Resume

• 2005

  New York State Banking Department

  University of Scranton

  New York State Banking Department

• 2004

  IEP at Baruch College

  IEP at Baruch College

  University of Scranton

  Scranton

  Pennsylvania

  Associate Professor

  Trinity College - Hartford

• 2001

  Queens College

  University of Scranton

  College of Staten Island

  City University of New York

  Queens College

  Research and Data Analyst

  Palo Alto

  CA

  Metreo

  College of Staten Island

  City University of New York

  Staten Island

  NY

  Assistant Professor

  Yerevan State University

  Ph.D.

  Financial Economics

• Financial Economics

  Teaching

  Economics

  Quantitative Research

  SQL

  Statistical Modeling

  Macroeconomics

  University Teaching

  Stata

  Matlab

  Higher Education

  Public Speaking

  Eviews

  Quantitative Analytics

  Statistics

  Microeconomics

  Research

  Econometrics

  Qualitative Research

  Data Analysis

  Herd behaviour in the Turkish banking sector.

  This study looks for evidence of investor herding in the Turkish banking sector. We apply the methodology of Chang et al. (2000) to daily stock returns between 2007 and 2012 and find evidence of herding. This result is robust under model specifications that control for market and firm fundamentals. Herding behaviour shows asymmetric effects

  and investors herd only in rising markets.

  Herd behaviour in the Turkish banking sector.

  This study examinesherding behavior in all industrial sectors of the Turkish stock market. Applyingthe methodology of Chang et al. (2000)to the Turkish sectoral daily stock prices from 2002 to 2014

  we found strong evidence ofherding. This evidence did not disappearevenafter we controlled formarketregimesand firm fundamentals. Investor herding is asymmetric in all sectors; even though herding is prevalent inboth rising and falling markets

  it ismore pronounced inrising markets. In the financial

  services

  andtechnology sectorsherding is detected only in the highly volatile markets. In contrast

  in low-volatilitymarketswe confirmherding only in the services sector. \n\nSectoral Herding: Evidence from an Emerging Market. Available from: https://www.researchgate.net/publication/289801944_Sectoral_Herding_Evidence_from_an_Emerging_Market [accessed May 2

  2016].

  Sectoral Herding: Evidence from an Emerging Market

  In this study

  we examine whether house price cycles led or lagged business cycles in the state-level U.S. data from 1979 to 2012. We use a vector Markov-switching model to test for various lead/lag scenarios across the U.S. For the majority of the U.S. states as well as the aggregate U.S.

  we could not reject the hypothesis that between 1979 and 2012 house prices did not lead the economy. We find that between 2002 and 2011

  house prices led the economy in 22 states and nationally. The states where prior to the 2007 recession house prices grew faster than six times the state's population growth rate were almost guaranteed to suffer the economic consequences of the pre-2007 house price decline.

  Business and Real Estate Price Cycles Across the US: Evidence from a Vector Markov-Switching Regression Exercise

  Using a two-period non-stochastic life-cycle model

  Hauenschild and Stahlecker (2001) show that when information about future labor income is ambiguous

  individuals may engage in precautionary savings even if their marginal utility is not convex. We extend the methodology of Houenschild and Stahlecker to a model with standard preferences and demonstrate the precautionary savings that consumers accumulate due to ambiguity and fuzzy decision-making possibly explain the “excess consumption growth puzzle.”

  Ambiguity and the Excess Consumption Growth Puzzle

  This article investigates whether large non-bank institutional investors herded during the dot-com bubble of the 1990s. We use the vector Markov-switching model of Hamilton and Lin (1996) to analyse the technology stockholdings of 115 large institutional investors from 1980 to 2012. By imposing different restrictions on the elements of the transition probability matrix

  we are able to test for various lead/lag scenarios that might have existed between the technology stockholding of each investor and that of the residual market. We find that only 17.4% of the investors in our sample herded during the dot-com bubble. Thus

  during the dot-com bubble

  herding among large institutional investors was not an especially widespread phenomenon. Among those investors that herded

  80% herded during the run-up

  10% during the collapse and 10% during both phases of the dot-com bubble. About 23% of all investors in our sample exited from the technology sector before the bubble collapsed. These results seem to support Abreu and Brunnermeier’s (2003) theory of bubbles and crashes.

  Did large institutional investors flock into the technology herd? An empirical investigation using a vector Markov-switching model.

  Barry Ma

  We show how an extreme value statistical test

  designed under the assumption of normality

  achieves higher power than traditional tests when the underlying distribution turns out to have thicker tails. We also demonstrate the increase in power with numerical simulations. An empirical example with application in risk management is given.

  Power and thick tails: an ARCH process example with extreme value as test statistic

  Aram

  Metreo

  Trinity College - Hartford