8,650 research outputs found
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
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
Cooperative Energy Trading in CoMP Systems Powered by Smart Grids
This paper studies the energy management in the coordinated multi-point
(CoMP) systems powered by smart grids, where each base station (BS) with local
renewable energy generation is allowed to implement the two-way energy trading
with the grid. Due to the uneven renewable energy supply and communication
energy demand over distributed BSs as well as the difference in the prices for
their buying/selling energy from/to the gird, it is beneficial for the
cooperative BSs to jointly manage their energy trading with the grid and energy
consumption in CoMP based communication for reducing the total energy cost.
Specifically, we consider the downlink transmission in one CoMP cluster by
jointly optimizing the BSs' purchased/sold energy units from/to the grid and
their cooperative transmit precoding, so as to minimize the total energy cost
subject to the given quality of service (QoS) constraints for the users. First,
we obtain the optimal solution to this problem by developing an algorithm based
on techniques from convex optimization and the uplink-downlink duality. Next,
we propose a sub-optimal solution of lower complexity than the optimal
solution, where zero-forcing (ZF) based precoding is implemented at the BSs.
Finally, through extensive simulations, we show the performance gain achieved
by our proposed joint energy trading and communication cooperation schemes in
terms of energy cost reduction, as compared to conventional schemes that
separately design communication cooperation and energy trading
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