Ali Habibnia

Ali Habibnia
Virginia Polytechnic Institute and State University | VT · Department of Economics

Ph.D. Statistics London School of Economics

About

14
Publications
44,708
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Citations
Introduction
I am an Assistant Professor in the Department of Economics and the Computational Modeling and Data Analytics, College of Science, Virginia Tech. My research focuses on the intersection of statistical machine learning and big data econometrics, with a particular interest in the high-dimensional nonlinear time-series analysis and their applications in macroeconomic/financial forecasting and estimation of big financial networks. I received my Ph.D. (2017) from the London School of Economics and Political Science, Department of Statistics.
Additional affiliations
October 2012 - February 2017
The London School of Economics and Political Science
Position
  • PhD Student

Publications

Publications (14)
Article
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors (“big data” environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a neural net with skip-layer connections. We expressly include bo...
Preprint
Full-text available
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors (big data environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of deep neural nets with skip-layer connections. We expressly include...
Preprint
Full-text available
This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a deep neural net with skip-layer connections. We expressly inclu...
Code
Attached MATLAB code is developed to test whether the underlying structure within the recorded data is linear or nonlinear. The nonlinearity measure introduced in Kruger et al (2005) performs a multivariate analysis assessing the underlying relationship within a given variable set by dividing the data series into smaller regions, calculating the su...
Technical Report
Full-text available
* Preliminaries: Ways to get help, File extensions, Common data types, Data import/export, Basic commands, Create basic variables, Basic math functions, Trigonometric functions, Linear algebra, Accessing/assignment elements, Character and string, Regular expression, "IS*" functions, Convert functions, Programming, Errors, Parallel computing (CPU &...
Presentation
Full-text available
Forecasting in Big Data Environments High-dimensional Nonlinear Time Series Analysis Nonlinear Forecasting Using a Large Number of Predictors Forecasting in Big Data Environments with a Shrinkage Estimation of Skip-layer Neural Networks Past, Present and Future of Testing for Nonlinearity in Time Series Motivation and Inspirations, Nonlinear Factor...
Article
Full-text available
Financial instability and its destructive effects on the economy can lead to financial crises due to its contagion or spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives central banks and policy makers the ability to take proper policies in order to stabilize financial markets. Much work is currently b...
Presentation
Full-text available
Machine Learning & Deep Learning Applied to Trading
Thesis
Full-text available
In this thesis, I study high-dimensional nonlinear time series analysis, and its applications in financial forecasting and identifying risk in highly interconnected financial networks. The first chapter is devoted to the testing for nonlinearity in financial time series. I present a tentative classification of the various linearity tests that have...
Poster
Full-text available
This study proposes a nonlinear generalization of factor models based on artificial neural networks for forecasting financial time series with many predictors. http://eprints.lse.ac.uk/62916/
Presentation
Full-text available
Foreign Exchange Rate Risk Measurement and Managemen - Ali Habibnia - LSE Risk & Stochastic Group
Article
Full-text available
In recent years, support vector regressions (SVRs), a novel artificial neural network (ANN) technique, has been successfully used as a nonparametric tool for regression estimation and forecasting time series data. In this thesis, we deal with the application of SVRs in financial markets volatility forecasting.An accurate forecast of volatility is e...
Article
Indubitably investing in the gold market has been one of the most captivating investments in the world over the centuries. The gold standard has been played a key role in the international monetary system here before. It is utilized to mint coins and gold bullion as the money reserves. Because of the volatile and uncertain nature of the paper curre...

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