When I work with a group that wants to complete a task but spends no time developing a cooperative relationship, I generally find their work product to be merely satisfactory. The product meets minimum standards, but typically the dynamic within teams that spend no time on relationship development is not condusive to innovation or creativity.
The job may get completed, but people do not enjoy the experience or each other. Teams that spend all their time on relationship development seem to enjoy the process of working together, but also find they have trouble staying on task and finishing the assignment on time. I often find their work to be incomplete.
Groups that spend an equal amount of time on tasks and relationships seem to have better outcomes. When a group deliberately moves toward balance, some people who are used to working exclusively on the task or on the relationship may feel awkward. Others in the group can bring them along, however, by acknowledging the importance of both kinds of activities. By balancing time and energy between the two activities, people feel better about what they’ve accomplished together—the group has more pride in what it has produced. The task/relationship model presents three possible group configurations.
Group 1 (task 80%, relationship 20%) meets the requirements of the task, although some partners may feel left out. The “doers” in the group take over and do it. Not everyone in the group feels ownership of the outcome, and not everyone feels good about the process used in accomplishing the task. A typical comment from a group member: “At least it’s done.”
Group 2 (task 50%, relationship 50%) spends its energy developing practical partnerships within the group. They develop relationships and work on the task. People complete the task on time, with high quality, and in a creative manner. What contributes to the high functioning of this model is the group’s intention to get the job done as a team process. Members respect one another and use everyone’s contributions to create the best outcome. A typical comment: “I enjoyed doing the task, I’m proud to have been part of it, and everyone seemed to get along well.”
Group 3 (task 20%, relationship 80%) puts its energy into developing relationships. The “relators” take over and welcome the opportunity to socialize and share information. Although they may not accomplish the entire task in the required time, people feel good about what they do accomplish. At least they develop some shortterm relationships. A typical comment: “Even if we didn’t finish, I really feel good about what we did.”
It would, of course, be ideal if people embraced change with zest and unquestionable commitment. But rarely have I worked with partners where that has occurred. Usually I encounter resistance even after we’ve set up a plan and created an environment in which change can take root.Understanding why people resist change is a big step toward helping them overcome their fear and anxiety. This article outlines some common fears people have about change and offers some remedies to overcome them.
Now that you’ve seen some strategies for understanding and improving your Partnering Intelligence as it relates to change, it’s time to think about how you manage change in your life and your partnerships. Just as change is a powerful dynamic in the partnership, the ability to balance the change process between the two critical components of
partnership—task and relationship—is essential. No partnership can flourish without this balance. In deference to “getting the job done,” it is the relationship component that is most often ignored. But being accountable for our own productivity is only half of the equation. After all, if achieving our goals only required that we be accountable for our own actions, we wouldn’t need a partner. The other half of the equation is building a trusting relationship with our partner. And this requires us to change not what we might do, but how we do it.
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.