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.
co-authored with Ray Hashemi, Azita Bahrami, and Jeffrey Young, Proceedings of the International Conference on Computational Science and Computational Intelligence, forthcoming.
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.
“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.
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.
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.
Journal of Economic Literature, 54(4), 2016, 1551-1580. DOI: 10.1257/jel.54.4.1551.
“The Entropic Valuation of Options: Accounting for Higher Order Moments in High-frequency Data”
Abstract: An alternative approach to the Black-Scholes-Merton formulation of option valuation is the entropy pricing theory. Entropy pricing applies notions of information theory to derive the theoretical value of options. I elaborate further on the maximum entropy formulation of option pricing using a generalized set of moment constraints. Higher order moments contain more information about the price density and characterize the shape of the underlying distribution. In a Monte Carlo study, I present entropies of heavy-tailed distributions and show that entropic call densities vary with constraints and become closer to each other as the order of moments increases. In an empirical analysis using high-frequency S&P 500 index options, I examine the impact of moment constraints on the accuracy of theoretical values. Simulation and empirical evidence suggest that the entropic pricing framework provides more accurate results for heavy-tailed, high-frequency data when higher order moment constraints are imposed.
“Market Manipulation in the Midst of the Pandemic”
co-authored with Viktoria Dalko and Hyeeun Shim.
Abstract: At the onset of the COVID-19 pandemic, the Chicago Board Options Exchange’s volatility index reached heights last experienced during the 2008 financial crisis. Although the World Health Organization announcement of the pandemic and an increasing number of COVID-19 cases and deaths contribute to the high level of volatility, the question remains whether stock market manipulation has increased. This paper provides a framework to capture market manipulation, gives simulation examples, and illustrates empirical evidence of manipulation in financial markets. The framework is applied to the high-frequency volatility index and Australia’s leading share market index. Our findings suggest a higher degree of market manipulation subsequent to the declaration of the pandemic.
“A Hedonic Analysis of the Housing Market: Accounting for Endogenous Marketing Time”
co-authored with Jason Beck.
Abstract: Hedonic modeling can be used to examine the impacts of housing characteristics on selling price. This paper estimates a hedonic price function for single-family houses in Savannah, GA for the period 2007–2016. Digressing from conventional approaches of modeling a reduced-form hedonic price function, we estimate a structural function whereby the house sale price is directly affected by not only the usual house attributes, but also marketing time. Both the home sale price and time on market, however, are endogenously determined. To account for this source of endogeneity, we estimate the structural hedonic function using a parametric control function. The control-function estimator utilizes conditional heteroskedasticity of structural errors in the triangular model. Using this approach, we identify relationship between the house price and its time on market solely based on nonlinearities in the control function without the need to look for excludable instrumental variables for the latter endogenous variable. Our findings suggest that housing prices increase with marketing time.
“Examining the Effects of the European Monetary Union”
co-authored with Kundan Kishor and Suyong Song.
Abstract: We examine treatment effects of joining the European monetary union on macroeconomic 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 joining the union leads to decline in inflation, inflation volatility, real GDP growth and bond yield for the post-EMU formation period. The treatment effects on bond yield and debt show divergent pattern in core and periphery countries after the financial crisis. The bond market in the southern EMU countries benefited disproportionately in the pre-crisis period in terms of lower bond yields.
“Forecasting Marketing Time in the Savannah Housing Market”
co-authored with Ray Hashemi, Jason Beck, Azita Bahrami, and A Galbraith.
Abstract: The number of days that a home stays on the market, also known as Days-On-Market (DOM), provides information about the real estate market. At the micro level, DOM affects the buyer’s and seller’s decision and at the macro level, DOM is a measure of liquidity in the housing market and indicates the level of risk associated with real estate investments. We introduce a data mining method to forecast DOM for homes in savannah.
“The Expected Loss from Sea Level Rise”
co-authored with Ruth Dittrich.
Sea level rise is one of the major consequences of climate change. Global warming leads to a rise in global mean sea level, mainly due to melting of glaciers and ice sheets. 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 estimate the risk-neutral density of change in global mean sea level and then use the estimated density to compute the expected loss from sea level rise.