Yanxi Hou
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View article: Factorized Tail Volatility Model: Augmenting Excess-over-Threshold Method for High-Dimensional Hevay-Tailed Data
Factorized Tail Volatility Model: Augmenting Excess-over-Threshold Method for High-Dimensional Hevay-Tailed Data Open
Ecess-over-Threshold method is a crucial technique in extreme value analysis, which approximately models larger observations over a threshold using a Generalized Pareto Distribution. This paper presents a comprehensive framework for analyz…
View article: Bootstrap-based Inference for Bivariate Heteroscedastic Extremes with a Changing Tail Copula
Bootstrap-based Inference for Bivariate Heteroscedastic Extremes with a Changing Tail Copula Open
This paper introduces a copula-based model for independent but non-identically distributed data with heteroscedastic extremes marginal and changing tail dependence structures. We establish a unified framework for inference by proving the w…
View article: Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index
Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index Open
Systemic risk refers to the potential for a disruption in one part of a financial system to trigger a cascade of adverse effects, impacting the functioning of the system. Despite the progress on novel systemic risk measures, research on dy…
View article: Online Prediction of Extreme Conditional Quantiles via B-Spline Interpolation
Online Prediction of Extreme Conditional Quantiles via B-Spline Interpolation Open
Extreme quantiles are critical for understanding the behavior of data in the tail region of a distribution. It is challenging to estimate extreme quantiles, particularly when dealing with limited data in the tail. In such cases, extreme va…
View article: EVIboost for the Estimation of Extreme Value Index Under Heterogeneous Extremes
EVIboost for the Estimation of Extreme Value Index Under Heterogeneous Extremes Open
Modeling heterogeneity on heavy-tailed distributions under a regression framework is challenging, yet classical statistical methodologies usually place conditions on the distribution models to facilitate the learning procedure. However, th…
View article: Incrementally Zero-Shot Detection by an Extreme Value Analyzer
Incrementally Zero-Shot Detection by an Extreme Value Analyzer Open
Human beings not only have the ability to recognize novel unseen classes, but also can incrementally incorporate the new classes to existing knowledge preserved. However, zero-shot learning models assume that all seen classes should be kno…
View article: Extreme and Inference for Tail Gini Functionals With Applications in Tail Risk Measurement
Extreme and Inference for Tail Gini Functionals With Applications in Tail Risk Measurement Open
Tail risk analysis focuses on the problem of risk measurement on the tail regions of financial variables. As one crucial task in tail risk analysis for risk management, the measurement of tail risk variability is less addressed in the lite…
View article: Prediction of Extremal Expectile Based on Regression Models with Heteroscedastic Extremes
Prediction of Extremal Expectile Based on Regression Models with Heteroscedastic Extremes Open
Expectile recently receives much attention for its coherence as a tail risk measure. Estimation of conditional expectile at extremal tails is of great interest in quantitative risk management. Regression analysis is a convenient and useful…
View article: Prediction of Extremal Expectile Based on Regression Models With Heteroscedastic Extremes
Prediction of Extremal Expectile Based on Regression Models With Heteroscedastic Extremes Open
Expectile recently receives much attention for its coherence as a tail risk measure. Estimation of conditional expectile at extremal tails is of great interest in quantitative risk management. Regression analysis is a convenient and useful…
View article: Inference for Conditional Value-at-Risk of a Predictive Regression
Inference for Conditional Value-at-Risk of a Predictive Regression Open
Conditional value-at-risk is a popular risk measure in risk management. We study the inference problem of conditional value-at-risk under a linear predictive regression model. We derive the asymptotic distribution of the least squares esti…
View article: Statistical Inference for a Relative Risk Measure
Statistical Inference for a Relative Risk Measure Open
For monitoring systemic risk from regulators’ point of view, this article proposes a relative risk measure, which is sensitive to the market comovement. The asymptotic normality of a nonparametric estimator and its smoothed version is esta…