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INSY%20434-001%20LAB%201_Financial_Tools_Data_Bases%20WINTER%202010

INSY%20434-001%20LAB%201_Financial_Tools_Data_Bases%20WINTER%202010

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INSY 434-001 Introduction: Computational Tools and  Financial D/Bs Lab instructor: Yong Lee Lab 1
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COMPUTATION TOOLS Languages C, C++, Java, Fortran, VBA and etc Packages MATLAB, STATA, SAS, R, S-PLUS and etc Other easier tools EXCEL (with VBA) or other statistical tools
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FINANCIAL DATABASES McGill’s available D/Bs CRSP, Compustat, OptionMetrics, RiskMetrics, TAQ, Datastream, Bloomberg(only through its terminal), and etc Other data providers S&P, Moody’s, Fitch, Duff & Phelps ... : credit rating agencies Markit: specialized in CDS and other credit derivative D/Bs
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ABOUT MATLAB? Pros Most popular and standard tool in financial industry and engineering and science Cost- and time-efficient to learn, much easier than C or C++ Powerful for matrix, optimization (similar to Excel Solver) and numerical analysis (e.g.) Discrete solving continuous Partial Differential Equations, numerical analysis, extracting implied volatility using Black-Scholes
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Unformatted text preview: analysis, extracting implied volatility using Black-Scholes model, etc. Cons WHAT TO LEARN Basics Variable creation, declaration, assigning values to variables, random number generation and other pre-requisite skills required for simulation Vectors and matrixes Looping skills: ‘if’, ‘for’, ‘while’, and other looping skills Use vectors wherever possible. More cost- and time-efficient LET’S HIT THE WALL . ....... Data running procedure 1) Explore databases (e.g., CRSP, Compustat, Option metrics, Yahoo-finance, US-Treasury (Prof. French’s site or Treasury)) 2) Downloading time-series and/or cross-sectional data from the above D/Bs 3) Cleanse and process (e.g., with Access, Text Pad, STATA, SAS or any other packages comfortable for you) in order to kill or correct raw data which is either missing or wrong...
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