**8. References**


considered.

**Author details** 

Pako Thupayagale

**8. References** 

*Bank of Botswana, Botswana* 

Income. 2009; 19(1) : 73-81.

Financ Econ. 2008; 6: 459-97.

supplementary analysis. 2011. EMTA.

especially over long horizons, for Hong Kong, Mexico and South Africa. Put differently, the computational complexity of long memory modelling is not commensurate with the benefits (in terms of forecast power). Third, the results of the VaR estimation may provide guidance on more effective prudential standards for operational risk measurement and, as result, may help ensure adequate capitalisation and reduce the probability of financial distress. The results highlight the importance of using out-of-sample forecasting techniques and the stipulated probability level for the identification of methods that minimise the occurrence of VaR exceptions. Standard models – RiskMetrics and GARCH – that are already widely used by market participants are generally shown to outperform the more computationally intensive wavelet-derived FIGARCH model in estimating VaR across the probability levels

In sum, this research has evaluated the long memory properties of return volatility in fixed income markets. This paper also complements the literature on long memory models and the forecast performance of these models that has attracted interest in other asset classes. In addition, the results of this study may potentially be used to inform portfolio and risk analysis. In particular, it is shown that in the context of VaR estimation existing models based on the GARCH and/or RiskMetrics process are more accurate (and simpler) than their long memory counterpart. Some caveats to these results exist, however. First, squared returns provide a noisy proxy for the 'true' volatility. In the case of this analysis, data constraints limited alternative options. However, future research may find that the application of realised variance may produce more accurate forecasts. Second, future research may also consider exploring the relevance of other long memory models, for example, models with asymmetric effects given that market volatility is often reported as

[1] McCarthy J, Pantolon C, Li HC. Investigating long memory in yield spreads. J Fixed

[2] Schotman PC, Tschernig R, Budek J. Long memory and the term structure of risk. J

[3] McCarthy J, DiSario R, Saraoglu H, Li H. Tests of long-range dependence in interest

[4] Emerging market trading association. Third quarter 2011 debt trading volume survey

being 'directional', i.e., volatility is higher in a down – than an upmarket.

rates using wavelets. Q Rev Econ & Financ. 2004; 44: 180-9.


	- [25] Fleming J, Kirby C, Ostdiek B. The economic value of volatility timing. J Financ. 2001; 56: 329-455.

**Chapter 0**

**Chapter 6**

**Risk Management in Collaborative Systems**

Many domains reached a point in which the knowledge required for skillful, professional practice can no longer be acquired in a decade, factor that generates increased specialization [2]. This increased specialization makes collaboration crucial because complex problems require more knowledge than any particular person possesses. The relevant information required to solve complex problems is normally distributed among different persons or stakeholders. In order to create insight, new ideas or new artifacts it is considered a prerequisite to bring different and often controversial points of view together, and create a shared understanding among stakeholders. It is generally considered that insight moments for creative individuals are the result of working in isolation, but it has been proven that the

Collaboration is a very dynamic process that combines functionality that supports communication, management and involves content handling. During the execution of a project team members are not always collaborating and their work alternates with cooperation, when a greater emphasis is placed on a value-chain model of producing results. Project management is a tool used to provide a team the capabilities required to produce the benefits defined by vision [24]. Risk management is a critical element in defining the relationship between risks, uncertainty and objectives thus contributing to the chances of success in the execution of a project [9]. In the present context of information society both project and risk management should reconsider collaboration by thoroughly understanding its mechanisms and adapting its tools in order to fully harness it. In this study we will present some of the main aspects regarding collaboration that are relevant in order to build an efficient project and risk management strategy. This will be followed by some approaches from software development that we consider relevant for the context and present our risk

The term collaboration is used often when one refers to quite different aspects of working together, like cooperation or even communication [33]. Focusing on national programs that

distribution, and reproduction in any medium, provided the original work is properly cited.

©2012 Podean and Benta, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

©2012 Podean and Benta, licensee InTech. This is a paper distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

Marius Ioan Podean and Dan Benta

http://dx.doi.org/10.5772/50844

**1. Introduction**

Additional information is available at the end of the chapter

role of interaction and collaboration is critical [19].

management approach for similar projects.

cited.

**2. Collaboration**

