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Relevant bibliographies by topics / Parametric Multivariate Extreme Value Models

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Academic literature on the topic 'Parametric Multivariate Extreme Value Models'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Parametric Multivariate Extreme Value Models"

1

Morganti, Paolo Riccardo. "Extreme Value Theory and Auction Models." Abril - Junio 2021 16, no. 2 (2021): 1–15. http://dx.doi.org/10.21919/remef.v16i2.596.

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The objective of this article is to develop a parametric approach to estimating auctions with incomplete data using Extreme Value Theory (EVT). The methodology is mainly theoretical: we first review that, when only transaction prices can be observed, the distribution of private valuations is irregularly identified. The sample bias produced by nonparametric estimators will affect all functionals of practical interest. We provide simulations for a best-case scenario and a worst-case scenario. Our results show that, compared to nonparametric approaches, the approximation of such functionals developed using EVT produces more accurate results, is easy to compute, and does not require strong assumptions about the unobserved distribution of bidders' valuations. It is recommended that financial operators working with auctions use this parametric approach when facing incomplete datasets. Given the difficult nature of the analysis, this work does not provide large sample properties for the proposed estimators and recommends the use of bootstrapping. This article contributes originally to the literature of structural estimation of auction models providing a useful and robust parametric approximation.

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2

AghaKouchak, Amir, and Nasrin Nasrollahi. "Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data." Water Resources Management 24, no. 6 (2009): 1229–49. http://dx.doi.org/10.1007/s11269-009-9493-3.

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3

Božović, Miloš. "Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach." Entropy 22, no. 12 (2020): 1425. http://dx.doi.org/10.3390/e22121425.

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This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.

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4

Guevara, C. Angelo, and Moshe E. Ben-Akiva. "Sampling of alternatives in Multivariate Extreme Value (MEV) models." Transportation Research Part B: Methodological 48 (February 2013): 31–52. http://dx.doi.org/10.1016/j.trb.2012.11.001.

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5

Salvadori, G., and C. De Michele. "Estimating strategies for multiparameter Multivariate Extreme Value copulas." Hydrology and Earth System Sciences 15, no. 1 (2011): 141–50. http://dx.doi.org/10.5194/hess-15-141-2011.

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Abstract. Multivariate Extreme Value models are a fundamental tool in order to assess potentially dangerous events. Exploiting recent theoretical developments in the theory of Copulas, new multiparameter models can be easily constructed. In this paper we suggest several strategies in order to estimate the parameters of the selected copula, according to different criteria: these may use a single station approach, or a cluster strategy, or exploit all the pair-wise relationships between the available gauge stations. An application to flood data is also illustrated and discussed.

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6

Salvadori, G., and C. De Michele. "Estimating strategies for Multiparameter Multivariate Extreme value copulas." Hydrology and Earth System Sciences Discussions 7, no. 5 (2010): 7563–90. http://dx.doi.org/10.5194/hessd-7-7563-2010.

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Abstract. Multivariate Extreme Value models are a fundamental tool in order to assess potentially dangerous events. Exploiting recent theoretical developments in the theory of Copulas, new multiparameter models can be easily constructed. In this paper we suggest several strategies in order to estimate the parameters of the selected copula, according to different criteria: these may use either a nearest neighbor or a nearest cluster approach, or exploit all the pair-wise relationships between the available gauge stations. An application to flood data is also illustrated and discussed.

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7

Han, Yu. "Semi-Parametric Statistical Model for Extreme Value Statistical Models and Application in Automatic Control." Applied Mechanics and Materials 680 (October 2014): 455–58. http://dx.doi.org/10.4028/www.scientific.net/amm.680.455.

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The frequency that extreme events appear in the life is low,but once it appears,the impact will be significant; many scholars have conducted in depth research and found that statistical theory of extreme value. The theory of extreme statistics plays a more and more important role in many fields such as automatic control, assembly line etc. This paper,makes an in-depth research towards the characteristics and parameter estimation of the extreme value statistical models,as well as the application,mainly analyzes the Bayes parameter estimation method of extreme value distribution,the extreme value distribution theory and Copula function random vector model.

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8

Cirillo, Pasquale, and Jürg Hüsler. "GENERALIZED EXTREME SHOCK MODELS WITH A POSSIBLY INCREASING THRESHOLD." Probability in the Engineering and Informational Sciences 25, no. 3 (2011): 419–34. http://dx.doi.org/10.1017/s0269964811000088.

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We propose a generalized extreme shock model with a possibly increasing failure threshold. Although standard models assume that the crucial threshold for the system might only decrease over time, because of weakening shocks and obsolescence, we assume that, especially at the beginning of the system's life, some strengthening shocks might increase the system tolerance to large shock. This is, for example, the case of turbines’ running-in in the field of engineering. On the basis of parametric assumptions, we provide theoretical results and derive some exact and asymptotic univariate and multivariate distributions for the model. In the last part of the article we show how to link this new model to some nonparametric approaches proposed in the literature.

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9

Bounceur, Ahcene, Salvador Mir, Reinhardt Euler, and Kamel Beznia. "Estimation of Analog/RF Parametric Test Metrics Based on a Multivariate Extreme Value Model." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 5 (2020): 966–76. http://dx.doi.org/10.1109/tcad.2019.2907923.

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10

Kyselý, Jan. "A Cautionary Note on the Use of Nonparametric Bootstrap for Estimating Uncertainties in Extreme-Value Models." Journal of Applied Meteorology and Climatology 47, no. 12 (2008): 3236–51. http://dx.doi.org/10.1175/2008jamc1763.1.

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Abstract The parametric and nonparametric approaches to the bootstrap are compared as to their performance in estimating uncertainties in extreme-value models. Simulation experiments make use of several combinations of true and fitted probability distributions utilized in climatological and hydrological applications. The results demonstrate that for small to moderate sample sizes the nonparametric bootstrap should be interpreted with caution because it leads to confidence intervals that are too narrow and underestimate the real uncertainties involved in the frequency models. Although the parametric bootstrap yields confidence intervals that are slightly too liberal as well, it improves the uncertainty estimates in most examined cases, even under conditions in which an incorrect parametric model is adopted for the data. Differences among three examined types of bootstrap confidence intervals (percentile, bootstrap t, and bias corrected and accelerated) are usually smaller in comparison with those between the parametric and nonparametric versions of bootstrap. It is concluded that the parametric bootstrap should be preferred whenever inferences are based on small to moderate sample sizes (n ≤ 60) and a suitable model for the data is known or can be assumed, including applications to confidence intervals related to extremes in global and regional climate model projections.

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