11,207 research outputs found
Bayesian emulation for optimization in multi-step portfolio decisions
We discuss the Bayesian emulation approach to computational solution of
multi-step portfolio studies in financial time series. "Bayesian emulation for
decisions" involves mapping the technical structure of a decision analysis
problem to that of Bayesian inference in a purely synthetic "emulating"
statistical model. This provides access to standard posterior analytic,
simulation and optimization methods that yield indirect solutions of the
decision problem. We develop this in time series portfolio analysis using
classes of economically and psychologically relevant multi-step ahead portfolio
utility functions. Studies with multivariate currency, commodity and stock
index time series illustrate the approach and show some of the practical
utility and benefits of the Bayesian emulation methodology.Comment: 24 pages, 7 figures, 2 table
The Failure of Uncovered Interest Parity: Is it Near-rationality in the Foreign Exchange Market?
A risk-averse US investor adjusts the shares of a portfolio of short-term nominal domestic and foreign assets to maximize expected utility. The optimal strategy is to respond immediately to all new information which arrives weekly. We calculate the expected utility foregone when the investor abandons the optimal strategy and instead optimizes less frequently. We also consider the cases where the investor ignores the covariance between returns sourced in different countries, and where the investor makes unsystematic mistakes when forming expectations of exchange rate changes. We demonstrate that the expected utility cost of sub-optimal behaviour is generally very small. Thus, for example, if investors adjust portfolio shares every three months, they incur an average expected utility loss equivalent to about 0.16% p.a. It is therefore plausible that slight opportunity costs of frequent optimization may outweigh the benefits. This result may help explain the failure of uncovered interest parity.
International diversification with securitized real estate and the veiling glare from currency risk
This paper analyzes diversification benefits from international securitized real estate in a mixed-asset context. We apply regression-based mean-variance efficiency tests, conditional on currency-unhedged and fully hedged portfolios to account for foreign exchange risk exposure. From the perspective of a US investor, it is shown that first, international diversification is superior to a US mixed-asset portfolio, second, adding international real estate to an already internationally diversified stock and bond portfolio results in a further significant improvement of the risk-return trade-off and, third, considering unhedged international assets could lead to biased asset allocation decisions not realizing the true diversification benefits from international assets. Our in-sample results are quite robust in out-of-sample analysis and when investment frictions like short selling constraints are introduced. --Diversification Benefits,International Mixed-Asset Portfolios,Currency Hedging,Spanning Tests,Short Selling Constraints
Portfolio Choice with Stochastic Investment Opportunities: a User's Guide
This survey reviews portfolio choice in settings where investment
opportunities are stochastic due to, e.g., stochastic volatility or return
predictability. It is explained how to heuristically compute candidate optimal
portfolios using tools from stochastic control, and how to rigorously verify
their optimality by means of convex duality. Special emphasis is placed on
long-horizon asymptotics, that lead to particularly tractable results.Comment: 31 pages, 4 figure
Algorithm Portfolios for Noisy Optimization
Noisy optimization is the optimization of objective functions corrupted by
noise. A portfolio of solvers is a set of solvers equipped with an algorithm
selection tool for distributing the computational power among them. Portfolios
are widely and successfully used in combinatorial optimization. In this work,
we study portfolios of noisy optimization solvers. We obtain mathematically
proved performance (in the sense that the portfolio performs nearly as well as
the best of its solvers) by an ad hoc portfolio algorithm dedicated to noisy
optimization. A somehow surprising result is that it is better to compare
solvers with some lag, i.e., propose the current recommendation of best solver
based on their performance earlier in the run. An additional finding is a
principled method for distributing the computational power among solvers in the
portfolio.Comment: in Annals of Mathematics and Artificial Intelligence, Springer
Verlag, 201
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A review of portfolio planning: Models and systems
In this chapter, we first provide an overview of a number of portfolio planning models
which have been proposed and investigated over the last forty years. We revisit the
mean-variance (M-V) model of Markowitz and the construction of the risk-return
efficient frontier. A piecewise linear approximation of the problem through a
reformulation involving diagonalisation of the quadratic form into a variable
separable function is also considered. A few other models, such as, the Mean
Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the
Minimax (MM) model which use alternative metrics for risk are also introduced,
compared and contrasted. Recently asymmetric measures of risk have gained in
importance; we consider a generic representation and a number of alternative
symmetric and asymmetric measures of risk which find use in the evaluation of
portfolios. There are a number of modelling and computational considerations which
have been introduced into practical portfolio planning problems. These include: (a)
buy-in thresholds for assets, (b) restriction on the number of assets (cardinality
constraints), (c) transaction roundlot restrictions. Practical portfolio models may also
include (d) dedication of cashflow streams, and, (e) immunization which involves
duration matching and convexity constraints. The modelling issues in respect of these
features are discussed. Many of these features lead to discrete restrictions involving
zero-one and general integer variables which make the resulting model a quadratic
mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the
algorithms and solution methods for this class of problems are also discussed. The
issues of preparing the analytic data (financial datamarts) for this family of portfolio
planning problems are examined. We finally present computational results which
provide some indication of the state-of-the-art in the solution of portfolio optimisation
problems
Fuzziness and Funds Allocation in Portfolio Optimization
Each individual investor is different, with different financial goals,
different levels of risk tolerance and different personal preferences. From the
point of view of investment management, these characteristics are often defined
as objectives and constraints. Objectives can be the type of return being
sought, while constraints include factors such as time horizon, how liquid the
investor is, any personal tax situation and how risk is handled. It's really a
balancing act between risk and return with each investor having unique
requirements, as well as a unique financial outlook - essentially a constrained
utility maximization objective. To analyze how well a customer fits into a
particular investor class, one investment house has even designed a structured
questionnaire with about two-dozen questions that each has to be answered with
values from 1 to 5. The questions range from personal background (age, marital
state, number of children, job type, education type, etc.) to what the customer
expects from an investment (capital protection, tax shelter, liquid assets,
etc.). A fuzzy logic system has been designed for the evaluation of the answers
to the above questions. We have investigated the notion of fuzziness with
respect to funds allocation.Comment: 21 page
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