Stochastic Optimal Control and the U.S. Financial Debt Crisis [electronic resource] /
Stein, Jerome L.author.
Stochastic Optimal Control (SOC)—a mathematical theory concerned with minimizing a cost (or maximizing a payout) pertaining to a controlled dynamic process under uncertainty—has proven incredibly helpful to understanding and predicting debt crises and evaluating proposed financial regulation and risk management. Stochastic Optimal Control and the U.S. Financial Debt Crisis analyzes SOC in relation to the 2008 U.S. financial crisis, and offers a detailed framework depicting why such a methodology is best suited for reducing financial risk and addressing key regulatory issues.  Topics discussed include the inadequacies of the current approaches underlying financial regulations, the use of SOC to explain debt crises and superiority over existing approaches to regulation, and the domestic and international applications of SOC to financial crises.  Principles in this book will appeal to economists, mathematicians, and researchers interested in the U.S. financial debt crisis and optimal risk management. Jerome L. Stein has been an emeritus professor of economics at Brown University since 1993, and has served as a visiting professor of applied mathematics since 1997. He is the author of nine research monographs, and has published over 100 journal articles in such leading publications as American Economic Review, Review of Economics and Statistics, Journal of Banking and Finance, and Contemporary Mathematics. He has served on the editorial boards of the Journal of Finance, American Economic Review, Journal of International and Comparative Economics, and the Journal of Banking and Finance.
Description based upon print version of record.
Includes bibliographical references and index.
Introduction/preface  -- Failure of the Fed, IMF, academic profession to anticipate the crisis, disregarded warnings -- Failure of the Quants, mathematical finance models -- Philosophy of Stochastic optimal control approach, relation to M-V analysis; Sensitivity of optimal debt and risk to alternative stochastic processes, Early Warning Signals -- Application of Stochastic Optimal Control to Financial crisis 2007-08 -- AIG in the crisis -- Crises in the 1980s: Agricultural, S&L -- Diversity of debt crises in Euro. .
Stochastic Optimal Control (SOC)—a mathematical theory concerned with minimizing a cost (or maximizing a payout) pertaining to a controlled dynamic process under uncertainty—has proven incredibly helpful to understanding and predicting debt crises and evaluating proposed financial regulation and risk management. Stochastic Optimal Control and the U.S. Financial Debt Crisis analyzes SOC in relation to the 2008 U.S. financial crisis, and offers a detailed framework depicting why such a methodology is best suited for reducing financial risk and addressing key regulatory issues.  Topics discussed include the inadequacies of the current approaches underlying financial regulations, the use of SOC to explain debt crises and superiority over existing approaches to regulation, and the domestic and international applications of SOC to financial crises.  Principles in this book will appeal to economists, mathematicians, and researchers interested in the U.S. financial debt crisis and optimal risk management. Jerome L. Stein has been an emeritus professor of economics at Brown University since 1993, and has served as a visiting professor of applied mathematics since 1997. He is the author of nine research monographs, and has published over 100 journal articles in such leading publications as American Economic Review, Review of Economics and Statistics, Journal of Banking and Finance, and Contemporary Mathematics. He has served on the editorial boards of the Journal of Finance, American Economic Review, Journal of International and Comparative Economics, and the Journal of Banking and Finance.
Finance.
Probabilities.
Statistics .
Finance, general.
Probability Theory and Stochastic Processes.
Statistics for Business, Management, Economics, Finance, Insurance.