With the significant advances in Information and Communications Technology (ICT) over the last half century, there is an increasingly perceived vision that computing will one day be the 5th utility (after water, electricity, gas, and telephony). This computing utility, like all other four existing utilities, will provide the basic level of computing service that is considered essential to meet the everyday needs of the general community. To deliver this vision, a number of computing paradigms have been proposed, of which the latest one is known as Cloud computing. Hence, in this paper, we define Cloud computing and provide the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs). We also provide insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA)-oriented resource allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global Cloud exchanges and markets. Then, we present some representative Cloud platforms, especially those developed in industries along with our current work towards realizing market-oriented resource allocation of Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the difference between High Performance Computing (HPC) workload and Internet-based services workload. We also describe a meta-negotiation infrastructure to establish global Cloud

Abstract. The proposed New Accord (Basel II) established by the Basel Committee on Banking Supervision calls for an explicit treatment of operational risk. Banks are required to demonstrate their ability to capture severe tail loss events. Value at risk is a risk measure that could be used to derive the necessary regulatory capital. Yet operational loss data typically exhibit irregularities which complicate the mathematical modeling. It is shown that traditional modeling approaches, including extreme value theory, reach their limits as the structure of operational loss data is barely in line with the modeling assumptions. 1.

The new Basel Capital Accord has opened up a discussion concerning the measurement of operational risk for banks. In our paper we do not take a stand on the issue of whether or not a quantitatively measured risk capital charge for operational risk is desirable; however, given that such measurement will come about, we review some of the tools which may be useful towards the statistical analysis of operational loss data. We also discuss the relevance of these tools for foreign reserves risk management of central banks.

This paper is based on a talk with the above title given at the SemStat meeting on Extreme Value Theory and Applications in Gothenburg on December 13, 2001

We review, from the point of view of credit risk management, classical Extreme Value Theory in its one–dimensional (EVT) as well as more–dimensional (MEVT) setup. The presentation is highly coloured by the current economic crisis against which background we discuss the (non–)usefulness of certain methodological developments. We further present an outlook on current and future research for the modelling of extremes and rare event probabilities.

The study presents the results of an analysis of credit risk factors of the Slovenian banking system. The objective of the empirical analysis is to establish which macroeconomic factors influence the systematic credit risk of the Slovenian banking loan portfolio. The research results have confirmed the main hypothesis that certain macroeconomic factors have a major influence on the examined credit risk. We could conclude that the credit risk of the loan portfolio depends on the employment or unemployment rate in Slovenia, on short and long-term interest rates of Slovenian banks and the Bank of Slovenia, and on the value of the Slovenian stock exchange index. We cannot claim that the examined credit risk depends on the inflation rate in Slovenia, the growth of gdp (industrial production), eur and usd exchange rates or the growth of Slovenian import and export. Key Words: Slovenian banking system, credit risk factors, loan portfolio, Bank of Slovenia, macroeconomic factors

In this paper, we give the axiomatic characterization of risk measures and discuss the treads of developments in this area. The main recently proposed risk measures are presented, and their properties and relations are discussed. The corresponding versions of dynamic risk measure are also briefly introduced.

This paper analyzes portfolio risk and volatility in the presence of constraints on portfolio rebalancing frequency. This investigation is motivated by the incremental risk charge (IRC) introduced by the Basel Committee on Banking Supervision. In contrast to the standard market risk measure based on a ten-day value-at-risk calculated at 99 % confidence, the IRC considers more extreme losses and is measured over a one-year horizon. More importantly, whereas ten-day VaR is ordinarily calculated with a portfolio’s holdings held fixed, the IRC assumes a portfolio is managed dynamically to a target level of risk, with constraints on rebalancing frequency. The IRC uses discrete rebalancing intervals (e.g., monthly or quarterly) as a rough measure of potential illiquidity in underlying assets. We analyze the effect of these rebalancing intervals on the portfolio’s profit and loss distribution over a risk-measurement horizon. We derive limiting results, as the rebalancing frequency increases, for the difference between discretely and continuously rebalanced portfolios; we use these to approximate the loss distribution for the discretely rebalanced portfolio relative to the continuously rebalanced portfolio. Our analysis leads to explicit measures of the impact of discrete rebalancing under a simple model of asset dynamics. 1

Abstract not found

Author for correspondence. Dependence modelling and estimation is a key issue in the assessment of portfolio risk. When measuring extreme risk in terms of the Value-at-Risk, the multivariate normal model with linear correlation as its natural dependence measure is by no means an ideal model. We suggest a large class of models and a new dependence function which allows us to capture the complete extreme dependence structure of a portfolio. We also present a simple nonparametric estimation procedure. To show our new method at work we apply it to a financial data set of zero coupon swap rates and estimate the extreme dependence in the data.