Slow adaptors want to be sure the change is permanent and necessary before they are willing to adapt to it. They do not believe in change for change’s sake. Slow adaptors often need specific details about the change event to determine how the change will directly impact them and their day-to-day routines. Slow adaptors require tactical information specific to their role or work processes to help convince them that the change is for the better and will be permanent. They tend to need consistent motivation.
Resistors of change refuse to acknowledge the need for change and often feel that change is directed at them personally, often slipping into a state of denial over change events. It is not uncommon for resistors to hold strong sway over a partnership, convincing others that change is harmful to the organization and the “mistake” will be rectified once “those people” understand the errors of their ways. Resistors hold nostalgia and the status quo in high esteem and are effective in trying to convince others to return to the “good old days.” Resistors not only deny the need for change, but may also deny later that any change has actually occurred. No amount of information will help them break through their veil of denial. The only sure strategy to help with resistors is to get them to understand that there is no turning back the clock; that is, the partnership is not going back to “how things were” and that they must either adapt or find other options outside the organization.
Because of the different approaches towards risk measurement the composition of the efficient portfolios should differ significantly. Asset classes with more negatively skewed or leptokurtic return distributions are expected to receive lower weightings in the shortfall risk and the Corning–Fisher framework. We will focus on the composition and risk/return profile of
three portfolios with very special characteristics. The minimum risk portfolio (MRP) minimizes the risk subject to the imposed short sales constraint. Furthermore, the tangency portfolio (TP) and an equal risk portfolio (ERP) are examined. The TP maximizes the ratio of excess return over the risk-free rate to portfolio risk, known as the Sharpe ratio. On a depiction of the efficient frontier the TP is determined by a tangency to the efficient frontier through the risk-free rate rf. As suggested by its name the ERP is designed to maximize return for a given level of risk, for example the volatility of a pure government portfolio. From the perspective of a government bond investor, it highlights the opportunity costs of neglecting the return opportunities associated with credits.
Altogether, the adjusted VaR has the desired properties to cope with nonnormal return distributions. Moreover, in conjunction with the correlation matrix of asset returns it allows to calculate the optimal portfolios with an algorithm similar to the quadratic programming algorithm used in the mean–variance framework. For the description of further transformations interested readers may refer to Mina and Ulmer (1999) and Li (2000).
Despite the obvious advantages of the VaR concept, investors should be careful when applying this approach for portfolio optimization. Although VaR fulfils our requirements with respect to reflecting downside risk, Artzner et al. (1997) have shown that it does not comply with one of the basic requirements of a satisfactory risk measure. In mathematical terms, it is not necessarily coherent, meaning that the condition of sub-additiveness is hurt. In other words, under certain circumstances the optimization problem has multiple local solutions. Convergence towards the one and only global optimum cannot be achieved with the usual Newton-style descent algorithms.
The statistical properties of bond returns do not correspond with one of the basic assumptions of modern portfolio theory. As indicated by the descriptive statistics the empirical distributions of bond returns significantly deviate from a normal distribution. Keeping in mind this deficiency we nevertheless provide the results of the classical mean–variance framework as a benchmark in the empirical part of this chapter. As an alternative, two techniques are introduced that explicitly take account of skewness and kurtosis during the process of portfolio construction.
Downside risk measures apply to the intuitive understanding of risk of most investors. They associate risk with below-target returns. Typical targets are “preservation of nominal or real capital invested”. By utilizing below-target returns, lower partial moments are measuring the amount of negative skewness of an empirical distribution.
Our investigation demonstrates that all of the examined bond indices exhibit negative skewness and excess kurtosis, both of which increase the probability of extreme negative returns. For all asset classes except for the most liquid sector, government bonds, significant autocorrelation is identified in monthly index returns. Therefore, sample estimators of standard deviation, skewness and kurtosis are biased. Hence, the results of common tests for normality of returns should be interpreted carefully. For reason of completeness the results of one test of normality are provided. The Jarque–Bera (1987) test for the normality of observations can only be rejected for the mortgage-backed securities and high-yield sector. However, it should be noted that the distribution of returns of single corporate bond issuers is highly skewed. But the broad diversification on the index level mitigates this effect.
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The macroeconomic analysis of corporate bond markets typically is based on aggregate measures of growth, employment, interest rates and monetary policy. The impact of changes of these variables on corporate revenues and cash flows and thus on credit risk depends on financial and operating leverage and on the ratio of earnings or cash flows to net interest payments, that is, some measure for interest coverage.
The subject of valuation can be analyzed from various perspectives. Investors usually tend to compare current spreads with historical spreads. However, it is highly recommended to consider the stage of the credit cycle between then and now, when doing this. The results also should be adjusted for different compositions of the credit universe and a potential rating drift over time. Fundamental models for credit spreads implicitly take changes of the economic environment and consequently of the average ratings of the issuers into account. In other words, the outcome of this kind of models is a fair spread for corporate bonds with respect to the economic environment.
Strategic asset allocation is the first step in the investment process for credit portfolios. At this stage, all analyses are from a top-down perspective. In other words, the medium to long-term outlook for credit quality and the future direction of credit spreads is assessed on an aggregate basis, that is, for the credit market as a whole. The research process therefore focuses on three main subjects: the macroeconomic environment for credit, valuation and technical market drivers. The weighting of these aspects, however, differs according to the market environment, the investment universe and the risk/return profile of the portfolio for which the top-down analysis is performed. Due to the increased business risk of noninvestment grade companies, changes of the macroeconomic environment are particularly important for high-yield investors, whereas credit spreads in the investment grade corporate bond market are frequently influenced by technical factors like the issuance of cash bonds or CDOs. Valuation aspects with respect to past performance in similar stages of the business cycle and in relation to other asset classes tend to have a high influence for investment grade as well as for high-yield bonds.
The management option to alter the capital structure establishes the link between structural models and fundamental credit analysis. It may be remembered that in the Merton framework the strike price of both the call and the put option on the firm’s assets changes when the capital structure of a company changes. This is undoubtedly one of the strategic risks a credit analyst has to evaluate. But there are further points that potentially may have a strong impact on credit spreads, but cannot be captured by quantitative models. Pension liabilities, off-balance-sheet items and litigation risk, which have been major drivers of credit spreads recently, are typical examples. Therefore, we feel that it is most promising not to concentrate on a purely quantitative or a purely fundamental approach.
Combinations of both may prove particularly insightful. Undoubtedly, none of the analyzed approaches works in every environment. But experienced and sophisticated managers will be able to select the best method for a given investment situation.
With respect to the analysis of a specific issuer, it is important to consider how far the market value of the firm’s assets is away from its default threshold. When the value of the assets approaches the default barrier, fundamental issues such as the likelihood of capital structure changes, possible corporate actions and potential changes in the business model determine credit valuation. Generally, the value of credit analysis rises with an increasing level of leverage because the probability of default is primarily related to management options, such as the above-mentioned. Credit analysts typically do not only rely on balance sheet analysis, but rather introduce metrics like debt-to-EBITDA ratios in order to measure a company’s ability to service its debt from operations. Furthermore, credit analysts’ views on the probability and timing of potential capital structure changes are essential in determining valuation. If the magnitude and likelihood of a change in the capital structure is high, then it will dominate any valuation of a credit. Yet, quantitative models help to estimate the impact of capital structure changes on the valuation of a particular issuer.
In determining if quantitative approaches may add value, and which model is best suited, both investment horizon and performance targets as well as credit-specific characteristics should be considered. We would distinguish those investors who are concerned with mark-to-market fluctuations from those who are focused on absolute return to maturity. The latter may find the long-term signals provided by credit analysts, rating agencies and quantitative models to be more relevant than market expectations that are reflected in credit spreads and implied default probabilities. In the portfolio context, quantitative tools are particularly helpful to determine the risks associated with correlated defaults. Absolute return investors enjoy more freedom to implement relative value trades than those investors that may not deviate substantially from a given benchmark index.