We investigate what can be learned from a purely phenomenological study of options prices without modelling assumptions. We fitted neural net (NN) models to LIFFE "ESX" European style FTSE 100 index options using daily data from 1992 to 1997. These non-parametric models reproduce the Black-Scholes (BS) analytic model in terms of fit and performance measures using just the usual five inputs (S, X, t, r, IV). We found that adding transaction costs (bid-ask spread) to these standard five parameters gives a comparable fit and performance. Tests show that the bid-ask spread can be a statistically significant explanatory variable for option prices. The difference in option prices between the models with transaction costs and those without ranges from about −3.0 to +1.5 index points, varying with maturity date. However, the difference depends on the moneyness (S/X), being greatest in-the-money. This suggests that use of a five-factor model can result in a pricing difference of up to £10 to £30 per call option contract compared with modelling under transaction costs. We found that the influence of transaction costs varied between different yearly subsets of the data. Open interest is also a significant explanatory variable, but volume is not.
The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.
Absrracf-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 example, and is then applied to prediction intervals for a financial engineering example viz. option pricing using data from LlFFE for 'ESX' European style options on the FTSE 100 index. Unbiased estimates of the standard error are obtained. The method uses standard regression procedures to determine local error bars and avoids programming special architectures. It is appropriate for target data with non-constant variance.
Anaerobic digestion provides an effective way of disposing of organic material in wastewater. The EUfunded TELEMAC project aims at improving the reliability and efficiency of monitoring and control of this type of wastewater treatment plant. One of its special features is the idea of a telecontrol centre which monitors multiple, geographically distributed plants remotely, acts as a centre of expertise, and brings together the expertise of a network of remote experts. Data mining has been identified as a potentially useful contributing technology. Sensor data is now becoming available for some pilot, laboratory scale, and industrial sized digesters. This paper presents the directions of work and emerging results of data mining. Particular themes considered here include: • experience gained in the data mining exercise; • the use of confidence and prediction intervals; • prospects for generalisation over different sizes and types of anaerobic digester; • relationship to the overall supervision system developed in the project.
Security risk mitigation is a salient issue in systems development research. This paper introduces a lightweight approach to security risk mitigation that can be used within an Agile Development framework -the Security Obstacle Mitigation Model (SOMM). The SOMM uses the concept of trust assumptions to derive obstacles and the concept of misuse cases to model the obstacles. A synthetic scenario, based on an on-line system, shows how the SOMM is used to anticipate malicious behaviour with respect to an operational information system and to document a priori how this malicious behaviour should be mitigated. Since the SOMM is conceptually simple in deployment, its use is well within the capacities of the users who form part of an Agile Development team and crucially it should not take up a significant amount of development time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.