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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
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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
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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
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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
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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
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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