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Hedge Fund
Scholarly Compositions - Featured Authors
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Dr. Stanislav Uryasev
Professor
Department of Industrial and Systems Engineering
University of Florida
Director
Risk Management and Financial Engineering Lab
(RMFE)
Academic Home Page
Brief
Biography:
Stan Uryasev, a
professor at the
University of Florida, is director of the Risk Management and
Financial Engineering Lab. His research is focused on the
development of efficient computer modeling and optimization
techniques and their applications in finance and military projects,
including: 1) Risk management 2) Portfolio optimization; 3) Asset
and Liability modeling; 4) Classification in financial applications,
and 5) Optimal trading strategies. Published three books (monograph
and two edited volumes) and about seventy research papers.
Research:
Research is focused,
mostly on Financial Engineering applications (portfolio
optimization, trading strategies, credit cards scoring),
Risk
Management (Conditional Value-at-Risk,
credit risk), and military applications.
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Master Funds in Portfolio
Analysis With General Deviation Measures
by R.
Tyrrell Rockafellar, Stanislav Uryasev, & Michael Zabarankin
July 5, 2004
Abstract
Generalized measures of deviation are considered as substitutes
for standard deviation in a framework like that of classical
portfolio theory for coping with the uncertainty inherent in
achieving rates of return beyond the risk-free rate. Such
measures, derived for example from conditional value-at-risk and
its variants, can reflect the different attitudes of different
classes of investors. They lead nonetheless to generalized
one-fund theorems in which a more customized version of
portfolio optimization is the aim, rather than the idea that a
single “master fund” might arise from market equilibrium and
serve the interests of all investors.
The results that are obtained cover discrete distributions along
with continuous distributions. They are applicable therefore to
portfolios involving derivatives, which create jumps in
distribution functions at specific gain or loss values, well as
to financial models involving finitely many scenarios.
Furthermore, they deal rigorously with issues that come up at
that level of generality, but have not
received adequate attention, including possible lack of
differentiability of the deviation expression with respect to
the portfolio weights, and the potential nonuniqueness of
optimal weights.
The results also address in detail the phenomenon that if the
risk-free rate lies above a certain threshold, the usually
envisioned master fund must be replaced by one of alternative
type, representing a "net short position" instead of a "net long
position" in the risky instruments. For nonsymmetric deviation
measures, the second type need not just be the reverse of the
first type, and there can sometimes even be an interval for the
risk-free rate in which no master fund of
either type exists. A notion of basic fund, in place of master
fund, is brought in to get around this difficulty and serve as a
single guide to optimality regardless of such circumstances.
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Deviation Measures in Generalized
Linear Regression
by R.
Tyrrell Rockafellar, Stanislav Uryasev, & Michael Zabarankin
University of Florida, Gainesville
December 21, 2002
Abstract
Linear regression is traditionally based on the minimization of
variance, or equivalently, standard deviation, but other
approaches are possible in which standard deviation is replaced
by something more general. A one-to-one correspondence is now
known between risk measures, such as have been introduced for
various applications in finance, and a large class of deviation
measures characterized by simple axioms. Included in that class
are asymmetric measures coming from conditional value-at-risk
and other currently attractive notions. This paper looks at
deviation in that wide sense, formulating the associated problem
of regression and investigating the existence and uniqueness of
the coefficients that constitute a solution. Such coefficients
are characterized in ways that provide a key to their
computation.
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Conditional value-at-risk for
general loss distributions
by R. Tyrrell Rockafellar & Stanislav Uryasev
University of Washington & University of Florida
July, 2001
Abstract
Fundamental properties of conditional value-at-risk (CVaR), as a
measure of risk with significant advantages over value-at-risk (VaR),
are derived for loss distributions in finance that can involve
discreetness. Such distributions are of particular importance in
applications because of the prevalence of models based on
scenarios and finite sampling. CVaR is able to quantify dangers
beyond VaR and moreover it is coherent. It provides optimization
short-cuts which, through linear programming techniques, make
practical many large-scale calculations
that could otherwise be out of reach. The numerical efficiency
and stability of such calculations, shown in several case
studies, are illustrated further with an example of index
tracking.
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Optimization of Conditional
Value-at-Risk
by Tyrrell Rockafellar & Stanislav Uryasev
September 15, 1999
Abstract
A new approach to optimizing or hedging a portfolio of financial
instruments to reduce risk is presented and tested on
applications. It focuses on minimizing Conditional Value-at-Risk
CVaR rather than minimizing Value-at-Risk VaR, but portfolios
with low CVaR necessarily have low VaR as well. CVaR, also
called Mean Excess Loss, Mean Shortfall, or Tail VaR, is anyway
considered to be a more consistent measure of risk than VaR.
Central to the new approach is a technique for portfolio
optimization which calculates VaR and optimizes CVaR
simultaneously. This technique is suitable for use by investment
companies, brokerage firms, mutual funds, and any business that
evaluates risks. It can be combined with analytical or
scenario-based methods to optimize portfolios with large numbers
of instruments, in which case the calculations often come down
to linear programming or nonsmooth programming. The methodology
can be applied also to the optimization of percentiles in
contexts outside of finance.
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