Portfolio management with cryptocurrencies: the role of estimation risk

This paper contributes to the literature on cryptocurrencies, portfolio management and estimation risk by comparing the performance of naïve diversification, Markowitz diversification and the advanced Black–Litterman model with VBCs that controls for estimation errors in a portfolio of cryptocurrencies. We show that the advanced Black–Litterman model with VBCs yields superior out-of-sample risk-adjusted returns as well as lower risks. Our results are robust to the inclusion of transaction costs and short-selling, indicating that sophisticated portfolio techniques that control for estimation errors are preferred when managing cryptocurrency portfolios

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

Socially responsible investment portfolios: does the optimization process matter?

This study investigates the impact of the choice of optimization technique when constructing Socially Responsible Investment (SRI) portfolios. Corporate Social Performance (CSP) scores are price sensitive information that is subject to considerable estimation risk. Therefore, uncertainty in the input parameters is greater for SRI portfolios than conventional portfolios, and this affects the selection of the appropriate optimization method. We form SRI portfolios based on six different approaches and compare their performance along the dimensions of risk, risk-return trade-off, diversification and stability. Our results for SRI portfolios contradict those of the conventional portfolio optimization literature. We find that the more “formal” optimization approaches (Black-Litterman, Markowitz and robust estimation) lead to SRI portfolios that are both less risky and have superior risk-return trade-offs than do more simplistic approaches; although they also have more unstable asset allocations and lower diversification. Our conclusions are robust to a series of tests, including the use of different estimation windows and stricter screening criteria

The role of transaction costs and risk aversion when selecting between one and two regimes for portfolio models

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Estimation of the inputs is the main problem when applying portfolio analysis, and Markov regime-switching models have been shown to improve these estimates. We investigate whether the use of two-regime models remains superior across a range of values of risk aversion and transaction costs, in the presence of skewness and kurtosis and no short sales. Our results for US data suggest that, due to differences in their risk preferences and transactions costs, most retail investors may prefer to use one-regime models, while investment banks may prefer to use two-regime models

Taxation, pension schemes and stakeholder wealth

Although tax relief on pensions is a controversial area of government expenditure, this is the first study of the tax effects for a real world defined benefit pension scheme. First, we estimate the tax and national insurance contribution (NIC) effects of the scheme’s change from final salary to career average revalued earnings (CARE) in 2011 on the gross and net wealth of the sponsor, government, and 16 age cohorts of members, deferred pensioners and pensioners. Second, we measure the size of the twelve income tax and NIC payments and reliefs for new members and the sponsor, before and after the rule changes. We find the total subsidy split is roughly 40% income tax subsidy; and 60% NIC subsidy. If lower tax rates in retirement and the risk premium effect of the exempt-exempt-taxed (EET) system are not viewed as a tax subsidy, the tax subsidy to members largely disappears. Any remaining subsidy drops, as a proportion of pension benefits, for high earners; as does that for NICs

Should investors include bitcoin in their portfolios? A portfolio theory approach

Many papers in recent years have examined the benefits of adding alternative assets to traditional portfolios containing stocks and bonds. Bitcoin has emerged as a new alternative investment for investors which has attracted much attention from the media and investors alike. However relatively little is known about the investment benefits of Bitcoin and therefore this paper examines the benefit of including Bitcoin in a traditional benchmark portfolio of stocks and bonds. Specially, we employ data up to June 2018 and analyse the potential out-of-sample portfolio benefits resulting from including Bitcoin in a stock-bond portfolio for a range of eight popular asset allocation strategies. The out-of-sample analysis shows that, across all different asset allocation strategies and risk aversions, the benefits of Bitcoin are quite considerable with substantially higher risk-adjusted returns. Our results are robust to rolling estimation windows, the incorporation of transaction costs, the inclusion of a commodity portfolio, alternative indices, short-selling as well as two additional optimization techniques including higher moments with (and without) variance-based constraints (VBCs). Therefore, our results suggest that investors should include Bitcoin in their portfolio as it generates substantial higher risk-adjusted returns

Harmful diversification: evidence from alternative investments

Alternative assets have become as important as equities and fixed income in the portfolios of major investors, and so their diversification properties are also important. However, adding five alternative assets (real estate, commodities, hedge funds, emerging markets and private equity) to equity and bond portfolios is shown to be harmful for US investors. We use 19 portfolio models, in conjunction with dummy variable regression, to demonstrate this harm over the 1997-2015 period. This finding is robust to different estimation periods, risk aversion levels, and the use of two regimes. Harmful diversification into alternatives is not primarily due to transactions costs or non-normality, but to estimation risk. This is larger for alternative assets, particularly during the credit crisis which accounts for the harmful diversification of real estate, private equity and emerging markets. Diversification into commodities, and to a lesser extent hedge funds, remains harmful even when the credit crisis is excluded

Optimal vs naïve diversification in cryptocurrencies

This paper contributes to the literature on cryptocurrencies by examining the performance of naïve (1/N) and optimal (Markowitz) diversification in a portfolio of four popular cryptocurrencies. We employ weekly data with weekly rebalancing and show there is very little to select between naïve diversification and optimal diversification. Our results hold for different levels of risk-aversion and an alternative estimation window

Asset liability modelling and pension schemes: the application of robust optimization to USS

This paper uses a novel numerical optimization technique - robust optimization - that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes-Stein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks