Results 291 to 300 of about 141,342 (308)

The importance of fat-tailed and skewed distributions in modeling value-at-risk

2018
Most of the Value-at-Risk models assume that financial returns are normally distributed, despite the fact that they are commonly known to be left skewed, fat-tailed and excess kurtosis. Forecasting Value-at-Risk with misspecified model leads to the underestimation or overestimation of the true Value-at-Risk.
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Value-at-Risk and least squares tail index estimation [PDF]

, 1999
The empirical evidence of heavy tails in stock return data is recognised by risk managers as an important factor in assessing the Value-at-Risk and risk profile of investment portfolios. Tail index estimation appears to be a tailor-made tool for estimating the extreme quantiles of heavy tailed distributions, as it exploits the information provided by ...
openaire  

Tail risk connectedness between US industries

International Journal of Finance and Economics, 2021
Linh H Nguyen, Linzhi Tan, Linh Xuan Diep Nguyen   +2 more
exaly  

Tail risk contagion between international financial markets during COVID-19 pandemic

International Review of Financial Analysis, 2021
exaly  

Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach

Resources Policy, 2021
Zhenhua Liu, Xunpeng Shi, Pengxiang Zhai   +2 more
exaly  

The world price of tail risk

Pacific-Basin Finance Journal, 2022
Kuan-Hui Lee, Cheol-Won Yang
exaly  

Stock market tail risk, tail risk premia, and return predictability

Journal of Futures Markets, 2021
Sangwon Suh, Sun-Joong Yoon
exaly  

Left-tail risk in China

Pacific-Basin Finance Journal, 2020
Fang Zhen, Xinfeng Ruan, Jin E Zhang
exaly  

Extreme and Inference for Tail Gini Functionals With Applications in Tail Risk Measurement

Journal of the American Statistical Association, 2021
exaly  

Extremal dependence structures and bounds of Tail Value-at-Risk

2016
In quantitative risk management, we often need to deal with risk aggregation with dependence uncertainty. In this session, we examine several extremal positive and negative dependence structures. We will also discuss their relationships with upper and lower bounds of concave distortion risk measures of aggregate risks.
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