14,703 research outputs found
A recursive algorithm for multivariate risk measures and a set-valued Bellman's principle
A method for calculating multi-portfolio time consistent multivariate risk
measures in discrete time is presented. Market models for assets with
transaction costs or illiquidity and possible trading constraints are
considered on a finite probability space. The set of capital requirements at
each time and state is calculated recursively backwards in time along the event
tree. We motivate why the proposed procedure can be seen as a set-valued
Bellman's principle, that might be of independent interest within the growing
field of set optimization. We give conditions under which the backwards
calculation of the sets reduces to solving a sequence of linear, respectively
convex vector optimization problems. Numerical examples are given and include
superhedging under illiquidity, the set-valued entropic risk measure, and the
multi-portfolio time consistent version of the relaxed worst case risk measure
and of the set-valued average value at risk.Comment: 25 pages, 5 figure
Multi-Period Trading via Convex Optimization
We consider a basic model of multi-period trading, which can be used to
evaluate the performance of a trading strategy. We describe a framework for
single-period optimization, where the trades in each period are found by
solving a convex optimization problem that trades off expected return, risk,
transaction cost and holding cost such as the borrowing cost for shorting
assets. We then describe a multi-period version of the trading method, where
optimization is used to plan a sequence of trades, with only the first one
executed, using estimates of future quantities that are unknown when the trades
are chosen. The single-period method traces back to Markowitz; the multi-period
methods trace back to model predictive control. Our contribution is to describe
the single-period and multi-period methods in one simple framework, giving a
clear description of the development and the approximations made. In this paper
we do not address a critical component in a trading algorithm, the predictions
or forecasts of future quantities. The methods we describe in this paper can be
thought of as good ways to exploit predictions, no matter how they are made. We
have also developed a companion open-source software library that implements
many of the ideas and methods described in the paper
Set-valued shortfall and divergence risk measures
Risk measures for multivariate financial positions are studied in a
utility-based framework. Under a certain incomplete preference relation,
shortfall and divergence risk measures are defined as the optimal values of
specific set minimization problems. The dual relationship between these two
classes of multivariate risk measures is constructed via a recent Lagrange
duality for set optimization. In particular, it is shown that a shortfall risk
measure can be written as an intersection over a family of divergence risk
measures indexed by a scalarization parameter. Examples include set-valued
versions of the entropic risk measure and the average value at risk. As a
second step, the minimization of these risk measures subject to trading
opportunities is studied in a general convex market in discrete time. The
optimal value of the minimization problem, called the market risk measure, is
also a set-valued risk measure. A dual representation for the market risk
measure that decomposes the effects of the original risk measure and the
frictions of the market is proved
Superhedging in illiquid markets
We study contingent claims in a discrete-time market model where trading
costs are given by convex functions and portfolios are constrained by convex
sets. In addition to classical frictionless markets and markets with
transaction costs or bid-ask spreads, our framework covers markets with
nonlinear illiquidity effects for large instantaneous trades. We derive dual
characterizations of superhedging conditions for contingent claim processes in
a market without a cash account. The characterizations are given in terms of
stochastic discount factors that correspond to martingale densities in a market
with a cash account. The dual representations are valid under a topological
condition and a weak consistency condition reminiscent of the ``law of one
price'', both of which are implied by the no arbitrage condition in the case of
classical perfectly liquid market models. We give alternative sufficient
conditions that apply to market models with nonlinear cost functions and
portfolio constraints
Asset Allocation under the Basel Accord Risk Measures
Financial institutions are currently required to meet more stringent capital
requirements than they were before the recent financial crisis; in particular,
the capital requirement for a large bank's trading book under the Basel 2.5
Accord more than doubles that under the Basel II Accord. The significant
increase in capital requirements renders it necessary for banks to take into
account the constraint of capital requirement when they make asset allocation
decisions. In this paper, we propose a new asset allocation model that
incorporates the regulatory capital requirements under both the Basel 2.5
Accord, which is currently in effect, and the Basel III Accord, which was
recently proposed and is currently under discussion. We propose an unified
algorithm based on the alternating direction augmented Lagrangian method to
solve the model; we also establish the first-order optimality of the limit
points of the sequence generated by the algorithm under some mild conditions.
The algorithm is simple and easy to implement; each step of the algorithm
consists of solving convex quadratic programming or one-dimensional
subproblems. Numerical experiments on simulated and real market data show that
the algorithm compares favorably with other existing methods, especially in
cases in which the model is non-convex
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