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Predictive regression with order-p autoregressive predictors

Amihud, Yakov; Hurvich, Clifford M.; Wang, Yi - In: Journal of empirical finance 17 (2010) 3, pp. 513-525

Persistent link: https://www.econbiz.de/10009267284

Richman, Ronald; Wüthrich, Mario V. - In: Risks : open access journal 8 (2020) 3/83, pp. 1-26

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...

Persistent link: https://www.econbiz.de/10012293262

Model error (or ambiguity) and its estimation, with particular application to loss reserving

Taylor, Greg; Mc Guire, Gráinne - In: Risks : open access journal 11 (2023) 11, pp. 1-28

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are...

Persistent link: https://www.econbiz.de/10014435599

Predictive regression with p-lags and order-q autoregressive predictors

Jayetileke, Harshanie L.; Wang, You-Gan; Zhu, Min - In: Journal of empirical finance 62 (2021), pp. 282-293

Persistent link: https://www.econbiz.de/10012693434

Testing for monotonicity in expected asset returns

Romano, Joseph P.; Wolf, Michael - In: Journal of empirical finance 23 (2013), pp. 93-116

Persistent link: https://www.econbiz.de/10010221769

Multivariate functional time series forecasting : application to age-specific mortality rates

Gao, Yuan; Shang, Han Lin - In: Risks : open access journal 5 (2017) 2, pp. 1-18

This study considers the forecasting of mortality rates in multiple populations. We propose a model that combines mortality forecasting and functional data analysis (FDA). Under the FDA framework, the mortality curve of each year is assumed to be a smooth function of age. As with most of the...

Persistent link: https://www.econbiz.de/10011643355

Return predictability and intertemporal asset allocation : evidence from a bias-adjusted VAR model

Engsted, Tom; Pedersen, Thomas Q. - In: Journal of empirical finance 19 (2012) 2, pp. 241-253

Persistent link: https://www.econbiz.de/10009615710

Biased information weight processing in stock markets

Mohrschladt, Hannes; Langer, Thomas - In: Journal of empirical finance 57 (2020), pp. 89-106

Persistent link: https://www.econbiz.de/10012430443

Use of prediction bias in active learning and its application to large variable annuity

Gweon, Hyukjun; Li, Shu; Xu, Yangxuan - In: Risks : open access journal 12 (2024) 6, pp. 1-14

assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA … portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning …. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling …

Persistent link: https://www.econbiz.de/10014636846

Special issue on high frequency data in finance ; Pt. 1

Baillie, Richard (contributor); … - 1997

Persistent link: https://www.econbiz.de/10001224723

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