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Enhancing Tail Probability Estimation Using Cluster Sampling Techniques in Financial Modeling

This project develops a standalone desktop application for tail probability estimation in stochastic financial modeling, utilizing cluster sampling techniques. By generating over 10,000 financial scenarios, we identify representative extreme cases efficiently. Employing three sampling methods—Significance Method, Euclidean Distance Method, and Present Value Distance Method—we ensure robust data processing. The application features an intuitive user interface built in C#, alongside sampling algorithms programmed in C++. It aims to streamline operations for insurance firms and actuarial researchers, addressing current macro inefficiencies.

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Enhancing Tail Probability Estimation Using Cluster Sampling Techniques in Financial Modeling

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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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