Optimal Reserve Holdings, Strategic Asset Allocation and Multiple-Goal Investment Plan for Sovereign Wealth Fund of China

This thesis expounds China’s foreign reserve policy and the investment management of the reserves in a behavioural approach. The research provides a behavioural explanation of China’s reserve accumulation, which is based on the optimal decision making under uncertainty. Then the thesis proposes a multiple-goal framework for strategic asset allocation of China’s reserve management and for the investment decision of Chinese Sovereign Wealth Fund (SWF). The research first tackles the reserve accumulation puzzle in China, by incorporating loss aversion and narrow framing into the utility maximisation of the representative agent who makes the decision of wealth allocation between consumption and saving under uncertainty. Due to China’s policy maker’s subscription to promoting GDP growth as the primary political goal, it is reasonable to assume that the policy maker as a representative agent derives utility not only from consumption but also from fluctuations of the value of GDP/income. This agent evaluates the possible uninsured risk of GDP fluctuation narrowly and tends to exhibit the attribute of loss aversion relative to her growth expectation as the reference point. Under the influence of loss aversion and narrow framing, the more the policy maker cares about GDP growth, the more she needs reserve assets as a precautionary means that may provide self-insurance against uninsured income risk. Such cognitive biases enhance the agent’s precautionary motive for foreign reserves in an uncertain world, which in turn leads her to believing in an optimal level of foreign reserves that is higher than that under conventional models with rational agents. Hence, this heightens the accumulation of foreign reserves in China. Second, this thesis develops a new construction of strategic asset allocation for central banks’ management of foreign reserves by way of embedding the Black-Litterman (B-L) model into the mean variance mental accounting (MVMA) framework. While the MVMA measure suggests a multiple-objective framework that may embrace the traditional objectives of reserve management, i.e. safety, liquidity and profitability, it is based on the mean-variance approach, which suffers from profound deficiencies such as the unrealistic objective function that it relies on and the tendency that the methods are prone to undue influences of outliers. So, the B-L model is applied in this study to form forward-looking return forecasts. This method allows us to overcome the error-maximising influences of the mean-variance optimization. Furthermore, one can combine the implied equilibrium excess returns as investors’ investment views to form priors for Bayesian estimation. The optimal asset allocation then can be derived in this framework, which is applied to practical use in the context of China. The third main Chapter of this thesis concerns the investment of China’s sovereign wealth fund (SWF). The establishment of the Chinese SWF can be regarded as an optimal policy response to the changing economic conditions facing China. This fund as a special investment vehicle proves very useful for China to focus on the returns objective of managing the reserve assets, on top of the safety and liquidity objectives. This is especially important in a low yield international environment. To help achieve the yield objective, this Chapter develops further the behavioural portfolio model cum the Black-Litterman method to derive the optimal asset allocation for China’s sovereign wealth fund

Why does Implied Risk Aversion Smile?

A few recent papers have derived estimates of the representative agent's risk aversion by comparing the statistical density of asset returns and the state-price density. The implied risk aversion estimates obtained in these studies are puzzling, exhibiting (i) pronounced U-shaped patterns (a "smile") and (ii) negative values. This paper analyzes three potential explanations for these phenomena: (i) heterogeneity in investor preferences, (ii) difficulties in estimating agents' beliefs and (iii) heterogeneous beliefs among agents. Our results show that preferences alone cannot explain the patterns reported in the literature. Misestimation of investors' beliefs caused by nonstationarity of the return process cannot explain the smile either. The patterns of beliefs misestimation required to generate the empirical implied risk aversion estimates found in the literature suggest that heterogeneous beliefs are the most likely cause of the smile.asset pricing; state-price density; heterogeneous preferences; heterogeneous beliefs; implied risk aversion

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

Systematic asset allocation using flexible views for South African markets

We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci [142, 144, 145]. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward-looking distribution that is consistent with this view while remaining as close as possible to the prior distribution. The framework derives the forward-looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrate a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant outperformance on a gross and net basis

Portfolio optimization methods, their application and evaluation

The submitted master’s thesis focuses on practical application of quantitative portfolio optimization in various forms. The thesis is organized in two main parts, theoretical and practical. The theoretical part introduces the underpinnings of portfolio theory. It describes the optimization process, introduces a number of selected optimization methods, and provides an overview of portfolio management. As a whole, it serves as an underlying for the practical part. The practical part of the thesis is based on an experiment that put multiple quantitative portfolio optimization methods into a contest. Different optimizers were applied to portfolios composed of identical assets, which were subsequently held under different portfolio management styles over a pre-specified period of time. The performance of each portfolio was measured expost, adequately evaluated in accord with the criteria of the experiment, and confronted with the others. The questions that this master’s thesis tried to find answers to were (1) which portfolio optimizer, out of the selected ones, performs the best, and (2) whether it is beneficial to conduct rather an active, or a passive portfolio management.Esta dissertação de mestrado apresenta uma aplicação prática da otimização quantitativa de um portfólio realizada de diversas formas. A tese está organizada em duas partes principais, uma teórica e uma prática. A parte teórica introduz os fundamentos da teoria de portfólio. Descreve o processo de otimização, apresenta vários métodos de otimização selecionadas e fornece uma visão geral da gestão de portfólios. Como um todo, serve como base para a parte prática. A parte prática da tese coloca vários métodos de otimização de portfólio quantitativos em competição. Diferentes optimizadores foram aplicados a carteiras compostas por ativos idênticos que foram subsequentemente mantidos sob diferentes estilos de gestão ao longo de um período de tempo pré-especificado. O desempenho de cada carteira foi medido ex-post, adequadamente avaliado de acordo com os critérios de otimização e comparado com as demais carteiras. As perguntas para as quais esta tese de mestrado tentou encontrar respostas foram (1) qual é o optimizador de portfólio, dentre os selecionados, tem o melhor desempenho e (2) se é benéfico conduzir uma gestão de portfólio muito ativa ou passiva

Optimal Foreign Reserves, The Dollar Trap and Demand for Global Safe Assets: A DSGE analysis for China

The recent surge of foreign reserves in emerging markets has sparked fierce debate about what level of reserves is the optimal amount for a country. Conventional models have achieved important advances in understanding the behaviour of central banks’ reserve policy, but fail to find convincing solutions to the puzzle of why emerging economies, and China in particular, would continue to accumulate massive reserves. With reference to China’s massive hoarding of foreign reserves, this thesis develops a representative agent model with elements of dynamic stochastic general equilibrium (DSGE) modelling. The model constructed in this thesis explicitly considers the risky steady state as the equilibrium point when agents take into account future uncertainty but when the shock realizations are zero. In this risky steady state we derive the optimal reserves for emerging markets, with particular reference to the Chinese case. The precautionary savings motivation for holding reserves is then analysed within this framework. This thesis derives the optimality of Chinese reserve accumulation, and provides a plausible explanation for reserve build-up in China and its underlying driving forces. In order to better understand the foreign reserves accumulation, this thesis further attempts to analyse current external wealth allocation in a portfolio perspective within a DSGE framework. A two-country model is employed, and a Value at Risk (VaR) constraint is introduced to reproduce the risk averse behaviour of investors. After accounting for risk diversification, our findings imply that an investor would shift their portfolio holding to bond related assets. Finally, China has accumulated a huge amount of foreign reserves. The majority of these assets are denominated in the US dollar. Furthermore, in terms of asset type, the US T-bill is the dominant investment instrument in China’s international portfolio choice. This raises questions as to why the central bank of China chooses to make such an investment decision, and what the global repercussions might be. Therefore, China’s role in the growing demand for global safe assets deserves exploration. Given the world-wide shortage of global safe assets, to what extent China will continue the current international investment decision, and the driving forces behind such policy inertia, are major concerns. In order to gain a better understanding, this thesis applies a global solving method, as well as a standard local solving method

Essays on robust portfolio selection and pension finance

This thesis examines three different, but related problems in the broad area of portfolio management for long-term institutional investors, and focuses mainly on the case of pension funds. The first idea (Chapter 3) is the application of a novel numerical technique – robust optimization – to a real-world pension scheme (the Universities Superannuation Scheme, USS) for first time. The corresponding empirical results are supported by many robustness checks and several benchmarks such as the Bayes-Stein and Black-Litterman models that are also applied for first time in a pension ALM framework, the Sharpe and Tint model and the actual USS asset allocations. The second idea presented in Chapter 4 is the investigation of whether the selection of the portfolio construction strategy matters in the SRI industry, an issue of great importance for long term investors. This study applies a variety of optimal and naïve portfolio diversification techniques to the same SRI-screened universe, and gives some answers to the question of which portfolio strategies tend to create superior SRI portfolios. Finally, the third idea (Chapter 5) compares the performance of a real-world pension scheme (USS) before and after the recent major changes in the pension rules under different dynamic asset allocation strategies and the fixed-mix portfolio approach and quantifies the redistributive effects between various stakeholders. Although this study deals with a specific pension scheme, the methodology can be applied by other major pension schemes in countries such as the UK and USA that have changed their rules

Applications of biased randomised algorithms and simheuristics to asset and liability management

Asset and Liability Management (ALM) has captured the attention of academics and financial researchers over the last few decades. On the one hand, we need to try to maximise our wealth by taking advantage of the financial market and, on the other hand, we need to cover our payments (liabilities) over time. The purpose of ALM is to give investors a series of resources or techniques to select the appropriate assets on the financial market that respond to the aforementioned two key factors: cover our liabilities and maximise our wealth. This thesis presents a set of techniques that are capable of tackling realistic financial problems without the usual requirement of considerable computational resources. These techniques are based on heuristics and simulation. Specifically, a biased randomised metaheuristic model is developed that has a direct application in the way insurance companies usually operate. The algorithm makes it possible to efficiently select the smallest number of assets, mainly fixed income, on the balance sheet while guaranteeing the company's obligations. This development allows for the incorporating of the credit quality of the issuer of the assets used. Likewise, a portfolio optimisation model with liabilities is developed and solved with a genetic algorithm. The portfolio optimisation problem differs from the usual one in that it is multi-period, and incorporates liabilities over time. Additionally, the possibility of external financing is included when the entity does not have sufficient cash. These conditions give rise to a complex problem that is efficiently solved by an evolutionary algorithm. In both cases, the algorithms are improved with the incorporation of Monte Carlo simulation. This allows the solutions to be robust when considering realistic market situations. The results are very promising. This research shows that simheuristics is an ideal method for this type of problem.La gestión de activos y pasivos (asset and liability management, ALM) ha acaparado la atención de académicos e investigadores financieros en las últimas décadas. Por un lado, debemos tratar de maximizar nuestra riqueza aprovechando el mercado financiero, y por otro, debemos cubrir nuestros pagos (pasivos) a lo largo del tiempo. El objetivo del ALM es dotar al inversor de una serie de recursos o técnicas para seleccionar los activos del mercado financiero adecuados para obedecer a los dos factores clave mencionados: cumplir con nuestros pasivos y maximizar nuestra riqueza. Esta tesis presenta un conjunto de técnicas que son capaces de abordar problemas financieros realistas sin la necesidad habitual de considerables recursos computacionales. Estas técnicas se basan en la heurística y la simulación. En concreto, se desarrolla un modelo metaheurístico sesgado que tiene una aplicación directa en la operación habitual de inmunización de las compañías de seguros. El algoritmo permite seleccionar eficientemente el menor número de activos, principalmente de renta fija, en el balance y garantizar las obligaciones de la compañía. Este desarrollo permite incorporar la calidad crediticia del emisor de los activos utilizados. Asimismo, se desarrolla un modelo de optimización de la cartera con el pasivo y se resuelve con un algoritmo genético. El problema de optimización de la cartera difiere del habitual en que es multiperiodo e incorpora los pasivos a lo largo del tiempo. Además, se incluye la posibilidad de financiación externa cuando la entidad no tiene suficiente efectivo. Estas condiciones dan lugar a un problema complejo que se resuelve eficientemente mediante un algoritmo evolutivo. En ambos casos, los algoritmos se mejoran con la incorporación de la simulación de Montecarlo. Esto permite que las soluciones sean robustas cuando consideramos situaciones de mercado realistas. Los resultados son muy prometedores. Esta investigación demuestra que la simheurística es un método ideal para este tipo de problemas.La gestió d'actius i passius (asset and liability management, ALM) ha acaparat l'atenció d'acadèmics i investigadors financers les darreres dècades. D'una banda, hem de mirar de maximitzar la nostra riquesa aprofitant el mercat financer, i de l'altra, hem de cobrir els nostres pagaments (passius) al llarg del temps. L'objectiu de l'ALM és dotar l'inversor d'una sèrie de recursos o tècniques per seleccionar els actius del mercat financer adequats per obeir als dos factors clau esmentats: complir els passius i maximitzar la nostra riquesa. Aquesta tesi presenta un conjunt de tècniques que són capaces d'abordar problemes financers realistes sense la necessitat habitual de recursos computacionals considerables. Aquestes tècniques es basen en l'heurística i la simulació. En concret, es desenvolupa un model metaheurístic esbiaixat que té una aplicació directa a l'operació habitual d'immunització de les companyies d'assegurances. L'algorisme permet seleccionar eficientment el menor nombre d'actius, principalment de renda fixa, al balanç i garantir les obligacions de la companyia. Aquest desenvolupament permet incorporar la qualitat creditícia de l'emissor dels actius utilitzats. Així mateix, es desenvolupa un model d'optimització de la cartera amb el passiu i es resol amb un algorisme genètic. El problema d'optimització de la cartera difereix de l'habitual en el fet que és multiperíode i incorpora els passius al llarg del temps. A més, s'inclou la possibilitat de finançament extern quan l'entitat no té prou efectiu. Aquestes condicions donen lloc a un problema complex que es resol eficientment mitjançant un algorisme evolutiu. En tots dos casos, els algorismes es milloren amb la incorporació de la simulació de Montecarlo. Això permet que les solucions siguin robustes quan considerem situacions de mercat realistes. Els resultats són molt prometedors. Aquesta recerca demostra que la simheurística és un mètode ideal per a aquesta mena de problemes.Tecnologías de la información y de rede