Special Issue "Recent Advances in Theory and Methods for the Analysis of High Dimensional and High Frequency Financial Data"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Guest Editor
Prof. Norman R. Swanson

Department of Economics, Rutgers University, USA
Website | E-Mail
Interests: financial econometrics; macroeconometrics; time series analysis; forecasting
Guest Editor
Prof. Xiye Yang

Department of Economics, Rutgers University, USA
Website | E-Mail
Interests: econometric theory; financial econometrics; asset pricing; empirical finance

Special Issue Information

Dear Colleagues,

There have been numerous econometric advances made in the fields of empirical and theoretical finance in recent years. Many such advances were initially spurred by recent technological, computing and data collection innovations. In particular, as computing ability and dataset sizes have increased, both empiricists and theoreticians have focused considerable attention on solving key unresolved issues relating to estimation and inference in the study of large datasets used in financial economics. Examples of topics in which important advances have been made include nonparametric and parametric estimation of models (e.g., simulated method of moments, indirect inference, and nonparametric simulated maximum likelihood, among others), and estimation and inference based on point and density estimators of possibly latent variables (e.g., realized measures of integrated volatility, and estimation and accuracy testing of predictive densities or conditional distributions, among others). Recently, considerable attention has also been placed on the development and application of tools useful for the analysis of the high dimensional and/or high frequency datasets that now dominate the landscape. These tools include machine learning, dimension reduction, and shrinkage based data methods, for example. The purpose of this Special Issue is to collect both methodological and empirical papers that develop and utilize state-of-the-art econometric techniques for the analysis of such data.

Prof. Norman R. Swanson
Prof. Xiye Yang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Empirical and theoretical financial econometrics
  • Big data
  • High dimensional and high frequency data

Published Papers (2 papers)

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Research

Open AccessArticle
Covariance Prediction in Large Portfolio Allocation
Econometrics 2019, 7(2), 19; https://doi.org/10.3390/econometrics7020019
Received: 12 November 2018 / Revised: 23 April 2019 / Accepted: 2 May 2019 / Published: 9 May 2019
Cited by 1 | PDF Full-text (365 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. [...] Read more.
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on stocks belonging to the S&P500 index from 2000 to 2017 and sub-periods. The analysis is done through several metrics, including standard deviation, turnover, net average return, information ratio and Sortino’s ratio. We find that no method is the best in all scenarios and the performance depends on the criterion, the period of analysis and the rebalancing strategy. Full article
Open AccessArticle
Using the Entire Yield Curve in Forecasting Output and Inflation
Econometrics 2018, 6(3), 40; https://doi.org/10.3390/econometrics6030040
Received: 17 June 2018 / Revised: 17 August 2018 / Accepted: 21 August 2018 / Published: 29 August 2018
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Abstract
In forecasting a variable (forecast target) using many predictors, a factor model with principal components (PC) is often used. When the predictors are the yield curve (a set of many yields), the Nelson–Siegel (NS) factor model is used in place of the PC [...] Read more.
In forecasting a variable (forecast target) using many predictors, a factor model with principal components (PC) is often used. When the predictors are the yield curve (a set of many yields), the Nelson–Siegel (NS) factor model is used in place of the PC factors. These PC or NS factors are combining information (CI) in the predictors (yields). However, these CI factors are not “supervised” for a specific forecast target in that they are constructed by using only the predictors but not using a particular forecast target. In order to “supervise” factors for a forecast target, we follow Chan et al. (1999) and Stock and Watson (2004) to compute PC or NS factors of many forecasts (not of the predictors), with each of the many forecasts being computed using one predictor at a time. These PC or NS factors of forecasts are combining forecasts (CF). The CF factors are supervised for a specific forecast target. We demonstrate the advantage of the supervised CF factor models over the unsupervised CI factor models via simple numerical examples and Monte Carlo simulation. In out-of-sample forecasting of monthly US output growth and inflation, it is found that the CF factor models outperform the CI factor models especially at longer forecast horizons. Full article
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