Research

Peer-Reviewed Publications

2022

“Option pricing with maximum entropy densities: The inclusion of higher-order moments”

Journal of Futures Markets, 42(10), 1821-1836. DOI: 10.1002/fut.22361.

“The dynamics of money velocity”

Applied Economics Letters. DOI: 10.1080/13504851.2022.2083062.

“On the comparison of inequality measures: Evidence from the World Values Survey”

co-authored with Mariana Saenz, Applied Economics Letters. DOI: 10.1080/13504851.2022.2118961.

“A recurrent neural network for prediction of the economic and financial indicators in context of the COVID-19 pandemic”

co-authored with Ray Hashemi, Jeffrey Young, and , Azita Bahrami, Proceedings of the International Conference on Computational Science and Computational Intelligence, forthcoming.

2021

“Variants of mixtures: Information properties and applications”

co-authored with Majid Asadi, Nader Ebrahimi, and Ehsan Soofi, Journal of the Iranian Statistical Society, 20(1), 27-59. DOI: 10.52547/jirss.20.1.27.

co-authored with Ray Hashemi, Jeffrey Young, and , Chanchal Tamrakar, Proceedings of the International Conference on Computational Science and Computational Intelligence, 262-267. DOI: 10.1109/CSCI54926.2021.00115.

“Prediction of days-on-market for single-family homes”

co-authored with Keagan Galbraithy, Ray Hashemi, and Jason Beck, Proceedings of the International Conference on Data Science.

2020

“MR plot: A big data tool for distinguishing distributions”

co-authored with Majid Asadi, Nader Ebrahimi, and Ehsan Soofi, Statistical Analysis and Data Mining: The American Statistical Association Data Science Journal, 2020, 13, 405-418. DOI: 10.1002/sam.11464.

“A mediated multi-RNN hybrid system for prediction of stock prices”

co-authored with Ray Hashemi, Azita Bahrami, and Jeffrey Young, Proceedings of the International Conference on Computational Science and Computational Intelligence, 382-387. DOI: 10.1109/CSCI51800.2020.00071.

2018

“Re-evaluating the effectiveness of Inflation Targeting”

co-authored with Kundan Kishor and Suyong Song , Journal of Economic Dynamics and Control, 2018, 90, 76-97. DOI: 10.1016/j.jedc.2018.01.045.

“Ranking forecasts by stochastic error distance, information and reliability measures”

co-authored with Nader Ebrahimi and Ehsan Soofi, International Statistical Review, 2018, 86(3), 442-468. DOI: 10.1111/insr.12250. Online Appendix.

“Examining the success of the central banks in Inflation Targeting countries: The dynamics of the inflation gap and institutional characteristics”

co-authored with Kundan Kishor, Studies in Nonlinear Dynamics and Econometrics, 2018, 22(1). DOI: 10.1515/snde-2016-0085.

“A mining driven decision support system for joining the European monetary union”

co-authored with Ray Hashemi, Azita Bahrami, Jeffrey Young, and Rosina Campbell. Proceedings of the International Conference on Advances in Information Mining and Management, 2018, 39-45. ISBN: 978-1-61208-654-5.

2017

“Extraction of the essential constituents of the S&P 500 index”

co-authored with Ray Hashemi, Azita Bahrami, and Jeffrey Young, Proceedings of the International Conference on Computational Science and Computational Intelligence, 2017, 350-356. DOI: 10.1109/CSCI.2017.59.

2016

“Doctoral dissertations in economics”

Journal of Economic Literature, 54(4), 2016, 1551-1580. DOI: 10.1257/jel.54.4.1551.

Working Papers

The (un)predictability of housing bubbles

Abstract: This paper employs econometric tests of rational bubbles, focusing on housing prices during and after the COVID-19 pandemic. I then examine monetary policy impacts on the housing bubble through a hierarchical Bayesian framework. The S&P/Case Shiller U.S. national home price adjusted by rental is considered for the empirical analysis. The findings suggest that the current housing bubble has not collapsed yet. Also, After the bubble starts, the test statistics exceed their critical values. When the bubble bursts, statistics fall below the critical values. The results also illustrate how home prices respond to the monetary policy socks.

Estimating hedonic models with endogenous marketing time using quantile regression without excluded instruments

co-authored with Jason Beck and Suyong Song.

Abstract: Hedonic modeling has been used to examine the impacts of housing characteristics on selling prices. Digressing from conventional hedonic modeling, we propose a control function approach in quantile regression models to account for heterogeneous effects of endogenous marketing time. Our approach utilizes conditional heteroscedasticity of structural errors in the triangular model as an identification strategy without excluded instruments. We document substantial heterogeneous effects of marketing time across the conditional distribution of housing prices, which show a U-shaped relationship; the marketing time impact is substantially larger for lower and higher quantiles of selling prices than for median selling price.

Does membership of the EMU matter for economic and financial outcomes?

co-authored with Kundan Kishor and Suyong Song.

Abstract: We examine treatment effects of joining the European monetary union (EMU) on macroeconomic and financial outcomes in member countries. Specifically, we apply propensity score analysis to mitigate the self-selection bias associated with the non-random nature of joining the union. The findings suggest that average treatment effect on the treated (ATT) of the EMU is associated with decline in volatility of inflation, real GDP growth and bond yields. Splitting the sample into the pre-crisis (1990-2008) and the post-crisis (2009-2019) periods and exclusion of Portugal, Ireland, Greece and Spain (PIGS) from the sample show divergent pattern of ATTs on bond yields and the debt-GDP ratio. The results suggest that the fiscal situation in the member states that excluded PIGS worsened in the pre-crisis period. We also find that PIGS benefitted from the EMU membership in terms of lower bond yields in the pre-crisis period.

Estimating loss from extreme climate events within a real options approach

co-authored with Ruth Dittrich.

Abstract: Sea level rise is a major consequences of climate change. This paper studies climate change uncertainty through an information theory framework and examines the current cost of extreme sea level rise within a real options analysis. We first propose an approach to estimate the risk-neutral density of change in global mean sea level and then use the estimated density to compute the expected overall cost from sea level rise. The proposed framework accounts for extreme sea level rise in computing the theoretical option value.

An information framework for measuring perception alignment in financial markets

co-authored with Viktoria Dalko and Hyeeun Shim.

Abstract: At the onset of the COVID-19 pandemic, the CBOE volatility index reached heights last experienced during the 2008 financial crisis. The consensus is that the World Health Organization’s announcement of the pandemic contributed to the high level of volatility. The question arises whether we have a potentially robust measure to quantify the degree that investors’ perceptions were suddenly aligned about future asset returns due to a WHO announcement. This paper provides an information framework to propose measuring the degree of perception alignment based on the perception alignment hypothesis. We provide simulation examples and illustrate empirical evidence of financial market manipulation, and estimate the loss of information due to those cases of perception alignment.

A probabilistic view to capture automation impacts

co-authored with Mariana Saenz.

Abstract: This paper examines the effects of automation on the number of transactions, sales, and cost in the foodservice industry. First, a big data tool is applied to distinguish distributions of transactions during different times of the day. Then the automation impacts on transactions are studied using a probabilistic approach in which the best fitting theoretical probability density is used in constructing simulated sampling distributions. Next, the effects of automation on sales and cost are examined through simulated forecasts and sampling distributions. The simulation studies illustrate how automation increases efficiency and improves forecasting accuracy.