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In-House Courses

Co-branded courses with experts and institutions involved in activities related to quantitative finance.

Statistical Decision Theory for Portfolio Optimization

Concepts of statistical decision theory

Random Variables
Regression Models
Probabilistic Inequalities

Statistical decision problems

Maximum Likelihood Method
Entropy Maximization
Regression Models
Classification
Statistical Decision Models with Risk and Deviation

Case studies with Portfolio Safeguard

The target audience for the course

The course is focused at practitioners working in the areas of risk management, decision making under uncertainty, and statistics. It can also be used as a supplementary reading for a number of graduate courses including but not limited to those of statistical analysis, models of risk, data mining, stochastic programming, financial engineering, modern portfolio theory, and advanced engineering economy.

Course can serve as a quick introduction into the theory of general error, deviation, and risk measures for the graduate students, engineers, and statisticians interested in modeling and managing risk in various applications such as optimal hedging, portfolio replication, portfolio optimization, cash flow matching.

About this course

The course is based on the book “Statistical Decision Problems” written by professor Stan Uryasev from University of Florida and Michael Zabarankin from Stevens Institute of Technology.

The course structure is being prepared in partnership with American Optimal Decisions company (AORDA).
AORDA provides consulting services on optimization, statistics and developing software in risk management and optimization.

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