Monte Carlo Simulations and Capital Structure Research

Authors


  • *We are grateful to a number of individuals for comments on our earlier working papers, from which some of this material is drawn. We thank Heitor Almeida, Malcolm Baker, Sugato Bhattacharyya, Christine Brown, Murillo Campello, Howard Chan, Eric Chang, Long Chen, Kevin Davis, Doug Foster, Murray Frank, Fangjian Fu, John Graham, Bruce Grundy, Campbell Harvey, Gilles Hilary, Armen Hovakimian, Nengjiu Ju, Ayla Kayhan, Laura Liu, Peter MacKay, Salih Neftci, Douglas Rolph, Nilanjan Sen, Lewis Tam, Sheridan Titman, Ivo Welch, Mungo Wilson, Xueping Wu, and especially Michael Lemmon (AFA 2007 discussant), Jie Gan, Vidhan Goyal, and Michael Roberts. We also thank seminar participants at the 11th Finsia–Melbourne Centre Banking and Finance Conference, 2007 American Finance Association meetings, Arizona State University, Chinese University of Hong Kong, City University of Hong Kong, Hong Kong Baptist University, University of Macau, Hong Kong University of Science and Technology, Nanyang Technological University, National University of Singapore, Singapore Management University, University of Hong Kong, University of New South Wales Research Camp 2006, and University of Southern California. Chang acknowledges financial support from Academic Research Fund Tier 1 provided by Ministry of Education (Singapore) under grant numbers SUG FY08, M58010006. Dasgupta acknowledges financial support from Hong Kong's Research Grants Council under grant # HKUST6451/05H.

Sudipto Dasgupta
Department of Finance
Hong Kong University of Science and Technology
Clear Water Bay
Kowloon
Hong Kong
dasgupta@ust.hk

ABSTRACT

The evolution of the debt ratio under alternative types of managerial behavior can generate non-standard leverage processes. This creates problems for statistical inference in empirical capital structure research. We argue in this paper that when the data generating process is not standard, a useful way to evaluate the appropriateness of inferences and the empirical methodology is via Monte Carlo simulations that mimic the data generating process under alternative assumptions about managerial behavior. We illustrate with several examples.

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