2013): 49–65. http://dx.doi.org/10.1016/j.jeconom.2012.08.003.

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7

Bensafta, Kamel Malik, and Gervasio Semedo. "De la transmission de la volatilité à la contagion entre marchés boursiers : l’éclairage d’un modèle VAR non linéaire avec bris structurels en variance." Articles 85, no. 1 (May 18, 2010): 13–76. http://dx.doi.org/10.7202/039734ar.

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Résumé Nous développons dans cet article une modélisation vectorielle autorégressive non linéaire pour l’étude des interdépendances entre les marchés boursiers. Parmi les innovations de ce travail, nous introduisons un bris structurel dans la matrice des variances-covariances conditionnelle d’un processus GARCH multivarié. Dans cet ordre d’idée, nous considérons une spécification BEKK de cette matrice augmentée avec des régresseurs de transmission des chocs de volatilité entre les marchés. L’objectif de cette modification est de répondre à plusieurs biais importants dans la mesure des volatilités et des corrélations entre les marchés : d’une part, le biais de surestimation de la persistance des chocs de volatilité; d’autre part, les biais d’hétéroscédasticité et de variables omises dans la mesure des corrélations. Nous considérons ici un échantillon de 11 marchés boursiers d’Europe, d’Amérique du Nord et d’Asie avec des données hebdomadaires des indices les plus larges entre 1985 et 2006. Plusieurs résultats intéressants sont obtenus avec cette modélisation : la réduction de la persistance des chocs de volatilité; l’évidence d’une transmission des prix et des incertitudes du marché américain vers les marchés européens et asiatiques; l’existence de phénomène de transmission régionale en Europe et en Asie; mis à part le krach américain d’octobre 1987, toutes les crises ne sont pas systématiquement contagieuses. Au final, il n’est pas évident que la libéralisation financière isole les marchés des crises financières diverses, bien que l’intégration soit un vecteur d’efficience des marchés. Les crises et le phénomène de contagion en période de crise peuvent être considérés comme des processus de rééquilibrage des marchés qui doivent être encadrés, régulés et supervisés.

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Vo, Minh. "Oil and stock market volatility: A multivariate stochastic volatility perspective." Energy Economics 33, no. 5 (September 2011): 956–65. http://dx.doi.org/10.1016/j.eneco.2011.03.005.

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Haug, Stephan, and Robert Stelzer. "MULTIVARIATE ECOGARCH PROCESSES." Econometric Theory 27, no. 2 (September 13, 2010): 344–71. http://dx.doi.org/10.1017/s0266466610000289.

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A multivariate extension of the exponential continuous time GARCH (p, q) model (ECOGARCH) is introduced and studied. Stationarity and mixing properties of the new stochastic volatility model are investigated, and ways to model a component-wise leverage effect are presented.

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So, Mike K. P., and C. Y. Choi. "A multivariate threshold stochastic volatility model." Mathematics and Computers in Simulation 79, no. 3 (December 2008): 306–17. http://dx.doi.org/10.1016/j.matcom.2007.12.003.

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Dissertations / Theses on the topic "Volatilité multivariée"

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Choi, Chiu Yee. "A multivariate threshold stochastic volatility model /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?MATH%202005%20CHOI.

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Bongers, Martin B. "Multivariate volatility modelling in modern finance." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/4373.

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Includes abstract.
Includes bibliographical references (leaves 100-101).
The aim of the study is to ascertain whether the information gained from the more complicated multivariate matrix decomposition models can be used to better forecast the covariance matrix and produce a Value at Risk estimate which more appropriately describes fat-tailed financial time-series.

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Marchese, Malvina. "Whittle estimation of multivariate exponential volatility models." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3173/.

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The aim of this thesis is to offer some insights into two topics of some interest for time-series econometric research. The first chapter derives the rates of convergence and the asymptotic normality of the pooled OLS estimators for linear regression panel models with mixed stationary and non-stationary regressors. This work is prompted by the consideration that many economic models of interest present a mixture of I(1) and I(0) regressors, for example models for analysis of demand system or for assessment of the relationship between growth and inequality. We present results for a model where the regressors and the regressand are cointegrated. We find that the OLS estimator is asymptotically normal with convergence rates T p n and p nT for respectively the non-stationary and the stationary regressors. Phillips and Moon (1990) show that in a cointegrated regression model with non-stationary regressors, the OLS estimator converges at a rate of T p n. We find that the presence of one stationary regressor in the model does not increases the rate of convergence. All the results are derived for sequential limits, with T going to infinity followed by n; and under quite restrictive regularity conditions. Chapters 3-5 focus on parametric multivariate exponential volatility models. It has long been recognized that the volatility of stock returns responds differently to good news and bad news. In particular, while negative shocks tend to increase future volatility, positive ones of the same size will increase it by less or even decrease it. This was in fact one of the chief motivations that led Nelson (1991) to introduce the univariate EGARCH model. More recently empirical studies have found that the asymmetry is a robust feature of multivariate stock returns series as well, and several multivariate volatility models have been developed to capture it. Another important property that characterizes the dynamic evolution of volatilities is that squared returns have significant autocorrelations that decay to zero at a slow rate, consistent with the notion of long memory, where the auto-covariances are not absolutely summable. Univariate long-memory volatility models have received a great deal of attention. However, the generalization to a multivariate long-memory volatility model has not been attempted in the literature. Chapter 3 offers a detailed literature review on multivariate volatility models. Chapter 4 and 5 introduce a new multivariate exponential volatility (MEV) model which captures long-range dependence in the volatilities, while retaining the martingale difference assumption and short-memory dependence in mean. Moreover the model captures cross-assets spillover effects, leverage and asymmetry. The strong consistency and the asymptotic normality of the Whittle estimator of the parameters in the Multivariate Exponential Volatility model is established under a variety of parameterization. The results cover both the case of exponentially and hyperbolically decaying coefficients, allowing for different degrees of persistence of shocks to the conditional variances. It is shown that the rate of convergence and the asymptotic normality of the Whittle estimates do not depend on the degree of persistence implied by the parameterization as the Whittle function automatically compensates for the possible lack of square integrability of the model spectral density.

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Eratalay, Mustafa Hakan. "Three essays on multivariate volatility modelling and estimation." Doctoral thesis, Universidad de Alicante, 2012. http://hdl.handle.net/10045/26482.

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Wang, Jian. "Real time estimation of multivariate stochastic volatility models." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/16786/.

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This thesis firstly considers a modelling framework for multivariate volatility in financial time series. As most financial returns exhibit heavy tails and skewness, we are considering a model for the returns based on the skew-t distribution, while the volatility is assumed to follow a Wishart autoregressive process. We define a new type of Wishart autoregressive process and highlight some of its properties and some of its advantages. Particle filter based inference for this model is discussed and a novel approach of estimating static parameters is provided. Furthermore, an alternative methodology for estimating higher dimension data is developed. Secondly, inspired from the idea of Ulig's Wishart process, a new Wishart-Newton model is developed. The approach combines conjugate Bayesian inference while the hyper parameters are estimated by a Newton-Raphson method and here an online volatility estimate algorithm is proposed. The two proposed models are compared with the benchmarking GO-GARCH model in both function execution time and cumulative returns of different dimensional datasets.

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Platanioti, Kiriaki. "Inference for multivariate stochastic volatility and related models." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501781.

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Loddo, Antonello. "Bayesian analysis of multivariate stochastic volatility and dynamic models." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4359.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (April 26, 2007) Vita. Includes bibliographical references.

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Vestweber, Johanna [Verfasser]. "Geometric ergodicity of multivariate stochastic volatility models / Johanna Vestweber." Ulm : Universität Ulm, 2018. http://d-nb.info/1151938378/34.

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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4875/1/research_report_updated.pdf.

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We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.
Series: Research Report Series / Department of Statistics and Mathematics

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Gribisch, Bastian [Verfasser]. "Modeling and Forecasting of Multivariate Stock Market Volatility / Bastian Gribisch." Kiel : Universitätsbibliothek Kiel, 2013. http://d-nb.info/1031914897/34.

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Books on the topic "Volatilité multivariée"

1

King, Mervyn A. Volatility and links between national stock markets. Cambridge, MA: National Bureau of Economic Research, 1990.

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King, Mervyn A. Volatility and links between national stock markets. London: London School of Economics, Financial Markets Group, 1993.

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3

Chevallier, Julien, Stéphane Goutte, David Guerreiro, Sophie Saglio, and Bilel Sanhaji, eds. Financial Mathematics, Volatility And Covariance Modelling: Volume 2. Milton, Cambridge, UK: Routledge, 2019.

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Book chapters on the topic "Volatilité multivariée"

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Tsay, Ruey S. "Multivariate volatility models." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 210–22. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2006. http://dx.doi.org/10.1214/074921706000001058.

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Fengler, Matthias R., and Helmut Herwartz. "Multivariate Volatility Models." In Applied Quantitative Finance, 221–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-05021-7_10.

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Chib, Siddhartha, Yasuhiro Omori, and Manabu Asai. "Multivariate Stochastic Volatility." In Handbook of Financial Time Series, 365–400. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_16.

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4

Fengler, Matthias R., and Helmut Herwartz. "Multivariate Volatility Models." In Applied Quantitative Finance, 313–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69179-2_15.

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5

Fengler, M. R., H. Herwartz, and F. H. C. Raters. "Multivariate Volatility Models." In Applied Quantitative Finance, 25–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54486-0_2.

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6

Cipra, Tomas. "Multivariate Volatility Modeling." In Time Series in Economics and Finance, 351–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46347-2_13.

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Hunter, John, Simon P. Burke, and Alessandra Canepa. "Heteroscedasticity and Multivariate Volatility." In Multivariate Modelling of Non-Stationary Economic Time Series, 305–38. London: Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-31303-4_7.

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Amendola, Alessandra, Manuela Braione, Vincenzo Candila, and Giuseppe Storti. "Combining Multivariate Volatility Models." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 39–43. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_7.

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Omori, Yasuhiro, and Tsunehiro Ishihara. "Multivariate Stochastic Volatility Models." In Handbook of Volatility Models and Their Applications, 175–97. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch7.

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Cubadda, Gianluca, Alain Hecq, and Antonio Riccardo. "Forecasting realized volatility measures with multivariate and univariate models." In Financial Mathematics, Volatility and Covariance Modelling, 286–307. Abingdon, Oxon ; New York, NY : Routledge, 2019. | Series: Routledge advances in applied financial econometrics ; Volume 2: Routledge, 2019. http://dx.doi.org/10.4324/9781315162737-12.

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Conference papers on the topic "Volatilité multivariée"

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Allen, David, Michael McAleer, Robert Powell, and Abhay Singh. "Volatility Spillover and Multivariate Volatility Impulse Response Analysis of GFC News Events." In 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icefs-17.2017.9.

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Du, Jun, and Yang Liu. "Credit Risk Pricing with Multivariate Stochastic Volatility." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.50.

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Basturk, Nalan, Rui Jorge Almeida, Robert Golan, and Uzay Kaymak. "Multivariate time-varying volatility modeling using Probabilistic Fuzzy Systems." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850011.

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Zhang, Jing, and Yaming Zhuang. "Volatility spillover among USA and major East Asian stock indices based on multivariate stochastic volatility with regime-switching model." In 2017 17th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2017. http://dx.doi.org/10.23919/iccas.2017.8204399.

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Ho, Kin Yip. "Volatility Dynamics of the Greater China Stock Markets: A Multivariate Asymmetric Approach." In 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.74.

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Asai, Manabu, and Michael McAleer. "A Multivariate Asymmetric Long Memory Conditional Volatility Model with X, Regularity and Asymptotics." In 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icefs-17.2017.1.

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Ahmad, Zuriyati, and Tuan Nadiah Tuan Razilah. "Gauging Stock Price Volatility during the Financial Crisis using a Multivariate Cointegration Analysis." In Economics and Business International Conference 2019. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0009327005850590.

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"A volatility impulse response analysis applying multivariate GARCH models and news events around the GFC." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.e4.allen.

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"Modeling the Conditional Volatility Asymmetry of business cycles in four OECD countries: A multivariate GARCH approach." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.d8.ho.

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Souza, Adriano, and Rui Menezes. "An alternative method for the evaluation of a multivariate productive process in the presence of volatility." In Industrial Engineering (CIE-40). IEEE, 2010. http://dx.doi.org/10.1109/iccie.2010.5668307.

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Reports on the topic "Volatilité multivariée"

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Baldivieso, Sebastian. Sensitivity Diagnostics and Adaptive Tuning of the Multivariate Stochastic Volatility Model. Portland State University Library, February 2020. http://dx.doi.org/10.15760/etd.7296.

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