A neural network versus Black–Scholes: a comparison of pricing and hedging performances

An Erratum has been published for this article in Journal of Forecasting 22(6‐7) 2003, 551 The Black–Scholes formula is a well‐known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black–Scholes formula. The neural network method is applied to the out‐of‐sample pricing and delta‐hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge‐analysis is stressed further in this paper. As benchmarks, the Black–Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright © 2003 John Wiley & Sons, Ltd.     

On the Circulant and Aperiodic Models of Deconvolution

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Forecast accuracy after pretesting with an application to the stock market

In econometrics, as a rule, the same data set is used to select the model and, conditional on the selected model, to forecast. However, one typically reports the properties of the (conditional) forecast, ignoring the fact that its properties are affected by the model selection (pretesting). This is wrong, and in this paper we show that the error can be substantial. We obtain explicit expressions for this error. To illustrate the theory we consider a regression approach to stock market forecasting, and show that the standard predictions ignoring pretesting are much less robust than naive econometrics might suggest. We also propose a forecast procedure based on the ‘neutral Laplace estimator’, which leads to an improvement over standard model selection procedures. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimation and testing of time-varying coefficient regression models in the presence of linear restrictions

A linear regression model with random walk coefficients is extended to allow for linear restrictions between the coefficients to be satisfied at each point in time. Estimation in this model is shown to be no more involved than estimation in the standard model. It is also demonstrated how, after a slight modification to the testing problem, classical test procedures may be applied to the problem of testing for such restrictions. The performance of the Lagrange Multiplier test for a variety of different restrictions is then investigated via simulation. An empirical application involving testing for homogeneity in a random walk coefficient version of the AIDS model is given.     

A ground‐level ozone forecasting model for Santiago,Chile

A physically based model for ground‐level ozone forecasting is evaluated for Santiago, Chile. The model predicts the daily peak ozone concentration, with the daily rise of air temperature as input variable; weekends and rainy days appear as interventions. This model was used to analyse historical data, using the Linear Transfer Function/Finite Impulse Response (LTF/FIR) formalism; the Simultaneous Transfer Function (STF) method was used to analyse several monitoring stations together. Model evaluation showed a good forecasting performance across stations—for low and high ozone impacts—with power of detection (POD) values between 70 and 100%, Heidke's Skill Scores between 40% and 70% and low false alarm rates (FAR). The model consistently outperforms a pure persistence forecast. Model performance was not sensitive to different implementation options. The model performance degrades for two‐ and three‐days ahead forecast, but is still acceptable for the purpose of developing an environmental warning system at Santiago. Copyright © 2002 John Wiley & Sons, Ltd.     

语境因素在The Sampler一文的语篇理解中的应用

运用背景知识,认知模式,认知语境知识和知识草案等理论去分析理解购物行为模式,通过分析行为模式的特殊性以及对词语标记性的运手,揭示了一个特殊购物者———TheSam-pler———充满孤独无助的内心世界。     

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.     

我国排球后备人才的培养与市场管理交流体制

分析了社会转型期我国排球后备人才的培养以及市场交流管理体制，认为我国排球后备人才培养体制由单一、封闭模式向多元、开放模式发展，建立多渠道、多形式的多元培养体制是大势所趋；传统的排球后备人才管理体制存在各种弊端，建立切实可行的排球后备人才流动市场体制，实现人才的最佳化流动，最大限度地合理使用排球后备人才，是提高后备人才使用率和成材率的关键。     

高等师范学生教师职业技能训练及培养模式新论

高等师范学生的教师职业技能训练,自国家1995年《教师资格条例》颁布以来,虽在全国有关高校一度出现好的势头,但很快被我国高校扩招的形势冲淡了,目前情况令人担忧。因此,在我国高等教育发展的新形势下,从宏观上构建高等师范学生教师职业技能培养模式应是当务之急。     

砼强度准则在井壁结构中的应用研究

本文根据井壁结构模型试验结果,对目前具有代表性的复杂应力状态下的砼强度准则进行了分析和对比,找到了一种比较适合于井壁结构的砼强度准则,从而为井壁结构承载能力的合理计算提供了理论基础.