Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Jerome V. Healy"'
Publikováno v:
Risks, Vol 12, Iss 9, p 148 (2024)
We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications
Externí odkaz:
https://doaj.org/article/8f771aee83944be4be52654a9986ab49
Publikováno v:
Environmental Modelling & Software. 22:315-322
Anaerobic digestion provides an effective way of disposing organic material in wastewater. The EU-funded TELEMAC project aims at improving the reliability and efficiency of monitoring and control of this type of wastewater treatment plant. One of its
Publikováno v:
Journal of Futures Markets. 27:471-494
The Black–Scholes (BS; F. Black & M. Scholes, 1973) option pricing model, and modern parametric option pricing models in general, assume that a single unique price for the underlying instrument exists, and that it is the mid- (the average of the as
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 344:289-293
We report call option pricing for up-and-out style barrier options through the use of a neural net model. A synthetic data set was constructed from the real LIFFE standard option price data by use of the Rubenstein and Reiner analytic model (Risk Sep
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 344:162-167
Non-parametric methods such as artificial neural nets can successfully model prices of financial options, out-performing the Black–Scholes analytic model (Eur. Phys. J. B 27 (2002) 219). However, the accuracy of such approaches is usually expressed
This study explores how price and non-price factors influence the quantity of retail deposits held by depository institutions. Price factors examined include the policy or base rate and retail deposit interest rates set by individual banks, and non-p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______645::25500ccf7034a1dae707e51d48a292b0
http://www.bangor.ac.uk/business/research/documents/BBSWP13006.pdf
http://www.bangor.ac.uk/business/research/documents/BBSWP13006.pdf
Autor:
Jerome V. Healy
Publikováno v:
Springer Optimization and Its Applications ISBN: 9781461437727
Computational techniques forregression have been widely applied to asset pricing, return forecasting, volatility forecasting, credit risk assessment, and value at risk estimation, among other tasks. Determining probabilistic bounds on results is esse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a1b676e936e852ec178ed3614951c94
https://doi.org/10.1007/978-1-4614-3773-4_1
https://doi.org/10.1007/978-1-4614-3773-4_1
Autor:
Laurent Lardon, Maurice Dixon, Jerome V. Healy, Simon Lambert, Jean-Philippe Steyer, Julian R. Gallop
Publikováno v:
Control Engineering Practice
Control Engineering Practice, Elsevier, 2007, 15 (8), pp.987-999. ⟨10.1016/j.conengprac.2006.11.010⟩
Control Engineering Practice, Elsevier, 2007, 15 (8), pp.987-999. ⟨10.1016/j.conengprac.2006.11.010⟩
International audience; The stable and efficient operation of anaerobic wastewater treatment plants (WWTPs) is a major challenge for monitoring and control systems. Support for distributed anaerobic WWTPs through remotely monitoring their data was in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cb8802a66f12b474170de7b14e6bbe6
https://hal.inrae.fr/hal-02659470
https://hal.inrae.fr/hal-02659470
Extracting the risk neutral density (RND) function from option prices is well defined in principle, but is very sensitive to errors in practice. For risk management, knowledge of the entire RND provides more information for Value-at-Risk (VaR) calcul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c11a48dbaaea43c86a780ee767c8c4ab
http://arxiv.org/pdf/physics/0607240
http://arxiv.org/pdf/physics/0607240
Publikováno v:
IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468).
This paper describes a generally applicable robust method for determining prediction intervals for models derived by non-linear regression. Hypothesis tests for bias are applied. The concept is demonstrated by application to a standard synthetic exam