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Relevant bibliographies by topics / Extreme quantile estimation

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Journal articles

Academic literature on the topic 'Extreme quantile estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Extreme quantile estimation"

1

Li, Deyuan, and Huixia Judy Wang. "Extreme Quantile Estimation for Autoregressive Models." Journal of Business & Economic Statistics 37, no. 4 (2018): 661–70. http://dx.doi.org/10.1080/07350015.2017.1408469.

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2

CAI, YUZHI. "A COMPARATIVE STUDY OF MONOTONE QUANTILE REGRESSION METHODS FOR FINANCIAL RETURNS." International Journal of Theoretical and Applied Finance 19, no. 03 (2016): 1650016. http://dx.doi.org/10.1142/s0219024916500163.

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Quantile regression methods have been used widely in finance to alleviate estimation problems related to the impact of outliers and the fat-tailed error distribution of financial returns. However, a potential problem with the conventional quantile regression method is that the estimated conditional quantiles may cross over, leading to a failure of the analysis. It is noticed that the crossing over issues usually occur at high or low quantile levels, which are the quantile levels of great interest when analyzing financial returns. Several methods have appeared in the literature to tackle this problem. This study compares three methods, i.e. Cai & Jiang, Bondell et al. and Schnabel & Eilers, for estimating noncrossing conditional quantiles by using four financial return series. We found that all these methods provide similar quantiles at nonextreme quantile levels. However, at extreme quantile levels, the methods of Bondell et al. and Schnabel & Eilers may underestimate (overestimate) upper (lower) extreme quantiles, while that of Cai & Jiang may overestimate (underestimate) upper (lower) extreme quantiles. All methods provide similar median forecasts.

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3

Kithinji, Martin M., Peter N. Mwita, and Ananda O. Kube. "Adjusted Extreme Conditional Quantile Autoregression with Application to Risk Measurement." Journal of Probability and Statistics 2021 (April 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/6697120.

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In this paper, we propose an extreme conditional quantile estimator. Derivation of the estimator is based on extreme quantile autoregression. A noncrossing restriction is added during estimation to avert possible quantile crossing. Consistency of the estimator is derived, and simulation results to support its validity are also presented. Using Average Root Mean Squared Error (ARMSE), we compare the performance of our estimator with the performances of two existing extreme conditional quantile estimators. Backtest results of the one-day-ahead conditional Value at Risk forecasts are also given.

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4

He, Yi, and John H. J. Einmahl. "Estimation of extreme depth-based quantile regions." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 2 (2016): 449–61. http://dx.doi.org/10.1111/rssb.12163.

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5

Gardes, Laurent. "Tail dimension reduction for extreme quantile estimation." Extremes 21, no. 1 (2017): 57–95. http://dx.doi.org/10.1007/s10687-017-0300-x.

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6

Onyutha, Charles, and Patrick Willems. "Uncertainty in calibrating generalised Pareto distribution to rainfall extremes in Lake Victoria basin." Hydrology Research 46, no. 3 (2014): 356–76. http://dx.doi.org/10.2166/nh.2014.052.

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Uncertainty in the calibration of the generalised Pareto distribution (GPD) to rainfall extremes is assessed based on observed and large number of global climate model rainfall time series for nine locations in the Lake Victoria basin (LVB) in Eastern Africa. The class of the GPD suitable for capturing the tail behaviour of the distribution and extreme quantiles is investigated. The best parameter estimation method is selected following comparison of the method of moments, maximum likelihood, L-moments, and weighted linear regression in quantile plots (WLR) to quantify uncertainty in the extreme intensity quantiles by employing the Jackknife method and nonparametric percentile bootstrapping in a combined way. The normal tailed GPD was found suitable. Although the performance of each parameter estimation method was acceptable in a number of evaluation criteria, generally the WLR technique appears to be more robust than others. The difference between upper and lower limits of the 95% confidence intervals expressed as a percentage of the empirical 10-year rainfall intensity quantile ranges from 9.25 up to 59.66%. The assessed uncertainty will be useful in support of risk based planning, design and operation of water engineering and water management applications related to floods in the LVB.

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7

You, Alexandre, Ulrike Schneider, Armelle Guillou, and Philippe Naveau. "Improving extreme quantile estimation via a folding procedure." Journal of Statistical Planning and Inference 140, no. 7 (2010): 1775–87. http://dx.doi.org/10.1016/j.jspi.2010.01.007.

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8

Morio, Jérôme. "Extreme quantile estimation with nonparametric adaptive importance sampling." Simulation Modelling Practice and Theory 27 (September 2012): 76–89. http://dx.doi.org/10.1016/j.simpat.2012.05.008.

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9

Kim, Sojung, Kyoung-Kuk Kim, and Heelang Ryu. "Robust quantile estimation under bivariate extreme value models." Extremes 23, no. 1 (2019): 55–83. http://dx.doi.org/10.1007/s10687-019-00362-2.

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10

Dutta, Santanu, and Suparna Biswas. "Extreme quantile estimation based on financial time series." Communications in Statistics - Simulation and Computation 46, no. 6 (2017): 4226–43. http://dx.doi.org/10.1080/03610918.2015.1112908.

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