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Course Code DECS622

Course Title Risk Analytics

Credits 3

Course Objectives The course concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models.

Course Description    

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Probability mathematics and simulation: Probability distribution equations; probability rules; statistical measures Building and running a model: Model design and scope; Building models that are efficient; most common modeling errors Basic Random Processes: Binomial process, Poisson process, Hypergeometric process, Central limit theorem, Renewal processes Fitting Distributions to data: Analysing properties of the observed data, Fitting a nonparametric distribution to the observed data, Fitting a first-order parametric distribution to the observed data, Fitting a second-order parametric distribution to the observed data Forecasting with uncertainty: Common Financial Time Series models, Autoregressive models, Markov chain models, Time series models with leading indicators, Long-term forecasting Modelling correlation and dependencies: Rank-order correlation, The Envelope method, Multiple correlation using look-up table Testing and modeling causal relationships: Types of model to analyze data, From risk factors to causes, Evaluating evidence, The limits of causal arguments, Is causal analysis essential? Checking and validating a model: Spreadsheet model errors, checking model behavior, comparing predictions against reality Insurance and finance risk analysis modeling: operational risk modeling, credit risk, credit ratings and Markov chain models, measures of risk, modeling a correlated insurance portfolio Project Risk Analysis: Cost risk analysis, scheduled risk analysis, portfolio of risks.

Course Delivery The course places a lot of emphasis on modeling and hands-on assignments. A series of class room exercises and/or assignments will enable students to model problems and use appropriate tools for the same. The entire course will have substantial emphasis on problem solving using SAS EG and/or IBM SPSS. Around 25-30% of the course is proposed to be delivered by practitioners from the industry.


Textbook 

Vose David, Risk Analysis: A Quantitative Guide, John Wiley & Sons; 3rd Edition

Reference Books & Additional Reading Material   

Coleman Thomas S.,Quantitative Risk Management + Website: A Practical Guide to Financial Risk, John Wiley & Sons Inc LaursenGert H. N. and ThorlundJesper, Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, John Wiley & Sons, I Ed, 2010 http://www.nedarc.org/tutorials/utilizingData/communicateNumbersEffectively/communi catingStatistics.html


risk-analytics