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A quantile regression neural network approach to estimating the conditional density of multiperiod returns

Taylor, James W. - In: Journal of forecasting 19 (2000) 4, pp. 299-311

Persistent link: https://www.econbiz.de/10001504631

A quantile regression approach to estimating the distribution of multiperiod returns

Taylor, James W. - In: The journal of derivatives : the official publication … 7 (1999) 1, pp. 64-78

Persistent link: https://www.econbiz.de/10001432473

Forecasting value at risk and expected shortfall using a semiparametric approach based on the asymmetric laplace distribution

Taylor, James W. - In: Journal of business & economic statistics : JBES ; a … 37 (2019) 1, pp. 121-133

Persistent link: https://www.econbiz.de/10012176554

Using CAViaR models with implied volatility for value-at-risk estimation

Jeon, Jooyoung; Taylor, James W. - In: Journal of forecasting 32 (2013) 1, pp. 62-74

Persistent link: https://www.econbiz.de/10009758719

Return-Volatility Relationship : Insights from Linear and Non-Linear Quantile Regression

Allen, David E. - 2013

The purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the volatility...

Persistent link: https://www.econbiz.de/10013083138

An approximate long-memory range-based approach for value at risk estimation

Meng, Xiaochun; Taylor, James W. - In: International journal of forecasting 34 (2018) 3, pp. 377-388

Persistent link: https://www.econbiz.de/10012030985

Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data

Meng, Xiaochun; Taylor, James W. - In: European journal of operational research : EJOR 280 (2020) 1, pp. 191-202

Persistent link: https://www.econbiz.de/10012132379