An application of the Black–Litterman model with EGARCH-M-derived views for international portfolio management

This paper provides an application of the Black–Litterman methodology to portfolio management in a global setting. The novel feature of this paper relative to the extant literature on Black–Litterman methodology is that we use GARCH-derived views as an input into the Black–Litterman model. The returns on our portfolio surpass those of portfolios that rely on market equilibrium weights or Markowitz-optimal allocations. We thereby illustrate how the Black–Litterman model can be put to work in designing global investment strategies.      

保险集团财务管理模式探析

近几年来我国保险企业集团化快速发展,财务管理如何适应集团化的需要成了一个亟待研究和解决的问题。本文从分析集团企业不同财务管理模式的利弊入手,全面分析了影响保险集团财务管理模式的各种因素,明确了集团财务管理的基本要求,对保险集团不同发展阶段财务管理模式的选择提出了建议。     

Financial literacy and portfolio diversification

We use a survey of individual investors disclosed by the Portuguese Securities Commission (CMVM) in May 2005 to study the impact of investors’ levels of financial literacy on portfolio diversification. We consider distinct aspects of financial literacy, and control for socioeconomic and behavioral differences among individual groups of investors. Our results suggest that investors’ educational levels and their financial knowledge have a positive impact on investor diversification. The information sources used by retail investors to gather information on markets and financial products also have a significant impact on the number of different assets included in a portfolio.     

票据业务的资金化运作趋势分析及建议

近年来,商业银行票据业务呈现资金化运作的趋势,在利率走势、交易方式、盈利模式等方面与资金业务越来越接近。文章在分析票据业务资金化运作趋势的特点及其成因的基础上,从加强对宏观经济和市场的研究、创新票据产品和业务等角度就进一步完善商业银行票据业务经营提出建议。     

A decision analytic approach for technology portfolio prioritization: aviation safety applications

A decision analytic approach for evaluating new aviation safety products and technologies is developed and demonstrated to consolidate five existing program assessment metrics to develop a unified metric that simultaneously considers the relative importance and contribution of each. This allows for a meaningful and objective evaluation and comparison of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP) advanced aeronautical products and technologies. The resulting decision model is referred to as the Composite Program Assessment Score (CPAS). The CPAS includes the five existing metrics; technical development risk, implementation risk, fatal accident rate reduction, safety benefits and cost, and safety risk reduction, which are each defined and quantified by different sources. The CPAS involves the scaling and combination of these individual metrics. In this paper, two alternative combinatorial modeling approaches to calculate the CPAS are presented. The weighted sum model and an additive value theory model are compared and contrasted. The resulting CPAS metrics allow an overall comparison of all 48 of the NASA AvSP products and technologies. Currently CPAS is based on preliminary weight measures from subject matter experts to reflect the relative importance of each metric. Actual case studies of both linear and non‐linear value functions are demonstrated.     

Corporate portfolio management

Summary We solve the optimal portfolio problem in continuous time from the point of view of a corporation, acting on behalf of risk neutral shareholders. Our model fits for example the case of a commercial bank. Risk aversion is generated endogenously by financial frictions, and increases when the value of the firm’s assets decrease. We find a remarkably simple investment policy: invest a multiple of the firm’s equity into the risky asset, keep the rest as cash reserves, and distribute dividends when the value of the firm exceeds some threshold. As a consequence, the firm locally behaves as a Von Neumann-Morgenstern investor with constant relative risk aversion.We thank three anonymous referees for their comments.     

Population age structure and household portfolio choices in Italy

Based on the exceptional ageing of the Italian population, this paper aims to contribute to the current debate on population ageing and financial markets. To this end, we use the data taken by the Bank of Italy Survey of Household Income and Wealth over the period 1995–2006, and we analyse the average household portfolios in relation to age and net wealth (NW). Our analysis rests on a clustering of assets according to risk, which is different from the one used in Guiso and Jappelli (Guiso, L., and T. Jappelli. 2002. The portfolio of Italian households. In Household portfolios, eds. L. Guiso, M. Haliassos, and T. Jappelli. Cambridge: MIT Press). We find that age has affected financial choices of Italian households over the whole decade, but the portfolio age profile has significantly evolved over time with important differences across wealth quartiles. Overall, our analysis highlights a tendency towards a hump-shaped age profile of the allocation in risky assets for the most NW levels.     

International portfolio diversification: an ICAPM approach with currency risk

This article investigates international stock market integration in four major developed economies, namely the United States, the Economic and Monetary Union of the European Union, Japan and the United Kingdom, and two Asian emerging, countries namely China and India, over the period from June 1994 to June 2009. To model stock market integration we estimate a dynamic version of the international capital asset pricing model (CAPM) in the absence of purchasing power parity. Conditional variance is modelled via a multivariate GARCH specification. To investigate the evolution of integration overtime we estimate the CAPM in sub-periods. In addition, we connect our results to the timing of world financial crises. Our findings show that the stock markets tend to move in parallel after June of 2002, although from 2002 to 2006 there have not been crises events. These results support the increasing globalization and interdependence of both emerging and developed markets in the recent decade, reducing the benefits of portfolio diversification.     

A jackknife-type estimator for portfolio revision

This article proposes a novel approach to portfolio revision. The current literature on portfolio optimization uses a somewhat naïve approach, where portfolio weights are always completely revised after a predefined fixed period. However, one shortcoming of this procedure is that it ignores parameter uncertainty in the estimated portfolio weights, as well as the biasedness of the in-sample portfolio mean and variance as estimates of the expected portfolio return and out-of-sample variance. To rectify this problem, we propose a jackknife procedure to determine the optimal revision intensity, i.e. the percent of wealth that should be shifted to the new, in-sample optimal portfolio. We find that our approach leads to highly stable portfolio allocations over time, and can significantly reduce the turnover of several well established portfolio strategies. Moreover, the observed turnover reductions lead to statistically and economically significant performance gains in the presence of transaction costs.     

Reduction of estimation risk in optimal portfolio choice using redundant constraints

It is well known that when the moments of the distribution governing returns are estimated from sample data, the out-of-sample performance of the optimal solution of a mean–variance (MV) portfolio problem deteriorates as a consequence of the so-called “estimation risk”. In this document we provide a theoretical analysis of the effects caused by redundant constraints on the out-of-sample performance of optimal MV portfolios. In particular, we show that the out-of-sample performance of the plug-in estimator of the optimal MV portfolio can be improved by adding any set of redundant linear constraints. We also illustrate our findings when risky assets are equally correlated and identically distributed. In this specific case, we report an emerging trade-off between diversification and estimation risk and that the allocation of estimation risk across portfolios forming the optimal solution changes dramatically in terms of number of assets and correlations.     

A composite indicator model to assess natural disaster risks in industry on a spatial level

In the event of natural disasters, industrial production sites can be affected by both direct physical damage and indirect damage. The indirect damage, which often exceeds the direct ones in value, mainly arises from business interruptions resulting from the impairment of information and material flows as well as from domino effects in interlaced supply chains. The importance of industry for society and the domino effects often result in severe economic, social, and environmental consequences of industrial disasters making industrial risk management an important task for risk managers at the administrative level (e.g. civil protection authorities). Since the possible industrial disaster damage depends not only on hazard and exposure but also on the vulnerability of a system, an effective and efficient industrial risk management requires information about the system’s regionalized vulnerability. This paper presents a new methodology for structural industrial vulnerability assessment based on production factors that enables to assess the regional industrial disaster vulnerability. In order to capture industry-specific vulnerability factors and to account for the processes underlying regional industrial vulnerability, a two-stage approach is developed. This approach combines a composite indicator model to assess sector-specific vulnerability indices (V_s) with a new regionalization method. The composite indicator model is based on methodologies from the field of multicriteria decision analysis (MultiAttribute Value Theory) and the Decision-Making Trial and Evaluation Laboratory Method is applied to correct the (V_s) for interdependencies among the indicators. Finally, the developed approach is applied to an exemplar case study and the industrial vulnerability of 44 administrative districts in the German federal state of Baden-Wuerttemberg is assessed.     

A proposed enterprise risk management model for health organizations

Health care organizations are environments with high management complexity and subject to a constant exposure to risks. Enterprise risk management (ERM) has been studied and applied in different economic environments with the aim of improving organizational performance. However, the health sector still suffers from a lack of attention in this context, in particular with regard to the need for a high degree of financial transparency and for the establishment of process-orientated management, and this provides the motivation for the study described in this paper. An ERM model for health organizations is proposed, based on a systematic literature review and on seven case studies in Brazilian hospitals. An approach to economic risk assessment using indicators such as the cash flow at risk and the variability of costs and receipts from the proposed model is suggested. The health organizations involved in the case studies all interpret ERM as a source of information contributing to corporate governance, and the indicators listed provide constructive data for improvement-driven decision-making. Given the interest expressed by the organizations involved, further application and validation of the proposed model in subsequent studies is suggested.     

A bootstrap-based comparison of portfolio insurance strategies

This study presents a systematic comparison of portfolio insurance strategies. We implement a bootstrap-based hypothesis test to assess statistical significance of the differences in a variety of downside-oriented risk and performance measures for pairs of portfolio insurance strategies. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic protection; initial wealth versus cumulated wealth protection; model-based versus model-free protection; and strong floor compliance versus probabilistic floor compliance. Our results indicate that the classical portfolio insurance strategies synthetic put and constant proportion portfolio insurance (CPPI) provide superior downside protection compared to a simple stop-loss trading rule and also exhibit a higher risk-adjusted performance in many cases (dependent on the applied performance measure). Analyzing recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provides significant improvements over the more traditional portfolio insurance strategies.     

Binary switch portfolio

We propose herein a new portfolio selection method that switches between two distinct asset allocation strategies. An important component is a carefully designed adaptive switching rule, which is based on a machine learning algorithm. It is shown that using this adaptive switching strategy, the combined wealth of the new approach is a weighted average of that of the successive constant rebalanced portfolio and that of the 1/N portfolio. In particular, it is asymptotically superior to the 1/N portfolio under mild conditions in the long run. Applications to real data show that both the returns and the Sharpe ratios of the proposed binary switch portfolio are the best among several popular competing methods over varying time horizons and stock pools.     

Optimal portfolio positioning within generalized Johnson distributions

Many empirical studies have shown that financial asset returns do not always exhibit Gaussian distributions, for example hedge fund returns. The introduction of the family of Johnson distributions allows a better fit to empirical financial data. Additionally, this class can be extended to a quite general family of distributions by considering all possible regular transformations of the standard Gaussian distribution. In this framework, we consider the portfolio optimal positioning problem, which has been first addressed by Brennan and Solanki [J. Financial Quant. Anal., 1981, 16, 279–300], Leland [J. Finance, 1980, 35, 581–594] and further developed by Carr and Madan [Quant. Finance, 2001, 1, 9–37] and Prigent [Generalized option based portfolio insurance. Working Paper, THEMA, University of Cergy-Pontoise, 2006]. As a by-product, we introduce the notion of Johnson stochastic processes. We determine and analyse the optimal portfolio for log return having Johnson distributions. The solution is characterized for arbitrary utility functions and illustrated in particular for a CRRA utility. Our findings show how the profiles of financial structured products must be selected when taking account of non Gaussian log-returns.     

Factor investing and currency portfolio management

Currency-specific pricing factors are pervasive in international asset pricing. However, portfolio and risk management based on forex factors, instead of individual currencies, are rarely discussed. This paper tries to fill this gap by modelling dynamic correlations and non-normality among forex factors. By considering the four most popular forex factors: the dollar risk factor, the carry trade factor, the currency momentum factor, and the currency value factor, we find that a dynamic conditional correlation copula (DCC-copula) model with skewed-t kernel fits the joint distribution well. We show that, for risk-averse investors who focus on factor investing or employ the forex factors to resize the specific risk exposure, ignoring the tail dependence structure of forex factors brings significant costs.     

Collective mental accounting: an integrated behavioural portfolio selection model for multiple mental accounts

We propose a behavioural portfolio selection model called collective mental accounting (CMA), which integrates all mental sub-portfolios (mental accounts) in one mathematical model. Moreover, this study contributes to the literature of behavioural portfolio selection in three further ways: first, the CMA model can determine the proportions of wealth allocated to each mental sub-portfolio with and without input from the investor. Second, unlike other mental accounting models (MA), in CMA it is possible to define constraints on total asset holdings such as short-selling, and cardinality constraints. Third, in order to make CMA more tractable and mathematically elegant, we obtain a semi-definite programming representation of the model. We also present a numerical example to investigate the effects of short-selling constraints as well as to compare the portfolio recommendations, utility functions, feasibility, and optimality of the CMA and MA models. The results reveal that although both models’ solutions are mean-variance efficient, CMA outperforms MA in terms of behavioural efficient frontier and utility functions.