Download or read book Nonparametric Functional Estimation with Applications to Financial Models written by Yacine Aït-Sahalia and published by . This book was released on 1993 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Functional Estimation and Related Topics written by G.G Roussas and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.
Download or read book Nonparametric Function Estimation Modeling and Simulation written by James R. Thompson and published by SIAM. This book was released on 1990-01-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topics emphasized include nonparametric density estimation as an exploratory device plus the deeper models to which the exploratory analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling, and parallel based algorithms. New methods discussed are quick nonparametric density estimation based techniques for resampling and simulation based estimation techniques not requiring closed form solutions.
Download or read book Nonparametric functional estimation written by B. L. S. Prakasa Rao and published by . This book was released on 1980 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Finance written by Jussi Klemelä and published by John Wiley & Sons. This book was released on 2018-02-28 with total page 849 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end. Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications. Written for the leading edge of finance, Nonparametric Finance: • Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction • Provides risk management guidance through volatility prediction, quantiles, and value-at-risk • Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more • Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles • Provides supplementary R code and numerous graphics to reinforce complex content Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage. Jussi Klemelä, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.
Download or read book Multivariate Nonparametric Regression and Visualization written by Jussi Sakari Klemelä and published by John Wiley & Sons. This book was released on 2014-05-05 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research Multiple examples to demonstrate the applications in the field of finance Sections with formal definitions of the various applied methods for readers to utilize throughout the book Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance.
Download or read book Functional Estimation for Density Regression Models and Processes written by Odile Pons and published by World Scientific. This book was released on 2011 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book
Download or read book Functional Estimation For Density Regression Models And Processes Second Edition written by Odile Pons and published by World Scientific. This book was released on 2023-09-22 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for samples of continuous or discrete processes. The optimization of these procedures is based on the choice of a bandwidth that minimizes an estimation error and the weak convergence of the estimators is proved. This book introduces new mathematical results on statistical methods for the density and regression functions presented in the mathematical literature and for functions defining more complex models such as the models for the intensity of point processes, for the drift and variance of auto-regressive diffusions and the single-index regression models.This second edition presents minimax properties with Lp risks, for a real p larger than one, and optimal convergence results for new kernel estimators of function defining processes: models for multidimensional variables, periodic intensities, estimators of the distribution functions of censored and truncated variables, estimation in frailty models, estimators for time dependent diffusions, for spatial diffusions and for diffusions with stochastic volatility.
Download or read book Multivariate Nonparametric Regression and Visualization written by Jussi Klemel? and published by Wiley-Interscience. This book was released on 2014-05-15 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research Multiple examples to demonstrate the applications in the field of finance Sections with formal definitions of the various applied methods for readers to utilize throughout the book Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance.
Download or read book Functional Estimation For Density Regression Models And Processes written by Odile Pons and published by World Scientific. This book was released on 2011-03-21 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators.
Download or read book Essays on Nonparametric Series Estimation with Application to Financial Econometrics written by Meng-Shiuh Chang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation includes two essays. In the first essay, I proposed an alternative estimator for multivariate densities. This estimator can be characterized as a transformation based estimator. The first stage estimates each marginal density separately. In the second stage, the joint density of estimated marginal cumulative distribution functions (CDF) are approximated by the exponential series estimator. The final estimate is then obtained as the product of the marginal densities and the joint density estimated in the second stage. Extensive Monte Carlo studies show the proposed estimator outperforms kernel estimators in joint density and tail distribution estimation. An illustrative example on estimating the conditional copula density between S & P 500 and FTSE 100 given Hangseng and Nikkei 225 is also discussed. In the second essay, I extended the semiparametric model by Chen and Fan [X. Chen, Y. Fan, Estimation of copula-based semiparametric time series models, Journal of Econometrics 130 (2006) 307-335], and studied a class of univariate copula-based nonparametric stationary Markov models in which the copulas and the marginal distributions are estimated nonparametrically. In particular, I focused on the stationary Markov process of order 1 with continuous state space because it has the beta-mixing property for the analysis of weakly dependent processes. The copula density functions for time series models are approximated by the series estimate on sieve spaces. In this study, a finite dimensional linear space spanned by a sequence of power functions is treated as the sieve space where the estimation space of the copula density function is based. This sieve series estimator can be characterized as the exponential series estimator under mild smoothness conditions. By using the beta-mixing properties, I showed that the copula density function approximated by the exponential series estimator for stationary first-order Markov processes has the same convergence rate as the i.i.d. data. The Monte Carlo simulations show that the proposed estimator outperforms the kernel estimator in the conditional density estimation, except for the Frank copula-based Markov model. In addition, the proposed estimator considerably dominates the kernel estimator when used in the one-step-ahead forecast.
Download or read book Financial Modeling Under Non Gaussian Distributions written by Eric Jondeau and published by Springer Science & Business Media. This book was released on 2007-04-05 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.
Download or read book Nonparametric Functional Data Analysis written by Frédéric Ferraty and published by Springer Science & Business Media. This book was released on 2006-11-22 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.
Download or read book Simulated Nonparametric Estimation of Dynamic Models with Applications to Finance written by Filippo Altissimo and published by . This book was released on 2005 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Econometric Methods written by Qi Li and published by Emerald Group Publishing. This book was released on 2009-12-04 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.
Download or read book Nonparametric Finance written by Jussi Klemelä and published by John Wiley & Sons. This book was released on 2018-03-13 with total page 681 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end. Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications. Written for the leading edge of finance, Nonparametric Finance: • Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction • Provides risk management guidance through volatility prediction, quantiles, and value-at-risk • Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more • Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles • Provides supplementary R code and numerous graphics to reinforce complex content Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage. Jussi Klemelä, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.
Download or read book Essays on Testing Functional Coefficient Models and Applications in Finance written by Xingtong Zhang and published by . This book was released on 2020 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays on specification tests on functional coefficient models via the Fourier transform and applications on conditional asset pricing models. The first essay, "An Asymptotically Efficient Test for Functional Coefficient Models", proposes a consistent test for model specification in a functional coefficient model that uses the discrete Fourier transform of a consistent nonparametric estimator of the random coefficient. As a generalization of the conditional moment tests by Bierens (1980, 1982), it is applicable in testing part of the coefficient functions, rather than testing for all of the them jointly. Although a nonparametric estimation step is included, my method is able to detect local alternatives at a rate of root T, owing to the U-process structure of the test statistics. Monte Carlo studies demonstrate that my method outperforms current nonparametric tests, such as the generalized likelihood ratio test by Fan et al. (2001) and the Wald-typed tests by Li et al. (2002), especially when the sample size decreases and the dimension of the state variables increases. In the second essay titled "A Consistent Model Specification Test for Functional Coefficient Models", I propose a consistent test for model specifications in functional coefficient models via discrete Fourier transform (DFT). The DFT of the sample score function can extract the local property of unknown parameters over the state variable. Therefore, my test avoids nonparametric estimation and is asymptotically more efficient than the existing nonparametric tests. It can detect a class of local alternatives at the parametric rate. Furthermore, my test allows the regressors and the state variables to be the same and is also robust to heteroscedasticity and serial correlation. Simulation studies show that the proposed test has reasonable size and excellent power against various misspecifications of coefficient functions. The two essays both aim at improving the efficiency of the tests over existing nonparametric tests in the literature. Rather than the same, they can be viewed as complement to each other. The first involves a step of nonparametric estimation and thus can test part of coefficient functions, rather than the joint test for all of the coefficient functions in the model. The drawback is that it is not tuning parameter free. The second uses a score function approach which is based on the residuals from the parametric estimation and is free of nonparametric estimation. Both have their merits and limitations and together provide a system of more efficient methods that can be applied in various economics circumstances. The third essay, "How Does Smooth Structural Change Affect Asymmetric Dependence in Foreign Exchange market?", I use a copula approach based on Patton (2006) to model this asymmetric exchange rate dependence and investigate how different but reasonable specification of marginal distribution affect the asymmetric behavior between mark-dollar and yen-dollar exchange rates. Central banks are facing a trade-off between export competitiveness and price stability, which will result in an asymmetric dependence behavior among currencies during joint appreciations versus during joint depreciation. It is plausible that the change pace of the underlying economic mechanism and technological progress can cause the structural change of exchange rate in a country. Furthermore, since the pattern of correlation or dependence structure is determined by second or higher order moment, we would expect the marginal structural change in volatility to change the dependence structure in joint distribution. This chapter can also serve as an empirical evidence of implementing smooth structural change into test for asymmetric correlations, proposed by Hong, Tu and Zhou (2007).
