To split or not to split: Capital allocation with convex risk measures

Convex risk measures were introduced by Deprez and Gerber (1985). Here the problem of allocating risk capital to subportfolios is addressed, when aggregate capital is calculated by a convex risk measure. The Aumann-Shapley value is proposed as an appropriate allocation mechanism. Distortion-exponential measures are discussed extensively and explicit capital allocation formulas are obtained for the case that the risk measure belongs to this family. Finally the implications of capital allocation with a convex risk measure for the stability of portfolios are discussed

Upgrading investment regulations in second pillar pension systems : a proposal for Colombia

The passivity of the demand for pension products is one of the striking features of mandatory pension systems. Consequently, the provision of multiple investment alternatives to households (multifund schemes) does not ensure that contributions are invested efficiently. In addition, despite the theoretical findings that short term return maximization is not conductive to long-term return maximization, the regulatory framework of pension fund management companies puts excessive emphasis on short-term maximization. Therefore, it is not obvious that typical regulatory framework of pension funds is conductive to optimal pensions. By establishing a set of default options on investment portfolios, this paper proposes a mechanism to align the incentives of the pension fund management companies with the long-term objectives of the contributors. The paper provides a methodology, which is subsequently applied to Colombia.Debt Markets,Emerging Markets,Financial Literacy,Mutual Funds,Investment and Investment Climate

A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To address cyclicality and underperformance, we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We test our system across multiple combinations of smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return oriented portfolios (up to 50$\%$ in excess of market annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60$\%$ in excess of market annually) show model performance improvement with respect to portfolios built using full feature HMMs

Portfolio regulation of life insurance companies and pension funds

This paper examines the rationale, nature and financial consequences of two alternative approaches to portfolio regulations for the long-term institutional investor sectors life insurance and pension funds. These approaches are, respectively, prudent person rules and quantitative portfolio restrictions. The argument draws on the financial-economics of investment, the differing characteristics of institutions’ liabilities, and the overall case for regulation of financial institutions. Among the conclusions are: · regulation of life insurance and pensions need not be identical; · prudent person rules are superior to quantitative restrictions for pension funds except in certain specific circumstances (which may arise notably in emerging market economies), and; · although in general restrictions may be less damaging for life insurance than for pension funds, prudent person rules may nevertheless be desirable in certain cases also for this sector, particularly in competitive life sectors in advanced countries, and for pension contracts offered by life insurance companies. These results have implications inter alia for an appropriate strategy of liberalisation. 1 The author is Professor of Economics and Finance, Brunel University, Uxbridge, Middlesex UB3 4PH, United Kingdom (e-mail ‘[email protected]’, website: ‘www.geocities.com/e_philip_davis’). He is also a Visiting Fellow at the National Institute of Economic and Social Research, an Associate Member of the Financial Markets Group at LSE, Associate Fellow of the Royal Institute of International Affairs and Research Fellow of the Pensions Institute at Birkbeck College, London. Work on this topic was commissioned by the OECD. Earlier versions of this paper were presented at the XI ASSAL Conference on Insurance Regulation and Supervision in Latin America, Oaxaca, Mexico, 4-8 September 2000, and at the OECD Insurance Committee on 30 November 2000. The author thanks participants at the conference and A Laboul for helpful comments. Views expressed are those of the author and not necessarily those of the institutions to which he is affiliated, nor those of the OECD. This paper draws on Davis and Steil (2000)

Joined-Up Pensions Policy in the UK: An Asset-Libility Model for Simultaneously Determining the Asset Allocation and Contribution Rate

The trustees of funded defined benefit pension schemes must make two vital and inter-related decisions - setting the asset allocation and the contribution rate. While these decisions are usually taken separately, it is argued that they are intimately related and should be taken jointly. The objective of funded pension schemes is taken to be the minimization of both the mean and the variance of the contribution rate, where the asset allocation decision is designed to achieve this objective. This is done by splitting the problem into two main steps. First, the Markowitz mean-variance model is generalised to include three types of pension scheme liabilities (actives, deferreds and pensioners), and this model is used to generate the efficient set of asset allocations. Second, for each point on the risk-return efficient set of the asset-liability portfolio model, the mathematical model of Haberman (1992) is used to compute the corresponding mean and variance of the contribution rate and funding ratio. Since the Haberman model assumes that the discount rate for computing the present value of liabilities equals the investment return, it is generalised to avoid this restriction. This generalisation removes the trade-off between contribution rate risk and funding ratio risk for a fixed spread period. Pension schemes need to choose a spread period, and it is shown how this can be set to minimise the variance of the contribution rate. Finally, using the result that the funding ratio follows an inverted gamma distribution, shortfall risk and expected tail loss are computed for funding below the minimum funding requirement, and funding above the taxation limit. This model is then applied to one of the largest UK pension schemes - the Universities Superannuation Scheme

Portfolio advice of a multifactor world

How does traditional portfolio theory adapt to the new facts? The old "two-fund" theorem becomes a "many-fund" theorem; some investors can improve returns by investing in portfolio strategies that let them take on nonmarket sources of risk; and other investors can shed nonmarket risks in the same way. Investors can, if willing to take on risks, improve returns by some modest market timing. However, the average investor must always hold the market, so only investors who are different from average can benefit from holding new and unusual portfoliosMutual funds ; Capital assets pricing model

Asset liability management using stochastic programming

This chapter sets out to explain an important financial planning model called asset liability management (ALM); in particular, it discusses why in practice, optimum planning models are used. The ability to build an integrated approach that combines liability models with that of asset allocation decisions has proved to be desirable and more efficient in that it can lead to better ALM decisions. The role of uncertainty and quantification of risk in these planning models is considered