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Example: Pricing a Multi-Period CDS with a Risky Counterparty Let s look again at the three-year credit default swap on Company X s bond. Previously, we found that if the counterparty (i.e., protection seller) is risk free, the price would be 43 basis points per annum. Let s now suppose that the protection seller is subject to default risk. Indeed, let s think about two potential sellers of protection on Company X: The lower-risk protection seller has a credit spread (over Treasuries) of 45 basis points; and the higher-risk protection seller has a credit spread of 65 basis points. Clearly, the buyer of the credit protection will pay less for the three-year protection to the higher-risk seller than they would to the lower-risk seller. The question is how much and the answer to this question depends on the correlation of default between Company X and the protection seller. The following table provides the prices for credit default swaps for both of the protection sellers, considering different correlations of default between Company X and the protection seller. Lower Risk Protection Seller 42 bps 40 bps 38 bps Higher Risk Protection Seller 39 bps 37 bps 35 bps

Suppose now that the focal sets and the distributions computed from them represent your knowledge about some domain and that you have to decide on a point of the domain without being able to gather further information about the obtaining state You could base your decision on the possibility distribution, which suggests the point (a3 , b3 ), or on the IRP probability distribution, which suggests the point (a1 , b1 ) We believe that in this example most people would agree that the point (a1 , b1 ) is the better choice, because there is evidence of two contexts, in which the point (a1 , b1 ) definitely is the correct description of the prevailing state In contrast to this, the point (a3 , b3 ) is covered by the focal sets of three contexts, but in all of them it is only possible among others.

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Remarks While cooptimization of all midterm decisions is desirable, existing software systems cannot achieve this. Different software is required for different components. For example, specialized software that optimizes the dispatch of hydro units is separate from packages that dispatch other technologies. Output from one study is used as input into another. All midterm plans should be subject to a power ow analysis to ensure that power ows resulting from the plan are feasible. This is accomplished by testing the plan against prede ned scenarios after the plan has been set as opposed to performing the power ow analysis within the dispatch routine. Midterm planning is an ongoing activity. Adjustments are made as system conditions change. Probabilistic analysis is often used to address uncertainty. For example, the cost adder applied to an environmentally constrained unit may be calculated under multiple scenarios with probabilities assigned to each outcome.

Knowledge of both wide-area and local campus topology is important for understanding potential bottlenecks and debugging end-to-end problems.

Although it is possible that the point (a3 , b3 ) is the correct one for all three contexts, we usually consider this to be unlikely and therefore decide against it As a consequence in this situation the insufficient reason principle approach seems to be superior to the possibilistic one..

A relational DBE supports four basic types of semantic integrity constraints or rules:

CustomerInfo: Contains the usual customer information (name, address, credit card info, and so on), an account ID (primary key), and a password. The customer must supply the correct password to gain access to the application. CustomerPortfolio: Contains an account ID (primary key), a stock ticker symbol, and a number representing how much of this security the customer holds. SecurityInfo: Contains a stock ticker symbol, a trade date, and the selling price of the security on the trade date. This table uses a compound key consisting of the stock ticker symbol and the trade date. TransactionHistory: Contains an account ID, a transaction date and type, a stock ticker symbol, and the number of shares traded on the transaction date. This table uses a compound key of the account ID, transaction date, stock ticker symbol, and the transaction type (buy or sell).

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