THE EFFECT OF STRESSED ECONOMIC CONDITIONS ON SYSTEMIC RISK WITHIN THE SOUTH AFRICAN BANKING SECTOR

The credit crisis resulted in increases in credit, market and operational risk, but it may also have precipitated a surge in systemic risk. Measuring systemic risk as the price of insurance against distressed losses in the South African banking sector, this article attempts to determine whether the financial crisis has in fact resulted in an increase in systemic risk. Using probabilities of default and asset return correlations as systemic risk indicators, it is found that the financial crisis has indeed increased systemic risk in South Africa. The impact was, however, less severe than that experienced in other large international banks.     

考虑基准资产的动态投资组合选取

职业基金经理的目标经常是希望自己的投资组合以稳定的表现能够超越所某一基准资产或组合。因此本文给出一个考虑基准资产的动态均值——方差投资组合选取模型。假设状态之间的转移遵循马氏过程,给定状态转移矩阵,可以得到对风险资产最优投入的解析表达式。此表达式表明对风险资产的投入由三项构成,前两项是不考虑基准资产时对风险资产的投入,最后一项与基准资产有关;在基准资产上的权重由基准资产收益的大小来决定,与积极投资组合管理者的风险厌恶程度无关;随着风险厌恶程度的增加,管理者会减少在风险资产上的投入。数值分析显示考虑基准资产的投资组合是一个积极的投资组合。     

Foreign Direct Investment,Sectoral Effects and Economic Growth in Africa

Abstract

Many small, frontier equity markets in regions such as Africa and Eastern Europe have opened in recent years. As in other larger emerging markets, important issues for investors are the extent of financial integration with exchanges in other countries and, if some reasonable degree of integration is found, whether such markets still provide diversification opportunities. Here, we will examine a frequently used metric of integration by testing for the existence of common trends, or cointegration, in these frontier markets. While common stochastic trends are found, results show that coefficients on cointegrating vectors are at times negative, and reaction to deviations from the long-run trend are often slow, thus indicating that frontier markets are a good source of diversification opportunities despite a degree of integration.     

Power mapping with dynamical adjustment for improved portfolio optimization

For financial risk management it is of vital interest to have good estimates for the correlations between the stocks. It has been found that the correlations obtained from historical data are covered by a considerable amount of noise, which leads to a substantial error in the estimation of the portfolio risk. A method to suppress this noise is power mapping. It raises the absolute value of each matrix element to a power q while preserving the sign. In this paper we use the Markowitz portfolio optimization as a criterion for the optimal value of q and find a K/T dependence, where K is the portfolio size and T the length of the time series. Both in numerical simulations and for real market data we find that power mapping leads to portfolios with considerably reduced risk. It compares well with another noise reduction method based on spectral filtering. A combination of both methods yields the best results.     

Asymptotics of the probability of minimizing ‘down-side’ risk under partial information

We consider minimizing the probability of falling below a target growth rate of the wealth process up to a time horizon T in an incomplete market model under partial information and then study the asymptotic behavior of the minimizing probability as T → ∞. This problem is closely related to an ergodic risk-sensitive stochastic control problem under partial information in the risk-averse case. Indeed, in our main theorem we relate the former problem to the latter as its dual. As a result we obtain an explicit expression for the limit value of the former problem in the case of linear Gaussian models.     

Comparative issues between linear and non-linear risk measures for non-convex portfolio optimization: evidence from the S&P 500

Abstract

This article focuses on inferring critical comparative conclusions as far as the application of both linear and non-linear risk measures in non-convex portfolio optimization problems. We seek to co-assess a set of sophisticated real-world non-convex investment policy limitations, such as cardinality constraints, buy-in thresholds, transaction costs, particular normative rules, etc. within the frame of four popular portfolio selection cases: (a) the mean-variance model, (b) the mean-semi variance model, (c) the mean-MAD (mean-absolute deviation) model and (d) the mean-semi MAD model. In such circumstances, the portfolio selection process reflects to a mixed-integer bi-objective (or in general multiobjective) mathematical programme. We precisely develop all corresponding modelling procedures and then solve the underlying problem by use of a novel generalized algorithm, which was exclusively introduced to cope with the above-mentioned singularities. The validity of the attempt is verified through empirical testing on the S&P 500 universe of securities. The technical conclusions obtained not only confirm certain findings of the particular limited existing theory but also shed light on computational issues and running times. Moreover, the results derived are characterized as encouraging enough, since a sufficient number of efficient or Pareto optimal portfolios produced by the models appear to possess superior out-of-sample returns with respect to the benchmark.     

Portfolio selection with commodities under conditional copulas and skew preferences

This article investigates the portfolio selection problem of an investor with three-moment preferences taking positions in commodity futures. To model the asset returns, we propose a conditional asymmetric t copula with skewed and fat-tailed marginal distributions, such that we can capture the impact on optimal portfolios of time-varying moments, state-dependent correlations, and tail and asymmetric dependence. In the empirical application with oil, gold and equity data from 1990 to 2010, the conditional t copulas portfolios achieve better performance than those based on more conventional strategies. The specification of higher moments in the marginal distributions and the type of tail dependence in the copula has significant implications for the out-of-sample portfolio performance.     

Relative Robust Portfolio Optimization with benchmark regret

We extend Relative Robust Portfolio Optimization models to allow portfolios to optimize their performance when considered relative to a set of benchmarks. We do this in a minimum volatility setting, where we model regret directly as the maximum difference between our volatility and that of a given benchmark. Portfolio managers are also given the option of computing regret as a proportion of the benchmark’s performance, which is more in line with market practice than other approaches suggested in the literature. Furthermore, we propose using regret as an extra constraint rather than as a brand new objective function, so practitioners can maintain their current framework. We also look into how such a triple optimization problem can be solved or at least approximated for a general class of objective functions and uncertainty and benchmark sets. Finally, we illustrate the benefits of this approach by examining its performance against other common methods in the literature in several equity markets.     

信用风险主流模型与RCM模型的比较及借鉴

RCM模型是信用风险管理理论的最新发展,它有效地融合了风险集中度、信用风险损失和资本充足率等变量,根据贷款组合整体价值的相应比例来度量风险集中度,并确定相应风险限额,为信用风险度量提供了一种新的理念和分析框架。本文介绍了RCM模型的基本理论,比较分析了现有主流信用风险模型与RCM模型的共性及差异,结果发现虽然RCM模型仍有一些缺陷,但其简便、实用的特点决定了其对我国银行业和银行监管机构具有较强的借鉴意义。