Macroeconomic Volatility and Sovereign Asset-Liability Management

For most developing countries, the predominant source of sovereign wealth is commodity related export income. However, over-reliance on commodity related income exposes countries to significant terms of trade shocks due to excessive price volatility. The spillovers are pro-cyclical fiscal policies and macroeconomic volatility problems that if not adequately managed, could have catastrophic economic consequences including sovereign bankruptcy. The aim of this study is to explore new ways of solving the problem in an asset-liability management framework for an exporting country like Ghana. Firstly, I develop an unconditional commodity investment strategy in the tactical mean-variance setting for deterministic returns. Secondly, in continuous time, shocks to return moments induce additional hedging demands warranting an extension of the analysis to a dynamic stochastic setting whereby, the optimal commodity investment and fiscal consumption policies are conditioned on the stochastic realisations of commodity prices. Thirdly, I incorporate jumps and stochastic volatility in an incomplete market extension of the conditional model. Finally, I account for partial autocorrelation, significant heteroskedastic disturbances, cointegration and non-linear dependence in the sample data by adopting GARCH-Error Correction and dynamic Copula-GARCH models to enhance the forecasting accuracy of the optimal hedge ratios used for the state-contingent dynamic overlay hedging strategies that guarantee Pareto efficient allocation. The unconditional model increases the Sharpe ratio by a significant margin and noticeably improves the portfolio value-at-risk and maximum drawdown. Meanwhile, the optimal commodities investment decisions are superior in in-sample performance and robust to extreme interest rate changes by up to 10 times the current rate. In the dynamic setting, I show that momentum strategies are outperformed by contrarian policies, fiscal consumption must account for less than 40% of sovereign wealth, while risky investments must not exceed 50% of the residual wealth. Moreover, hedging costs are reduced by as much as 55% while numerically generating state-dependent dynamic futures hedging policies that reveal a predominant portfolio strategy analogous to the unconditional model. The results suggest buying commodity futures contracts when the country’s current exposure in a particular asset is less than the model implied optimal quantity and selling futures contracts when the actual quantity exported exceeds the benchmark.Open Acces

International portfolio optimisation with integrated currency overlay costs and constraints

International financial portfolios can be exposed to substantial risk from variations of the exchange rates between the countries in which they hold investments. Nonetheless, foreign exchange can both generate extra return as well as loss to a portfolio, hence rather than just being avoided, there are potential advantages to well-managed international portfolios. This paper introduces an optimisation model that manages currency exposure of a portfolio through a combination of foreign exchange forward contracts, thereby creating a “currency overlay” on top of asset allocation. Crucially, the hedging and transaction costs associated with holding forward contracts are taken into account in the portfolio risk and return calculations. This novel extension of previous overlay models improves the accuracy of the risk and return calculations of portfolios. Consequently, more accurate investment decisions are obtained through optimal asset allocation and hedging positions. Our experimental results show that inclusion of such costs significantly changes the optimal decisions. Furthermore, effects of constraints related to currency hedging are examined. It is shown that tighter constraints weaken the benefit of a currency overlay and that forward positions vary significantly across return targets. A larger currency overlay is advantageous at low and high return targets, whereas small overlay positions are observed at medium return targets. The resulting system can hence enhance intelligent expert decision support for financial managers

A dynamic programming approach to constrained portfolios

This paper studies constrained portfolio problems that may involve constraints on the probability or the expected size of a shortfall of wealth or consumption. Our first contribution is that we solve the problems by dynamic programming, which is in contrast to the existing literature that applies the martingale method. More precisely, we construct the non-separable value function by formalizing the optimal constrained terminal wealth to be a (conjectured) contingent claim on the optimal non-constrained terminal wealth. This is relevant by itself, but also opens up the opportunity to derive new solutions to constrained problems. As a second contribution, we thus derive new results for non-strict constraints on the shortfall of inter¬mediate wealth and/or consumption

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

Application of stochastic programming to management of cash flows with FX exposure

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 23/06/2006.In this thesis we formulate a model for foreign exchange (FX) exposure management and multi-currency cash management taking into consideration random fluctuations of exchange rates and net revenues of a multinational firm (MNF). The central decision model used in this thesis is a scenario-based stochastic programming (SP) recourse model. A critical review of alternative scenario generation methods is given followed by analysis of some desirable properties of the scenario tree. The application of matching statistical moments of a probability distribution to generate a multiperiod scenario tree for our problem is described in detail. A four-stage SP decision model is formulated using the random parameter values. This model evaluates currency / cash flows hedging strategies, which provide rolling decisions on the size and timing of the forward positions. We compute an efficient frontier from which an investor can choose an optimal strategy according to his risk and return preferences. The flexibility of the SP model allows an investor to analyse alternative risk-return trading strategies. The model decisions are investigated by making comparisons with decisions based purely on the expected value problem. The investigation shows that there is a considerable improvement to the "spot only" strategy and provides insight into how these decisions are made. The contributions of the thesis are summarised below. (i) The FX forward scenario trees are derived using an arbitrage-free pricing strategy and is in line with modem principles of finance. (ii) Use of the SP model and forward contracts as a tool for hedging decisions is novel. (iii) In particular smoothing of the effects in exchange rates and the smoothing of account receivables are examples of innovative modelling approaches for FX management

Improving Market Risk Management with Heuristic Algorithms

Recent changes in the regulatory framework for banking supervision increase the regulatory oversight and minimum capital requirements for financial institutions. In this thesis, we research active portfolio optimisation techniques with heuristic algorithms to manage new regulatory challenges faced in risk management. We first study if heuristic algorithms can support risk management to find global optimal solutions to reduce the regulatory capital requirements. In a benchmark comparison of variance, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) objective functions combined with different optimisation routines, we show that the Threshold Accepting (TA) heuristic algorithm reduces the capital requirements compared with the Trust-Region (TR) local search algorithm. Secondly, we introduce a new risk management approach based on the Unconditional Coverage test to optimally manage the regulatory capital requirements, while avoiding to over- or underestimate the portfolio risk. In an empirical analysis with TA and TR optimisation, we show that our new approach successfully optimises the portfolio risk-return profile and reduces the capital requirements. Next, we analyse the effect of different estimation techniques on the capital requirements. More specifically, empirical and analytical VaR and CVaR estimation is compared with a simulation-based approach using a multivariate GARCH process. The optimisation is performed using the Population-Based Incremental Learning (PBIL) algorithm. We find that the parametric and empirical distribution assumption generate similar results and neither of them clearly outperforms the other. However, portfolios optimised with the simulation approach reduce the capital requirements by about 11%. Finally, we introduce a global VaR and CVaR hedging approach with multivariate GARCH process and PBIL optimisation. Our hedging framework provides a self-financing hedge that reduces transaction costs by using standardised derivatives. The empirical study shows that the new approach increases the stability of the portfolio while avoiding high transaction costs. The results are compared with benchmark portfolios optimised with a Genetic Algorithm

Time-varying minimum-cost portfolio insurance under transaction costs problem via Beetle Antennae Search Algorithm (BAS)

Portfolio insurance is a hedging strategy which is used to limit portfolio losses without having to sell off stock when stocks decline in value. Consequently, the minimization of the costs related to portfolio insurance is a very important investment strategy. On the one hand, a popular option to solve the static minimum-cost portfolio insurance problem is based on the use of linear programming (LP) methods. On the other hand, the static portfolio selection under transaction costs (PSTC) problem is usually approached by nonlinear programming (NLP) methods. In this article, we define and study the time-varying minimum-cost portfolio insurance under transaction costs (TV-MCPITC) problem in the form of a time-varying nonlinear programming (TV-NLP) problem. Using the Beetle Antennae Search (BAS) algorithm, we also provide an online solution to the static NLP problem. The online solution to a time-varying financial problem is a great technical analysis tool and along with fundamental analysis will enable the investors to make better decisions. To the best of our knowledge, an approach that incorporates modern meta-heuristic optimization techniques to provide a more realistic online solution to the TV-MCPITC problem is original. In this way, by presenting an online solution to a time-varying financial problem we highlight the limitations of static methods. Our approach is also verified by numerical experiments and computer simulations as an excellent alternative to conventional MATLAB methods

Mathematical Analysis in Investment Theory: Applications to the Nigerian Stock Market

This thesis intends to optimise a portfolio of assets from the Nigerian Stock Exchange (NSE) using mathematical analysis in the investment theory to model the Nigerian financial market data better. In this work, we analysed the 82 stocks which were consistently traded in the NSE throughout 4years from August 2009 to August 2013. We attempt to maximise the expected return and minimise the variance of the portfolio by using Markowitz's portfolio selection model and a three-objective linear programming model allocating different percentages of weight to different assets to obtain an optimal/feasible portfolio of the financial sector of the NSM. The mean and the standard deviation served as constraints in the three-objective model used, and we constructed portfolios with the aims of maximising the returns and the Sharpe ratio and minimising the Standard Deviation (Variance) respectively. In another development, we use Random Matrix Theory (RMT) to analyse the Eigen-structure of the empirical correlations, apply the Marchenko-Pastur distribution of eigenvalues of a purely random matrix to investigate the presence of investment-pertinent information contained in the empirical correlation matrix of the selected stocks. We use a hypothesised standard normal distribution of eigenvector components from RMT to assess deviations of the empirical eigenvectors to this distribution for different eigenvalues. We also use the Inverse Participation Ratio to measure the deviation of eigenvectors of the empirical correlation matrix from RMT results. These preliminary results on the dynamics of asset price correlations in the NSE are essential for improving risk-return trade-offs associated with Markowitz's portfolio optimisation in the stock exchange, which we achieve by cleaning up the correlation matrix. Since the variance-covariance method underestimates risk, we employ Monte-Carlo simulations to estimate Value-at-Risk (VaR) and copula for a portfolio of 9 stocks of NSE. The result compared with historical simulation and variance-covariance data. Finally, with the outcome of our simulation and analysis, we were able to select the assets that form the optimal portfolio and the weights allocation to each stock. We were able to provide advice to the investors and market practitioners on how best to invest in the sector of NSE. We propose to measure the extent of closeness or otherwise in selected sectors of the NSE and the Johannesburg Stock Exchange (JSE) in our future work