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Littérature scientifique sur le sujet « Extremal dependence modeling »

Auteur : Grafiati

Publié le 9 novembre 2024

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Articles de revues sur le sujet "Extremal dependence modeling"

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Barro, Diakarya. "Extremal Dependence Modeling with Spatial and Survival Distributions." Journal of Mathematics Research 9, no. 1 (2017): 127. http://dx.doi.org/10.5539/jmr.v9n1p127.

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This paper investigates some properties of dependence of extreme values distributions both in survival and spatial context. Specifically, we prospose a spatial Extremal dependence coefficient for survival distributions. Madogram is characterized in bivariate case and multivariate survival function and the underlying hazard distributions are given in a risky context.

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Huser, Raphaël, and Jennifer L. Wadsworth. "Modeling Spatial Processes with Unknown Extremal Dependence Class." Journal of the American Statistical Association 114, no. 525 (2018): 434–44. http://dx.doi.org/10.1080/01621459.2017.1411813.

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Mallam, Hassane Abba, Natatou Dodo Moutari, Barro Diakarya, and Saley Bisso. "Extremal Copulas and Tail Dependence in Modeling Stochastic Financial Risk." European Journal of Pure and Applied Mathematics 14, no. 3 (2021): 1057–81. http://dx.doi.org/10.29020/nybg.ejpam.v14i3.3951.

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These last years the stochastic modeling became essential in financial risk management related to the ownership and valuation of financial products such as assets, options and bonds. This paper presents a contribution to the modeling of stochastic risks in finance by using both extensions of tail dependence coefficients and extremal dependance structures based on copulas. In particular, we show that when the stochastic behavior of a set of risks can be modeled by a multivariate extremal process a corresponding form of the underlying copula describing theirdependence is determined. Moreover a n

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Apputhurai, P., and A. G. Stephenson. "Accounting for uncertainty in extremal dependence modeling using Bayesian model averaging techniques." Journal of Statistical Planning and Inference 141, no. 5 (2011): 1800–1807. http://dx.doi.org/10.1016/j.jspi.2010.11.038.

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Ressel, Paul. "Stable tail dependence functions – some basic properties." Dependence Modeling 10, no. 1 (2022): 225–35. http://dx.doi.org/10.1515/demo-2022-0114.

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Abstract We prove some important properties of the extremal coefficients of a stable tail dependence function (“STDF”) and characterise logistic and some related STDFs. The well known sufficient conditions for composebility of logistic STDFs are shown to be also necessary.

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Chen, Zaoli, and Gennady Samorodnitsky. "Extremal clustering under moderate long range dependence and moderately heavy tails." Stochastic Processes and their Applications 145 (March 2022): 86–116. http://dx.doi.org/10.1016/j.spa.2021.12.001.

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Olinda, R. A., J. Blanchet, C. A. C. dos Santos, V. A. Ozaki, and P. J. Ribeiro Jr. "Spatial extremes modeling applied to extreme precipitation data in the state of Paraná." Hydrology and Earth System Sciences Discussions 11, no. 11 (2014): 12731–64. http://dx.doi.org/10.5194/hessd-11-12731-2014.

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Abstract. Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in ge

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Li, Jiayi, Zhiyan Cai, Yixuan Liu, and Chengxiu Ling. "Extremal Analysis of Flooding Risk and Its Catastrophe Bond Pricing." Mathematics 11, no. 1 (2022): 114. http://dx.doi.org/10.3390/math11010114.

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Catastrophic losses induced by natural disasters are receiving growing attention because of the severe increases in their magnitude and frequency. We first investigated the extreme tail behavior of flood-caused economic losses and maximum point precipitation based on the peaks-over-threshold method and point process (PP) model and its extreme tail dependence. We found that both maximum point precipitation and direct economic losses are well-modeled by the PP approach with certain tail dependence. These findings were further utilized to design a layered compensation insurance scheme using estim

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Saunina, A. Yu, V. R. Nikitenko, A. A. Chistyakov, M. A. Zvaizgne, A. R. Tameev, and A. E. Aleksandrov. "Analytic Modeling of the of J–V Characteristics of Quantum Dot-Based Photovoltaic Cells." International Journal of Nanoscience 18, no. 03n04 (2019): 1940083. http://dx.doi.org/10.1142/s0219581x19400830.

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An analytic model of [Formula: see text]–[Formula: see text] characteristics of photovoltaic devices based on quantum dot (QD) solids is developed. The model yields the upper estimation of the power conversion efficiency and predicts its extremal dependence on the diffusion length of excitons. The predictive power of our model is approved by the comparison with the experimental data for PbS QD-based solar cells.

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Ferreira, Helena, and Marta Ferreira. "The stopped clock model." Dependence Modeling 10, no. 1 (2022): 48–57. http://dx.doi.org/10.1515/demo-2022-0101.

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Abstract The extreme value theory presents specific tools for modeling and predicting extreme phenomena. In particular, risk assessment is often analyzed through measures for tail dependence and high values clustering. Despite technological advances allowing an increasingly larger and more efficient data collection, there are sometimes failures in the records, which causes difficulties in statistical inference, especially in the tail where data are scarcer. In this article, we present a model with a simple and intuitive failures scheme, where each record failure is replaced by the last record

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Thèses sur le sujet "Extremal dependence modeling"

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Kereszturi, Monika. "Assessing and modelling extremal dependence in spatial extremes." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/86369/.

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Offshore structures, such as oil platforms and vessels, must be built such that they can withstand extreme environmental conditions (e.g., high waves and strong winds) that may occur during their lifetime. This means that it is essential to quantify probabilities of the occurrence of such extreme events. However, a difficulty arises in that there are very limited data available at these levels. The statistical field of extreme value theory provides asymptotically motivated models for extreme events, hence allowing extrapolation to very rare events. In addition to the risk to a single site, we

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Lecei, Ivan [Verfasser]. "Modelling extremal dependence / Ivan Lecei." Ulm : Universität Ulm, 2018. http://d-nb.info/1173249745/34.

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Johnson, Jill Suzanne. ""Modelling Dependence in Extreme Environmental Events"." Thesis, University of Newcastle upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525050.

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Navarrete, Miguel A. Ancona. "Dependence modelling and spatial prediction for extreme values." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369658.

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Eriksson, Kristofer. "Risk Measures and Dependence Modeling in Financial Risk Management." Thesis, Umeå universitet, Institutionen för fysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-85185.

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In financial risk management it is essential to be able to model dependence in markets and portfolios in an accurate and efficient way. A high positive dependence between assets in a portfolio can be devastating, especially in times of crises, since losses will most likely occur at the same time in all assets for such a portfolio. The dependence is therefore directly linked to the risk of the portfolio. The risk can be estimated by several different risk measures, for example Value-at-Risk and Expected shortfall. This paper studies some different ways to measure risk and model dependence, both

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Singh, Abhay Kumar. "Modelling Extreme Market Risk - A Study of Tail Related Risk Measures." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2011. https://ro.ecu.edu.au/theses/417.

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Market risk modelling is one of the most dynamic domains in finance. Risk is the uncertainty that affects the values of assets in the system in an unknown fashion causing fluctuations in their values and in investment outcomes. Market risk is defined as the losses due to fluctuations in the prices of financial assets which are caused by changing market conditions. Market risk modelling comprises tools and techniques which quantify the risk associated with financial instruments. Risk quantification is necessary to devise strategies such as hedging or diversification against the risk, to avoid s

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Boulin, Alexis. "Partitionnement des variables de séries temporelles multivariées selon la dépendance de leurs extrêmes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5039.

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Dans un grand éventail d'applications allant des sciences du climat à la finance, des événements extrêmes avec une probabilité loin d'être négligeable peuvent se produire, entraînant des conséquences désastreuses. Les extrêmes d'évènements climatiques tels que le vent, la température et les précipitations peuvent profondément affecter les êtres humains et les écosystèmes, entraînant des événements tels que des inondations, des glissements de terrain ou des vagues de chaleur. Lorsque l'emphase est mise sur l'étude de variables mesurées dans le temps sur un grand nombre de stations ayant une loc

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Ayari, Samia. "Nonparametric estimation of the dependence function for multivariate extreme value distributions." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4078.

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Dans cette thèse, nous abordons l'estimation non paramétrique de la fonction de dépendance des distributions multivariées à valeurs extrêmes. Dans une première partie, on adopte l’hypothèse classique stipulant que les variables aléatoires sont indépendantes et identiquement distribuées (i.i.d). Plusieurs estimateurs non paramétriques sont comparés pour une fonction de dépendance trivariée de type logistique dans deux différents cas. Dans le premier cas, on suppose que les fonctions marginales sont des distributions généralisées à valeurs extrêmes. La distribution marginale est remplacée par la

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Kyselá, Eva. "Modelling portfolios with heavy-tailed risk factors." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264017.

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The thesis aims to investigate some of the approaches to modelling portfolio returns with heavy-tailed risk factors. It first elaborates on the univariate time series models, and compares the benchmark model (GARCH with Student t innovations or its GJR extension) predictive performance with its two competitors, the EVT-GARCH model and the Markov-Switching Multifractal (MSM) model. The motivation of EVT extension of GARCH specification is to use a more proper distribution of the innovations, based on the empirical distribution function. The MSM is one of the best performing models in the multif

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Schulz, Thorsten [Verfasser], Matthias [Akademischer Betreuer] [Gutachter] Scherer, Griselda [Gutachter] Deelstra, and Ralf [Gutachter] Werner. "Stochastic dependencies in derivative pricing: Decoupled BNS-volatility, sequential modeling of jumps, and extremal WWR / Thorsten Schulz ; Gutachter: Matthias Scherer, Griselda Deelstra, Ralf Werner ; Betreuer: Matthias Scherer." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1147566003/34.

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Livres sur le sujet "Extremal dependence modeling"

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Gao, Yanhong, and Deliang Chen. Modeling of Regional Climate over the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.591.

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The modeling of climate over the Tibetan Plateau (TP) started with the introduction of Global Climate Models (GCMs) in the 1950s. Since then, GCMs have been developed to simulate atmospheric dynamics and eventually the climate system. As the highest and widest international plateau, the strong orographic forcing caused by the TP and its impact on general circulation rather than regional climate was initially the focus. Later, with growing awareness of the incapability of GCMs to depict regional or local-scale atmospheric processes over the heterogeneous ground, coupled with the importance of t

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Chapitres de livres sur le sujet "Extremal dependence modeling"

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Ortego, María I., Juan J. Egozcue, and Raimon Tolosana-Delgado. "Modeling Extremal Dependence Using Copulas. Application to Rainfall Data." In Lecture Notes in Earth System Sciences. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32408-6_13.

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Praprom, Chakorn, and Songsak Sriboonchitta. "Extreme Value Copula Analysis of Dependences between Exchange Rates and Exports of Thailand." In Modeling Dependence in Econometrics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_12.

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Boonyanuphong, Phattanan, and Songsak Sriboonchitta. "An Analysis of Volatility and Dependence between Rubber Spot and Futures Prices Using Copula-Extreme Value Theory." In Modeling Dependence in Econometrics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_27.

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Kaewkheaw, Mutita, Pisit Leeahtam, and Chukiat Chaiboosri. "An Analysis of Relationship between Gold Price and U.S. Dollar Index by Using Bivariate Extreme Value Copulas." In Modeling Dependence in Econometrics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_29.

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Coles, Stuart. "Extremes of Dependent Sequences." In An Introduction to Statistical Modeling of Extreme Values. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-3675-0_5.

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Taylor, John, and Jay Larson. "Resolution Dependence in Modeling Extreme Weather Events." In Computational Science — ICCS 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45545-0_29.

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Weissman, Ishay. "On Some Dependence Measures for Multivariate Extreme Value Distributions." In Advances in Mathematical and Statistical Modeling. Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4626-4_12.

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"Nonparametric Estimation of Extremal Dependence Anna Kiriliouk, Johan Segers, and Michał Warchoł." In Extreme Value Modeling and Risk Analysis. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/b19721-21.

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Smith, Elizabeth L., and David Walshaw. "Modelling Bivariate Extremes in a Region." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0048.

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Abstract Practitioners of extreme value methodology have been slow to accept the Bayesian paradigm, and the initial work that has been carried out in recent years has reflected the history of the classical approach, in concentrating solely on univariate problems. In this paper we take a first step towards balancing the substantial frequentist literature on multivariate extreme value inference by considering problems of bivariate inference from a Bayesian point of view. We relate the bivariate case to inference problems for extremes of environmental variables recorded at a number of locations in a spatial region. We show how inference for bivariate extreme value models can be implemented using an MCMC scheme, and compare two popular model families. We then select one of these families for use in a practical example involving rainfall data. We employ prior information on marginal behavior of extremes constructed from carefully elicited expert beliefs, while prior beliefs about the dependence parameter relate the strength of dependence inversely to the distance between locations, thus exploiting the spatial aspect inherent in the inference problem. We briefly discuss how our ongoing work in this area will lead to a spatial model which enables inference at a particular location of interest to be improved through the model for bivariate extremal dependencies with other locations. We conclude with a pointer to inference for max-stable process models, which are being developed by the authors to address the problems involved in truly multivariate inference.

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"Extreme Dependence Models." In Extreme Value Modeling and Risk Analysis. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/b19721-20.

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Actes de conférences sur le sujet "Extremal dependence modeling"

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Towe, Ross, Emma Eastoe, Jonathan Tawn, Yanyun Wu, and Philip Jonathan. "The Extremal Dependence of Storm Severity, Wind Speed and Surface Level Pressure in the Northern North Sea." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10154.

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Characterising the joint distribution of extremes of significant wave height and wind speed is critical for reliable design and assessment of marine structures. The extremal dependence of pairs of oceanographic variables can be characterised using one of a number of summary statistics, which describe the two different types of extremal dependence. Quantifying the type of extremal dependence is an essential pre-requisite to joint or spatial extreme value modelling, and ensures that appropriate model forms are employed. We estimate extremal dependence between storm peak significant wave height a

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"Evaluating extremal dependence in stock markets using Extreme Value Theory." 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.d6.singh2.

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McDonald, Andrew, Pang-Ning Tan, and Lifeng Luo. "COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/462.

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Normalizing flows—a popular class of deep generative models—often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence among variables. In light of this shortcoming, we propose COMET (COpula Multivariate ExTreme) Flows, which decompose the process of modeling a joint distribution into two parts: (i) modeling its marginal distributions, and (ii) modeling its copula distribution. COMET Flows capture he

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Wada, Ryota, Philip Jonathan, Takuji Waseda, and Shejun Fan. "Estimating Extreme Waves in the Gulf of Mexico Using a Simple Spatial Extremes Model." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95442.

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Abstract We seek to characterize the behavior of extreme waves in the Gulf of Mexico, using a 109 year-long wave hindcast (GOMOS). The largest waves in this region are driven by strong winds from hurricanes. Design of offshore production systems requires the estimation of extreme metocean conditions corresponding to return periods from 1 year to 10,000 years and beyond. For extrapolation to long return periods, estimation using data for around 100 years from a single location will incur large uncertainties. Approaches such as spatial pooling, cyclone track-shifting and explicit track modeling

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Vanem, Erik, Øystein Lande, and Elias Fekhari. "A Simulation Study on the Usefulness of the Bernstein Copula for Statistical Modeling of Metocean Variables." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-121159.

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Abstract Probabilistic modelling of relevant environmental variables are crucial for the safe design and operation of marine structures. Using metocean data, a joint model of several variables can be estimated, including their dependence structure. Often, a conditional model is assumed for this, but recently the non-parametric Bernstein copula has been suggested as an alternative tool to model such dependencies. As a non-parametric technique, it is very flexible and often provides excellent goodness-of-fit to data with different dependencies. However, non-parametric techniques are prone to ove

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Barbariol, Francesco, Alvise Benetazzo, Filippo Bergamasco, Sandro Carniel, and Mauro Sclavo. "Stochastic Space-Time Extremes of Wind Sea States: Validation and Modeling." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23997.

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Damages and accidents occurred to offshore structures and routing ships raise questions about adequacy of conventional time domain analysis of short-crested sea waves. Indeed, experimental and field evidence showed that during such wave states, typical of storms, the maximum sea surface elevation gathered at a single point in time, i.e. the time extreme, tends to underestimate the actual maximum that occurs over a surrounding area, i.e. the space-time extreme. Recently, stochastic models for the prediction of multidimensional Gaussian random fields maxima, e.g. Piterbarg’s theorem and Adler an

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Wada, Ryota, Philip Jonathan, and Takuji Waseda. "Spatial Features of Extreme Waves in Gulf of Mexico." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-19190.

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Abstract Extreme value analysis of significant wave height using data from a single location often incurs large uncertainty due to small sample size. Including wave data from nearby locations increases sample size at the risk of introducing dependency between extreme events and hence violating modelling assumptions. In this work, we consider extreme value analysis of spatial wave data from the 109-year GOMOS wave hindcast for the Gulf of Mexico, seeking to incorporate the effects of spatial dependence in a simple but effective manner. We demonstrate that, for estimation of return values at a g

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Yu, Hang, Zheng Choo, Justin Dauwels, Philip Jonathan, and Qiao Zhou. "Modeling spatially-dependent extreme events with Markov random field priors." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283503.

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Vanem, Erik. "Stochastic Models for Long-Term Prediction of Extreme Waves: A Literature Survey." In ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/omae2010-20076.

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This paper presents a literature survey on time-dependent statistical modelling of extreme waves. The focus is twofold: on statistical modelling of extreme waves and time-dependent statistical modelling. The first part will consist of a thorough literature review of statistical modelling of extreme waves and wave parameters. The second part will focus on statistical modelling of time- and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing stati

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Mackay, E. B. L., C. J. R. Murphy-Barltrop, and P. Jonathan. "The SPAR Model: A New Paradigm for Multivariate Extremes. Application to Joint Distributions of Metocean Variables." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-130932.

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Abstract This paper presents the application of a new multivariate extreme value model for the estimation of metocean variables. The model requires fewer assumptions about the forms of the margins and dependence structure compared to existing approaches, and provides a flexible and rigorous framework for modelling multi-variate extremes. The method involves a transformation of variables to polar coordinates. The tail of the radial variable is then modelled using the generalised Pareto distribution, with parameters conditional on angle, providing a natural extension of uni-variate theory to mul

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Rapports d'organisations sur le sujet "Extremal dependence modeling"

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Furman, Alex, Jan Hopmans, Shmuel Assouline, Jirka Simunek, and Jim Richards. Soil Environmental Effects on Root Growth and Uptake Dynamics for Irrigated Systems. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592118.bard.

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Root water uptake is perhaps the most important unknown in the mass balance of hydrological and agricultural systems. The understanding and the ability to predict root uptake and the way it is influence by environmental conditions has great potential in increasing water and fertilizer use efficiency and allowing better control of water and contaminant leach towards groundwater. This BARD supported research is composed of several components, including a) intensive laboratory work for the quantification of root uptake and the way it is controlled by environmental conditions; b) development of to

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Oliynyk, Kateryna, and Matteo Ciantia. Application of a finite deformation multiplicative plasticity model with non-local hardening to the simulation of CPTu tests in a structured soil. University of Dundee, 2021. http://dx.doi.org/10.20933/100001230.

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In this paper an isotropic hardening elastoplastic constitutive model for structured soils is applied to the simulation of a standard CPTu test in a saturated soft structured clay. To allow for the extreme deformations experienced by the soil during the penetration process, the model is formulated in a fully geometric non-linear setting, based on: i) the multiplicative decomposition of the deformation gradient into an elastic and a plastic part; and, ii) on the existence of a free energy function to define the elastic behaviour of the soil. The model is equipped with two bonding-related intern

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