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Neil Shephard - One of the best experts on this subject based on the ideXlab platform.

Michael Mcaleer - One of the best experts on this subject based on the ideXlab platform.

  • Frontiers in Time Series and Financial Econometrics: An Overview
    Research Papers in Economics, 2015
    Co-Authors: Shiqing Ling, Michael Mcaleer, Howell Tong
    Abstract:

    Two of the fastest growing frontiers in Econometrics and quantitative finance are time series and Financial Econometrics. Significant theoretical contributions to Financial Econometrics have been made by experts in statistics, Econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and Financial Econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Levy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non-negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors.

  • Frontiers in Time Series and Financial Econometrics
    Research Papers in Economics, 2015
    Co-Authors: Shiqing Ling, Michael Mcaleer, Howell Tong
    Abstract:

    __Abstract__ Two of the fastest growing frontiers in Econometrics and quantitative finance are time series and Financial Econometrics. Significant theoretical contributions to Financial Econometrics have been made by experts in statistics, Econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and Financial Econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Levy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non-negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors.

  • Frontiers in Time Series and Financial Econometrics: An overview ☆
    Journal of Econometrics, 2015
    Co-Authors: Shiqing Ling, Michael Mcaleer, Howell Tong
    Abstract:

    Two of the fastest growing frontiers in Econometrics and quantitative finance are time series and Financial Econometrics. Significant theoretical contributions to Financial Econometrics have been made by experts in statistics, Econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and Financial Econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Levy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non-negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors.

  • EDITORIAL NOTE: INTRODUCTION TO THE INAUGURAL SPECIAL ISSUE
    Annals of Financial Economics, 2014
    Co-Authors: Michael Mcaleer
    Abstract:

    The Annals of Financial Economics was first published in 2006, and has published in the first eight years a number of theoretical and empirical papers on numerous topics in Financial economics and Financial Econometrics. Now in its ninth year, it is pleasing that the first special issue of the journal will be published in the important area of “Recent Developments in Quantitative Finance”, comprising several interesting and important papers by some leading researchers in Financial Econometrics and empirical finance. BasedonselectedpapersfromtheInternationalConferenceoftheTaiwanFinance

  • Editorial: The Annals of Computational and Financial Econometrics, first issue
    Computational Statistics & Data Analysis, 2012
    Co-Authors: Michael Mcaleer, D. A. Belsley, E. J. Kontoghiorghes, H. K. Van Dijk, Luc Bauwens, Siem Jan Koopman, Alessandra Amendola
    Abstract:

    The Annals of Computational and Financial Econometrics (CFE)will be published as a supplement to the CSDA journal to serve as an outlet for distinguished research papers in computational Econometrics and Financial Econometrics. The high standards of the Annals will make it a valuable resource for econometric research. Each issue will be edited by several Guest Editors and Associated Editors who will be responsible, together with the Annals of CFE Managing Editors and CSDA Coeditors, for the selection of the papers. Of particular interest are papers in important areas of econometric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources of information about the most recent developments in computational and Financial Econometrics that are currently scattered throughout publications in specialized areas. Results presented in the Annals are of substantial interest for academic researchers and professionals in economics, Econometrics, finance and computational fields. The broad field of computational and Financial Econometrics has clearly interested a wide variety of researchers in economics, finance, statistics, mathematics and computing. Examples include Financial time-series analyses that focus on efficient and robust portfolio allocations over time, asset valuations with emphases on option pricing, volatility measurements, models of market microstructure effects, and credit risk. While such studies are often theoretical, they can also have a strong empirical element often associated with a significant computational aspect dealing with issues like high dimensionality and large numbers of observations. Algorithmic developments are also of interest since existing algorithms often do not utilize the best computational techniques for efficiency, stability, or conditioning. So also are developments of environments for conducting Econometrics, which are inherently computer based. Integrated Econometrics packages have grown well over the years, but still have much room for development. Previously the CSDA has published a number of special issues on computational and Financial Econometrics that have addressed computational and numerical methods used in solving theoretical and practical issues associated with econometric algorithms, the impact of computing on Econometrics, specific applications involving computing and Econometrics, and data-analytic methods in finance. These special issues indicate the importance of computing in Econometrics and highlight research opportunities that exist in this discipline. The first issue of the CSDA Annals of Computational and Financial Econometrics comprises 51 papers. It incorporates the 6th CSDA special issue on Computational Econometrics. Authors submitting a paper to the CSDA Annals of CFE should use the Elsevier Editorial System and select Annals of Computational and Financial Econometrics (http://ees.elsevier.com/csda).

Ole E. Barndorff-nielsen - One of the best experts on this subject based on the ideXlab platform.

Cheng Few Lee - One of the best experts on this subject based on the ideXlab platform.

  • Introduction to Financial Econometrics, Mathematics, Statistics, and Machine Learning
    2020
    Co-Authors: Cheng Few Lee
    Abstract:

    The main purpose of this introductory chapter is to give an overview of the following 130 papers, which discuss Financial Econometrics, mathematics, statistics, and machine learning. There are eight sections in this introductory chapter. Section 1 is the introduction, Section 2 discusses Financial Econometrics, Section 3 explores Financial mathematics, and Section 4 discusses Financial statistics. Section 5 of this introductory chapter discusses Financial technology and machine learning, Section 6 explores applications of Financial Econometrics, mathematics, statistics, and machine learning, and Section 7 gives an overview in terms of chapter and keyword classification of the handbook. Finally, Section 8 is a summary and includes some remarks.

  • Financial Econometrics mathematics statistics and Financial technology an overall view
    Review of Quantitative Finance and Accounting, 2020
    Co-Authors: Cheng Few Lee
    Abstract:

    Abstract Based upon my experience in research, teaching, writing textbooks, and editing handbooks and journals, this review paper discusses how Financial Econometrics, mathematics, statistics, and Financial technology can be used in research and teaching for students majoring in quantitative finance. A major portion of this paper discusses essential content of Lee and Lee (Handbook of Financial Econometrics, mathematics, statistics, and machine learning, World Scientific, Singapore, 2020). Then Lee (From east to west: memoirs of a finance professor on academia, practice, and policy, World Scientific, Singapore, 2017), Lee et al. (Financial Econometrics, mathematics and statistics, Springer, New York, 2019a; Machine learning for predicting default of credit card holders and success of kickstarters. Working paper, 2019b), and Lee and Lee (Handbook of Financial Econometrics and statistics, Springer, New York, 2015) are used to enhance the content of this paper. In addition, important and relevant papers, which have been published in different journals are also used to support the issues discussed in this paper. I have found the applications of Financial Econometrics, mathematics, statistics, and technology have improved drastically over the last five decades. Therefore, both practitioners and academicians need to update their skills in this area to compete in both Financial market and academic research.

  • introduction to Financial Econometrics mathematics and statistics
    2019
    Co-Authors: Cheng Few Lee, Hongyi Chen, John Lee
    Abstract:

    In this introduction chapter, we give an overall view of Financial Econometrics and statistics as indicated in the chapter outline. We then discuss the material covered in this book. There are 24 chapters, which are divided into four sections. These four sections are: regression and Financial Econometrics, time-series analysis and its applications, statistical distributions, option pricing model and risk management, and statistics, Ito’s calculus and option pricing model.

  • Handbook of Financial Econometrics and Statistics - Handbook of Financial Econometrics and Statistics
    2015
    Co-Authors: Cheng Few Lee, Jack C. Lee
    Abstract:

    The Handbook of Financial Econometrics and Statistics provides, in four volumes and over 100 chapters, a comprehensive overview of the primary methodologies in Econometrics and statistics as applied to Financial research. Including overviews of key concepts by the editors and in-depth contributions from leading scholars around the world, the Handbook is the definitive resource for both classic and cutting-edge theories, policies, and analytical techniques in the field. Volume 1 (Parts I and II) covers all of the essential theoretical and empirical approaches. Volumes 2, 3, and 4 feature contributed entries that showcase the application of Financial Econometrics and statistics to such topics as asset pricing, investment and portfolio research, option pricing, mutual funds, and Financial accounting research. Throughout, the Handbook offers illustrative case examples and applications, worked equations, and extensive references, and includes both subject and author indices.

  • handbook of Financial Econometrics and statistics
    2015
    Co-Authors: Cheng Few Lee, Jack C. Lee
    Abstract:

    The Handbook of Financial Econometrics and Statistics provides, in four volumes and over 100 chapters, a comprehensive overview of the primary methodologies in Econometrics and statistics as applied to Financial research. Including overviews of key concepts by the editors and in-depth contributions from leading scholars around the world, the Handbook is the definitive resource for both classic and cutting-edge theories, policies, and analytical techniques in the field. Volume 1 (Parts I and II) covers all of the essential theoretical and empirical approaches. Volumes 2, 3, and 4 feature contributed entries that showcase the application of Financial Econometrics and statistics to such topics as asset pricing, investment and portfolio research, option pricing, mutual funds, and Financial accounting research. Throughout, the Handbook offers illustrative case examples and applications, worked equations, and extensive references, and includes both subject and author indices.

Frank J. Fabozzi - One of the best experts on this subject based on the ideXlab platform.

  • Financial Econometrics Tools for Asset Management
    2021
    Co-Authors: Frank J. Fabozzi, Francesco A. Fabozzi, Marcos Lopez De Prado, Stoyan V. Stoyanov
    Abstract:

    The following sections are included:Learning ObjectivesIntroductionLinear RegressionPrincipal Component AnalysisTime Series Models for the Dynamics of VolatilityKey PointsReferences

  • selecting computational models for asset management Financial Econometrics versus machine learning is there a conflict
    The Journal of Portfolio Management, 2020
    Co-Authors: Joseph A. Cerniglia, Frank J. Fabozzi
    Abstract:

    Successful forecasting requires integrating Financial theory, market behavior, exploding sources of data, and computational innovation. Building accurate computational models can be achieved by assembling the most comprehensive toolbox. Both Financial Econometrics and machine learning approaches help to achieve this objective. Machine learning tools provide the ability to make more accurate predictions by accommodating nonlinearities in data, understanding complex interaction among variables, and allowing the use of large, unstructured datasets. The tools of Financial Econometrics remain critical in answering questions related to inference among the variables describing economic relationships in finance; when properly applied, their role has not diminished with the introduction of machine learning. TOPICS:Big data/machine learning, simulations, statistical methods Key Findings • Successful forecasting requires integration of Financial theory, market behavior, exploding sources of data, and computational innovation. • The well-known documented stylized statistical factors associated with Financial market variables and their importance in choosing and applying the proper statistical techniques and machine learning algorithms are discussed. • Guidelines are provided for integrating the characteristics of data, inference features of Financial Econometrics, and prediction capabilities from machine learning.

  • Selecting Computational Models for Asset Management: Financial Econometrics versus Machine Learning—Is There a Conflict?
    The Journal of Portfolio Management, 2020
    Co-Authors: Joseph A. Cerniglia, Frank J. Fabozzi
    Abstract:

    Successful forecasting requires integrating Financial theory, market behavior, exploding sources of data, and computational innovation. Building accurate computational models can be achieved by assembling the most comprehensive toolbox. Both Financial Econometrics and machine learning approaches help to achieve this objective. Machine learning tools provide the ability to make more accurate predictions by accommodating nonlinearities in data, understanding complex interaction among variables, and allowing the use of large, unstructured datasets. The tools of Financial Econometrics remain critical in answering questions related to inference among the variables describing economic relationships in finance; when properly applied, their role has not diminished with the introduction of machine learning. TOPICS:Big data/machine learning, simulations, statistical methods Key Findings • Successful forecasting requires integration of Financial theory, market behavior, exploding sources of data, and computational innovation. • The well-known documented stylized statistical factors associated with Financial market variables and their importance in choosing and applying the proper statistical techniques and machine learning algorithms are discussed. • Guidelines are provided for integrating the characteristics of data, inference features of Financial Econometrics, and prediction capabilities from machine learning.

  • Practical Applications of Issues in Applying Financial Econometrics to Factor-Based Modeling in Investment Management
    Practical Applications, 2016
    Co-Authors: Robert F. Engle, Sergio M. Focardi, Frank J. Fabozzi
    Abstract:

    Factor models can be powerful tools for the management of trading strategies, portfolio management and risk control, but implementation can be tricky. In Issues in Applying Financial Econometrics to Factor-Based Modeling in Investment Management, authors Robert Engle (NYU Stern School of Business), Sergio Focardi (Pole Universitaire Leonard De Vinci) and Frank Fabozzi (EDHEC) provide a practical approach for investors who want to avoid the most common pitfalls of factor-based modeling—especially overfitting and the curse of dimensionality. The authors identify three major challenges: The appropriate choice of type and quantity of factors, the issue of under- and overfitting, and biased backtesting results.

  • Encyclopedia of Financial Models - Scope and Methods of Financial Econometrics
    Encyclopedia of Financial Models, 2012
    Co-Authors: Sergio M. Focardi, Frank J. Fabozzi
    Abstract:

    Financial Econometrics is the Econometrics of Financial markets. It is a quest for models that describe Financial time series such as prices, returns, interest rates, Financial ratios, defaults, and so on. The economic equivalent of the laws of physics, Econometrics represents the quantitative, mathematical laws of economics. The development of a quantitative, mathematical approach to economics started at the end of the 19th century in a period of great enthusiasm for the achievements of science and technology. Robert Engle and Clive Granger, two econometricians who shared the 2003 Nobel Prize in Economics Sciences, have contributed greatly to the field of Financial Econometrics. Keywords: Static models; Dynamic models; data generating process; white noise; model selection; model estimation; model testing; ex novo; Statistical learning; probabilistic models; nonstationary time series; stationary time series; High-frequency data; model risk; model robustness; ex ante; model performance