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A nonparametric kernel regression approach for pricing options on stock market index

APPLIED ECONOMICS 2016年第10期48卷 902-913页

作者： Kung, James J. Ming Chuan Univ Dept Int Business Taipei Taiwan

Previous options studies typically assume that the dynamics of the underlying asset price follow a geometric Brownian motion (GBM) when pricing options on stocks, stock indices, currencies or futures. However, there is mounting empirical evidence that the volatility of asset price or return is far from constant. This article, in contrast to studies that use parametric approach for option pricing, employs nonparametric kernel regression to deal with changing volatility and, accordingly, prices options on stock index. Specifically, we first estimate nonparametrically the volatility of asset return in the GBM based on the Nadaraya-Watson (N-W) kernel estimator. Then, based on the N-W estimates for the volatility, we use Monte Carlo simulation to compute option prices under different settings. Finally, we compare the index option prices under our nonparametric model with those under the Black-Scholes model and the Stein-Stein model.

关键词： Geometric Brownian motion stock index option nonparametric kernel regression Nadaraya-Watson kernel estimator Black-Scholes model Stein-Stein model C14 C15 G13

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Pattern Recognition Based on the nonparametric kernel regression Method in A-share Market

Pattern Recognition Based on the Nonparametric Kernel Regres...

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International Conference on Soft Computing Techniques and Engineering Application (ICSCTEA)

作者： Sun, Huaiyu Zhu, Mi He, Feng Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China China Investment Secur Co Ltd Res Dept Shenzhen Peoples R China UBS SDIC Fund Management Co Ltd Prod Design Dept Shenzhen Peoples R China

ISBN: (纸本)9788132216957;9788132216940

Fourteen kinds of technical pattern in A-share market have been automatically identified in this paper, using nonparametric kernel regression method and quantitative recognition rules that are constrained and improved to make the model more feasible and effective for investment practice. K-S test shows that the distribution of the stock yield following the recognized patterns statistically differs from that of random sampling. Clustering analysis also suggests that most of the identified technical patterns have distinguishable effects on the subsequent stock price movement. These findings provide useful guidance for the development of innovative investment models in high-frequency automatic stock trading.

关键词： Technical pattern recognition nonparametric kernel regression

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Probability Density Estimation Based on nonparametric Local kernel regression

Probability Density Estimation Based on Nonparametric Local ...

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7th International Symposium on Neural Networks

作者： Han, Min Liang, Zhi-ping Dalian Univ Technol Sch Elect & Informat Engn Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9783642132773

In this research, a local kernel regression method was proposed to improve the computational efficiency after analyzing the kernel weights oh the non-parametric kernel regression. Based on the correlation between the distribution function and the probability density function, together with the nonparametric local kernel regression we developed a new probability density estimation method. With the proper setting of the sparse factor, the number of the kernels involved in the kernel smooth was controlled, and the density was estimated with highly fitness and smoothness. According to the simulations, we can see that the proposed method shows a very well performance both in the accuracy and the efficiency.

关键词： nonparametric kernel regression probability density estimation cumulative distribution functions

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VARIABLE BANDWIDTH kernel ESTIMATORS OF regression-CURVES

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ANNALS OF STATISTICS 1987年第1期15卷 182-201页

作者： MULLER, HG STADTMULLER, U UNIV ULM MATH ABT 1D-7900 ULMFED REP GER

In the model Yi=g(ti)+εi,i=1,⋯,n, where Yi are given observations, εi i.i.d. noise variables and ti nonrandom design points, kernel estimators for the regression function g(t) with variable bandwidth (smoothing parameter) depending on t are proposed. It is shown that in terms of asymptotic integrated mean squared error, kernel estimators with such a local bandwidth choice are superior to the ordinary kernel estimators with global bandwidth choice if optimal bandwidths are used. This superiority is maintained in a certain sense if optimal local bandwidths are estimated in a consistent manner from the data, which is proved by a tightness argument. The finite sample behavior of a specific local bandwidth selection procedure based on the Rice criterion for global bandwidth choice [Rice (1984)] is investigated by simulation.

关键词： 62G05 65D10 65J02 asymptotic optimality consistent bandwidth choice Gaussian limiting process local bandwidths nonparametric kernel regression Rice criterion tightness in $C$

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Indoor Localization with Channel Impulse Response Based Fingerprint and nonparametric regression

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2010年第3期9卷 1120-1127页

作者： Jin, Yunye Soh, Wee-Seng Wong, Wai-Choong Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

In this paper, we propose a fingerprint-based localization scheme that exploits the location dependency of the channel impulse response (CIR). We approximate the CIR by applying Inverse Fourier Transform to the receiver's channel estimation. The amplitudes of the approximated CIR (ACIR) vector are further transformed into the logarithmic scale to ensure that elements in the ACIR vector contribute fairly to the location estimation, which is accomplished through nonparametric kernel regression. As shown in our simulations, when both the number of access points and density of training locations are the same, our proposed scheme displays significant advantages in localization accuracy, compared to other fingerprint-based methods found in the literature. Moreover, absolute localization accuracy of the proposed scheme is shown to be resilient to the real time environmental changes caused by human bodies with random positions and orientations.

关键词： Indoor localization fingerprinting channel impulse response nonparametric kernel regression

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Performance evaluation of an acoustic indoor localization system based on a fingerprinting technique

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2014年第1期2014卷 1页

作者： Aloui, Nadia Raoof, Kosai Bouallegue, Ammar Letourneur, Stephane Zaibi, Sonia Univ Grenoble 3 Univ Grenoble 2 Univ Grenoble 1 Grenoble Inst TechnolCNRSUMR5216Grenoble Image Grenoble France Univ Tunis El Manar Ecole Natl Ingenieurs Tunis Lab Syst Commun LR 99 ES21 Tunis 1002 Tunisia Univ Maine LAUM Lab UMR CNRS 6613 F-72085 Le Mans France

We present an acoustic location system that adopts the time of arrival of the path of maximum amplitude as a signature and estimates the target position through nonparametric kernel regression. The system was evaluated in experiments for two main configurations: a privacy-oriented configuration with code division multiple access operation and a centralized configuration with time division multiple access operation. The effects of the number and positions of sources on the performance of the privacy-oriented system was studied. Moreover, the effect of the number of fingerprint positions on the performance of both systems was investigated. Results showed that our privacy-oriented scheme provides an accuracy of 8.5 cm with 87% precision, whereas our centralized system provides an accuracy of 2.7 cm for 93% of measurements. A comparison between our privacy-oriented system and another acoustic location system based on code division multiple access operation and lateration was conducted on our test bench and revealed that the cumulative error distribution function of the fingerprint-based system is better than that of the lateration-based system. This result is similar to that found for Wi-Fi radio-based localization. However, our experiments are the first to demonstrate the detrimental effect that reverberation has on naive acoustic localization approaches.

关键词： Acoustic Fingerprinting technique nonparametric kernel regression Code division multiple access operation Time division multiple access operation Privacy-oriented configuration Centralized system Lateration

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Uncertainty Estimation in Diffusion MRI Using the Nonlocal Bootstrap

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2014年第8期33卷 1627-1640页

作者： Yap, Pew-Thian An, Hongyu Chen, Yasheng Shen, Dinggang Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Univ N Carolina Biomed Res Imaging Ctr BRIC Chapel Hill NC 27599 USA Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea

In this paper, we propose a new bootstrap scheme, called the nonlocal bootstrap (NLB) for uncertainty estimation. In contrast to the residual bootstrap, which relies on a data model, or the repetition bootstrap, which requires repeated signal measurements, NLB is not restricted by the data structure imposed by a data model and obviates the need for time-consuming multiple acquisitions. NLB hinges on the observation that local imaging information recurs in an image. This self-similarity implies that imaging information coming from spatially distant (nonlocal) regions can be exploited for more effective estimation of statistics of interest. Evaluations using in silico data indicate that NLB produces distribution estimates that are in closer agreement with those generated using Monte Carlo simulations, compared with the conventional residual bootstrap. Evaluations using in vivo data demonstrate that NLB produces results that are in agreement with our knowledge on white matter architecture.

关键词： Bootstrap diffusion magnetic resonance imaging (MRI) estimator nonlocal means nonparametric kernel regression sampling distribution self-similarity uncertainty

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Real time air quality forecasting using integrated parametric and non-parametric regression techniques

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ATMOSPHERIC ENVIRONMENT 2015年 103卷 53-65页

作者： Donnelly, Aoife Misstear, Bruce Broderick, Brian Univ Dublin Trinity Coll Coll Green Dept Civil Struct & Environm Engn Dublin 2 Ireland

This paper presents a model for producing real time air quality forecasts with both high accuracy and high computational efficiency. Temporal variations in nitrogen dioxide (NO2) levels and historical correlations between meteorology and NO2 levels are used to estimate air quality 48 h in advance. nonparametric kernel regression is used to produce linearized factors describing variations in concentrations with wind speed and direction and, furthermore, to produce seasonal and diurnal factors. The basis for the model is a multiple linear regression which uses these factors together with meteorological parameters and persistence as predictors. The model was calibrated at three urban sites and one rural site and the final fitted model achieved R values of between 0.62 and 0.79 for hourly forecasts and between 0.67 and 0.84 for daily maximum forecasts. Model validation using four model evaluation parameters, an index of agreement (IA), the correlation coefficient (R), the fraction of values within a factor of 2 (FAC2) and the fractional bias (FB), yielded good results. The IA for 24 hr forecasts of hourly NO2 was between 0.77 and 0.90 at urban sites and 0.74 at the rural site, while for daily maximum forecasts it was between 0.89 and 0.94 for urban sites and 0.78 for the rural site. R values of up to 0.79 and 0.81 and FAC2 values of 0.84 and 0.96 were observed for hourly and daily maximum predictions, respectively. The model requires only simple input data and very low computational resources. It found to be an accurate and efficient means of producing real time air quality forecasts. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Nitrogen dioxide nonparametric kernel regression Air quality forecasting Statistical modelling

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nonparametric modeling of mass transfer coefficients for air stripping packed towers (Reprinted from the 1995 AWWA Annual Conference Proceedings)

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CANADIAN JOURNAL OF CIVIL ENGINEERING 1996年第2期23卷 549-559页

作者： Narbaitz, RM Djebbar, Y Department of Civil Engineering University of Ottawa Ottawa Ont. K1N 6N5 Canada

Existing parametric correlations have been found to have difficulties in predicting the removal of trace levels of volatile organic chemicals by modern air stripping towers. In this study, a new approach using a nonparametric kernel regression method was used to predict the mass transfer coefficient, K(L)a, of air stripping towers. Although only four variables were used, the predictions are already improved more than 50% as compared with Onda correlation, the best existing parametric correlation. The proposed technique shows a dependency of K(L)a on the liquid now rate which is in good agreement with established theory. Previous parametric approaches were unable to model this relationship correctly.

关键词： mass transfer coefficient air stripping tower volatile organic compound nonparametric kernel regression

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A fast and manufacture-friendly optical proximity correction based on machine learning

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MICROELECTRONIC ENGINEERING 2017年 168卷 15-26页

作者： Ma, Xu Jiang, Shangliang Wang, Jie Wu, Bingliang Song, Zhiyang Li, Yanqiu Beijing Inst Technol Key Lab Photoelect Imaging Technol & Syst Minist Educ China Beijing 100081 Peoples R China Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94706 USA

Pixel-based optical proximity correction (PBOPC) is currently a key resolution enhancement technique to push the resolution limit of optical lithography. However, the increasing scale, density and complexity of modern integrated circuits pose new challenges to both of the OPC computational intensity and mask manufacturability. This paper aims at developing a practical OPC algorithm based on a machine learning technique to effectively reduce the PBOPC runtime and mask complexity. We first divide the target layout into small regions around corners and edge fragments. Using a nonparametric kernel regression technique, these small regions are then filled in by the weighted linear combination of a subset of training OPC examples selected from the pre-calculated libraries. To keep balance between the image fidelity and mask complexity, we use an edge-based OPC (EBOPC) library to synthesize the OPC patterns in non-critical areas, while use another PBOPC library for hotspots. In addition, a post-processing method is developed to refine the regressed OPC pattern so as to guarantee the final image fidelity and mask manufacturability. Experimental results show that, compared to a currently professional PBOPC software, the proposed algorithm can achieve approximately two-fold speedup and more manufacture-friendly OPC patterns. (C) 2016 Published by Elsevier B.V.

关键词： Optical lithography Optical proximity correction Machine learning nonparametric kernel regression Manufacturability

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