This single volume describes the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. With mathematics playing a prominent role, the authors present standard risk-management and asset allocation models and more advanced extensions, discuss the laws in standard models that contributed to the 2008 financial crisis, and talk about current and future banking regulation. Importantly, they also explore algorithmic trading, which currently receives sparse attention in the literature. Their focus on practical issues and their ability to translate difficult risk management material into practice with insights into the difficulties of implementation and techniques for the required parameter estimation set their volume apart from others. By giving coherent recommendations about which statistical models to use for which asset class, they make a real contribution to the sciences of portfolio management and risk management.
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Professor Morton Glantz serves as a financial consultant, educator, and adviser to a broad spectrum of professionals, including corporate financial executives, government ministers, privatization managers, investment and commercial bankers, public accounting firms, members of merger and acquisition teams, strategic planning executives, management consultants, attorneys, and representatives of foreign governments and international banks. Professor Morton Glantz is a principal of Real Consulting and Real Options Valuation, firms specializing in risk consulting, training, certification, and advanced analytical software in the areas of risk quantification, analysis, and management solutions.
As a JP Morgan Chase (heritage bank) senior banker, Professor Glantz built a progressive career path specializing in credit analysis and credit risk management, risk grading systems, valuation models, and professional training. He was instrumental in the reorganization and development of the credit analysis module of the Bank’s Management Training Program-Finance, which at the time was recognized as one of the foremost training programs in the banking industry.
Professor Glantz is on the (adjunct) finance faculty of the Fordham Graduate School of Business. He has appeared in the Harvard University International Directory of Business and Management Scholars and Research, and has earned Fordham University Deans Award for Faculty Excellence on three occasions. He is a Board Member of the International Standards Board, International Institute of Professional Education and Research (IIPER). The IIPER is a global institute with partners and offices around the world, including the United States, Switzerland, Hong Kong, Mexico, Portugal, Singapore, Nigeria, and Malaysia. Professor Glantz is widely published in financial journals and has authored 8 books.
This single volume describes the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. With mathematics playing a prominent role, the authors present standard risk-management and asset allocation models and more advanced extensions, discuss the laws in standard models that contributed to the 2008 financial crisis, and talk about current and future banking regulation. Importantly, they also explore algorithmic trading, which currently receives sparse attention in the literature. Their focus on practical issues and their ability to translate difficult risk management material into practice with insights into the difficulties of implementation and techniques for the required parameter estimation set their volume apart from others. By giving coherent recommendations about which statistical models to use for which asset class, they make a real contribution to the sciences of portfolio management and risk management.|This single volume describes the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. With mathematics playing a prominent role, the authors present standard risk-management and asset allocation models and more advanced extensions, discuss the laws in standard models that contributed to the 2008 financial crisis, and talk about current and future banking regulation. Importantly, they also explore algorithmic trading, which currently receives sparse attention in the literature. Their focus on practical issues and their ability to translate difficult risk management material into practice with insights into the difficulties of implementation and techniques for the required parameter estimation set their volume apart from others. By giving coherent recommendations about which statistical models to use for which asset class, they make a real contribution to the sciences of portfolio management and risk management.
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