Resume

• 2012

  Thai

  English

  Doctor of Philosophy (Ph.D.)

  Agribusiness and Managerial Economics

  Texas A&M University

  3.875/4.000

• 2008

  Master's Degree

  Economics

  University of Warwick

  Distinction

• 2007

  Exchange Student

  Economics and Finance

  Tilburg University

  9.0/10.0

  Statistical Computations

  Microeconomic Theory I

  Finance Doctoral Seminar: Theoretical Asset Pricing

  Applied Simulation in Agricultural Economics

  Microeconomic Theory II

  Finance Doctoral Seminar: Empirical Asset Pricing

  Special Topics in Dynamic Optimization

  Frontiers in Agribusiness and Managerial Economics

  Econometrics I

  Macroeconomic Theory I

  Fundamentals in Agricultural Markets and Information Economics

  Agribusiness Markets and Applied Welfare Analysis

  Fundamentals in Agribusiness and Managerial Economics

  Econometrics II

  Macroeconomic Theory II

• 2004

  Bachelor's Degree

  Economics

  Chulalongkorn University

  4.00/4.00

• R

  Matlab

  VBA

  Statistics

  Stata

  Financial Analysis

  Research

  Microsoft Excel

  SAS

  Microsoft Office

  LaTeX

  Interdependence of Oil Prices and Stock Market Indices: A Copula Approach

  Ximing Wu

  David Leatham

  Tatevik Zohrabyan

  In this paper we study the relationship between the oil price and stock market index of various countries between 1982 and 2007. We exclude oil and gas stock companies from the stock indices to remove the obvious direct linkage. Oil price series are converted into local currency to account for possible exchange rate effects. The method of copula is used to model the general dependence between stock returns and oil price returns. Our findings suggest a weak dependence between oil prices and stock indices for most cases

  which are consistent with the results from previous studies. Exceptions are for the stock index returns of large oil consuming and producing countries (United States and Canada)

  which are shown to have a relatively strong dependence with the oil price series. The introduction of Euro in 1999 altered considerably dependence between oil prices and stock returns.\n

  Interdependence of Oil Prices and Stock Market Indices: A Copula Approach

  David Leatham

  Hankyeung Choi

  Contemporary Economics

  Given the emerging consensus from previous studies that crude oil and refined product (as well as crack spread) prices are cointegrated

  this study examines the link between the crude oil spot and crack spread derivatives markets. Specifically

  the usefulness of the two crack spread derivatives products (namely

  crack spread futures and the ETF crack spread) for modeling and forecasting daily OPEC crude oil spot prices is evaluated. Based on the results of a structural break test

  the sample is divided into pre-crisis

  crisis

  and post-crisis periods. We find a unidirectional relationship from the two crack spread derivatives markets to the crude oil spot market during the post-crisis period. In terms of forecasting performance

  the forecasting models based on crack spread futures and the ETF crack spread outperform the Random Walk Model (RWM)

  both in-sample and out-of-sample. In addition

  on average

  the results suggest that information from the ETF crack spread market contributes more to the forecasting models than information from the crack spread futures market.

  Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds

  David Leatham

  Purpose\n– The purpose of this paper is to examine the degree of dependence and extreme correlation (i.e. tail dependence) among US industry sectors.\n\nDesign/methodology/approach\n– This paper makes use of both conventional measures of dependence (the Pearson’s correlation coefficient

  Spearman’s rho and Kendall’s tau) and copula measures of extreme correlations (including the same-direction and cross-tail dependence coefficients) to explore sector diversification opportunities. The paper splits the full sample in three periods

  namely

  1995 to 2000

  2001 to 2006 and 2007 to 2012

  to access the extent to which the dependence results change through time.\n\nFindings\n– This research provides three important findings. First

  the degree of dependence and same-direction extreme correlations are high

  whereas the cross-extreme correlations are considerably low. Second

  the sector pairs offering the best and worst tail diversification change across sample periods. Third

  the traditional dependence measures suggest that benefits for sector diversification have decreased over all sample periods

  while the potential for sector diversification during extreme events has just started to disappear in the most recent period.\n\nPractical implications\n– An investor should consider both the normal co-movements and extreme co-movements among sector indices to maximize diversification benefits.\n\nOriginality/value\n– Given the limited empirical investigations of the degree of dependence and extreme correlation at a sector level

  the results from this research should provide additional and valuable information for both investors and empirical researchers about portfolio diversification and risk management.

  Dependence and extreme correlation among US industry sectors

  David Leatham

  Ryan Larsen

  Agricultural Finance Review

  Purpose\n– Portfolio theory suggests that geographical diversification of production units could potentially help manage the risks associated with farming

  yet little research has been done to evaluate the effectiveness of a geographical diversification strategy in agriculture. The paper aims to discuss this issue.\n\nDesign/methodology/approach\n– The paper utilizes several tools from modern finance theory

  including Conditional Value-at-Risk (CVaR) and copulas

  to construct a model for the evaluation of a diversification strategy. The proposed model – the copula-based mean-CVaR model – is then applied to the producer’s acreage allocation problem to examine the potential benefits of risk reduction from a geographical diversification strategy in US wheat farming. Along with the copula-based model

  the multivariate-normal mean-CVaR model is also estimated as a benchmark.\n\nFindings\n– The mean-CVaR optimization results suggest that geographical diversification is a viable risk management strategy from a farm’s profit margin perspective. In addition

  the copula-based model appears more appropriate than the traditional multivariate-normal model for conservative agricultural producers who are concerned with the extreme losses of farm profitability in that the later model tends to underestimate the minimum level of risk faced by the producers for a given level of profitability.\n\nOriginality/value\n– The effectiveness of geographical diversification in US wheat farming is evaluated. As a methodological contribution

  the copula approach is used to model the joint distribution of profit margins and CVaR is employed as a measure of downside risk.

  Geographical Diversification in Wheat Farming: A Copula-Based CVaR Framework

  David Leatham

  One of the most popular risk management strategies for wheat producers is varietal diversification. Previous studies proposed a mean-variance model as a tool to optimally select wheat varieties. However

  this study suggests that the mean–expected shortfall (ES) model (which is based on a downside risk measure) may be a better tool because variance is not a correct risk measure when the distribution of wheat variety yields is multivariate nonnormal. Results based on data from Texas Blacklands confirm our conjecture that the mean-ES framework performs better in term of selecting wheat varieties than the mean-variance method.

  Mean-Variance versus Mean-Expected Shortfall Models: An Application to Wheat Variety Selection

  David Leatham

  Hankyeung Choi

  This article employs a variety of econometric models (including OLS

  VEC/VAR

  DCC GARCH and a class of copula-based GARCH models) to estimate optimal hedge ratios for gasoline spot prices using gasoline exchange-traded funds (ETFs) and gasoline futures contracts. We then compare their performance using four different measures from the perspective of both their hedging objectives and trading position using four different measures: variance reduction measure

  utility-based measure and two tail-based measures (value at risk and expected shortfall). The impact of the 2008 financial market crisis on hedging performance is also investigated. Our findings indicate that

  in terms of variance reduction

  the static models (OLS and VEC/VAR) are found to be the best hedging strategies. However

  more sophisticated time-varying hedging strategies could outperform the static hedging models when the other measures are used. In addition

  ETF hedging is a more effective hedging strategy than futures hedging during the high-volatility (crisis) period

  but this is not always the case during the normal time (post-crisis) period.

  Optimal Gasoline Hedging Strategies Using Futures Contracts and Exchange-Traded Funds

  Sukcharoen

  Texas A&M University

  West Texas A&M University

  Amarillo

  Texas Area

  Assistant Professor

  West Texas A&M University

  Texas A&M University

  Texas A&M University

  Bryan/College Station

  Texas Area

  Research Assistant