8,954 research outputs found
Coherent measurement of factor risks
We propose a new procedure for the risk measurement of large portfolios. It
employs the following objects as the building blocks: - coherent risk measures
introduced by Artzner, Delbaen, Eber, and Heath; - factor risk measures
introduced in this paper, which assess the risks driven by particular factors
like the price of oil, S&P500 index, or the credit spread; - risk contributions
and factor risk contributions, which provide a coherent alternative to the
sensitivity coefficients.
We also propose two particular classes of coherent risk measures called Alpha
V@R and Beta V@R, for which all the objects described above admit an extremely
simple empirical estimation procedure. This procedure uses no model assumptions
on the structure of the price evolution.
Moreover, we consider the problem of the risk management on a firm's level.
It is shown that if the risk limits are imposed on the risk contributions of
the desks to the overall risk of the firm (rather than on their outstanding
risks) and the desks are allowed to trade these limits within a firm, then the
desks automatically find the globally optimal portfolio
RM-CVaR: Regularized Multiple -CVaR Portfolio
The problem of finding the optimal portfolio for investors is called the
portfolio optimization problem. Such problem mainly concerns the expectation
and variability of return (i.e., mean and variance). Although the variance
would be the most fundamental risk measure to be minimized, it has several
drawbacks. Conditional Value-at-Risk (CVaR) is a relatively new risk measure
that addresses some of the shortcomings of well-known variance-related risk
measures, and because of its computational efficiencies, it has gained
popularity. CVaR is defined as the expected value of the loss that occurs
beyond a certain probability level (). However, portfolio optimization
problems that use CVaR as a risk measure are formulated with a single
and may output significantly different portfolios depending on how the
is selected. We confirm even small changes in can result in huge
changes in the whole portfolio structure. In order to improve this problem, we
propose RM-CVaR: Regularized Multiple -CVaR Portfolio. We perform
experiments on well-known benchmarks to evaluate the proposed portfolio.
Compared with various portfolios, RM-CVaR demonstrates a superior performance
of having both higher risk-adjusted returns and lower maximum drawdown.Comment: accepted by the IJCAI-PRICAI 2020 Special Track AI in FinTec
- …