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CSTEP Cluster Sampling for Tail Estimation of Probability. Team. Project Team and Faculty. Created by: Alan Chandler Nathan Wood Eric Brown Temourshah Ahmady Faculty Advisor : Dr. James Schwing Client: Dr. Yvonne Chueh. Project Overview. Project Title:

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Team

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  1. CSTEP Cluster Sampling for Tail Estimation of Probability Team

  2. Project Team and Faculty Created by: Alan Chandler Nathan Wood Eric Brown Temourshah Ahmady Faculty Advisor: Dr. James Schwing Client: Dr. Yvonne Chueh

  3. Project Overview • Project Title: Scenario Reduction Technique for Stochastic Financial Modeling: A Distance-Clustering Sampling Tool Giving Tail Probability Estimation

  4. Tail Probability Estimation • Actuarial sciences • Randomly generated “scenarios” represent financial rate changes over h years {i1, i2, i3, i4, i5, i6, i7, i8, i9…ih) • Each population of scenarios typically more than 10,000

  5. Cluster Sampling • Cluster sampling identifies representative scenarios of extreme cases and their probability • 50 to 100 samplesdesired • Nested sampling Extreme scenarios

  6. Sampling Methods • Three methods used to identify representative samples (pivots) • Significance Method • Euclidean DistanceMethod • Present ValueDistance Method

  7. Clustering Algorithm • Euclidean Distance Method and Present Value Distance Method Sample Sample Sample Sample Sample Sample

  8. Problem to Solve • Insurance firms, as well as actuarial research • Populations stored in spreadsheets • Macros within spreadsheets used to calculate samples

  9. Problem to Solve • Macros are: • Too slow • Difficult to implement • A hassle to use • Provide a stand-alone desktop application that is user-friendly and efficient

  10. Basic Design • Waterfall Process ModelRequirements Design Prototype Construction Testing Installation • Programming languages • C# – Graphical User Interface • C++ – Sampling algorithm • Lua – Formula scripts

  11. Project Requirements • Use Cases

  12. Example Use Case • Process New Data • Import data • Select formula • Choose parameters • Start processing • Export Data

  13. Three Stages of Completion • Stage1: • Import universe, read in scenario data • Apply distance formula to universe • Output to new spreadsheet

  14. Three Stages of Completion • Stage 2: • Import universe, read in scenario data • Apply distance formula to universe • Edit formula constants to users needs • Output to new spreadsheet

  15. Three Stages of Completion • Stage 3: • Import universe, read in scenario data • Edit universe from program • Use nested samples • Apply distance formula to universe • Edit formulas to users needs • Output to new spreadsheet

  16. NonfunctionalRequirements • Performance Constraints • Size of input • Time to process • Memory available • Other Constraints • Windows (XP, Vista, 7) • Numeric precision

  17. Prototype Demo…

  18. Quality Assurance and Risk Management • Client acceptance of prototype and requirements • Present the prototype to the client • Received client’s feedback about the prototype • Modified the project based on client’s feedback • Client approved the final version of the prototype and requirements

  19. Risk Analysis • Unexpected events: (illness, injuries, family problems) • Project does not meet client needs and expectations • Project falls behind

  20. Risk Analysis • Strategies to mitigate the risk • Efficient and effective team work • Good communication with client and advisor • Ensuring that at least two members can perform a specific task

  21. Wrapping Up • Creating a project for tail estimation probability is feasible • Collecting requirements • Learning about project • Design decisions

  22. Question and Answer

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