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Going into October the bond market was falling sharply while commodity prices were firming The strong rebound in bond prices in late-October (reflecting a flight to safety during that month's stock market crash) witnessed a sharp pullback in commodities Commodities then rallied during November while bonds weakened In an unusual development both markets then rallied together into early 1988 That situation didn't last long, however Figure 36 shows that early in January of 1988 bonds rallied sharply into March while the CRB Index sold off sharply, hi March, bonds peaked and continued to drop into August The March peak in bonds coincided with a major lowpoint in the CRB.



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Do not dwell in the past, do not dream of the future, concentrate the mind on the present moment. The Buddha

which is exactly (10.1). The expectation is calculated under the risk-neutral measure Q: dr = (r, u)du + (r, u)dz, subject to some initial condition: r(t) = r This is the risk-neutral process for the short rate the short rate process under the risk neutral measure Q (in the risk-neutral world). u [t, T] (10.21)

13. Create another shell script and name it sc2. Have it read as follows:





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Find a short list of U.S. small-cap value products for possible inclusion in the Fund. The manager is a replacement for DVA. The size of the allocation will be approximately $20 million (based on the current market value of DVA s portfolio). There are ve minimum manager requirements for inclusion in the search: 1. Three-year track record. 2. Three-year Sharpe ratio in the top quartile among peer group. 3. Portfolio that will complement the fund s small-cap growth component. 4. Maximum fee of 100 basis points per annum. 5. To qualify as a market leader, the head portfolio manager needs to have at least 10 years experience managing small-cap portfolios. Based on these objectives and requirements, the search will be pretty wide open. The Fund s investment team has, however, made some very speci c requests with respect to manager tenure and product life. Using the information on the Fund s overall objective and the speci c manager requirements, we can create a screen that ful lls our requirements.

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Identifying fraudulent activities: Insurance fraud results from a number of situations, including in ated claims, misrepresented facts, and claims for nonexistent injuries or damages. Effective fraud detection would lead to signi cant nancial bene ts to the industry. Successful fraud detection would avoid the costs associated with processing fraudulent claims and allow legitimate claims to be processed faster. Today, only a small percentage of fraudulent cases are identi ed, and hence there is a need for more effective detection of these cases. Historically, fraud detection is performed while processing claims using speci c rules that suggest a claim might be fraudulent. These rules were manually developed by specialists who attempt to discern factors that differentiate fraudulent claims from legitimate claims. The rules identi ed were only able to identify a small handful of cases. Data mining is now being used to improve the quality of these rules. Rules developed in conjunction with data mining techniques can also be constantly updated to re ect changes in illegitimate activity over time. The improved rules help to identify more fraudulent cases, in addition to accelerating the processing of those cases predicted to have a low probability of fraud. Any modeling effort, however, faces a number of challenges. Since the investigation of fraudulent claims is expensive and time-consuming, any prediction must attempt to maximize detection with minimal false positives. The availability of data also presents a challenge, since the number of fraudulent cases currently detected and prosecuted is low. Either these small data sets are used or data sets containing signi cant amounts of noise need to be used. This is because the larger number of positive cases has not been fully investigated, and hence will include cases that are not fraudulent.

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