Financial Risk Modeling

Financial Risk Modeling 

taught by Huybert Groenendaal and Jane Pouzou

 

 
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Aim of Course:

This online course, "Financial Risk Modeling" will cover the most important principles, techniques and tools in Financial Quantitative Risk Analysis. The course has been developed to effectively combine theoretical sessions and real-world application with classroom examples and exercises in order to provide students with a comprehensive overview of Monte Carlo techniques. In addition to discussions of recent innovations in the application of Monte Carlo methods, the course will cover many practical examples, case studies and interactive sessions.

The course will also get the participants comfortable with risk analysis modeling environments (in this case the @RISK software combined with Excel, but the lessons and techniques apply equally well to other modeling environments). Finally, the course will also cover common mistakes and how to avoid them.

After completing this course, you will be able to:

  • Specify why and how a probability distribution is used in a financial model simulation
  • Learn about various probability distributions commonly used in financial applications;
  • Understand uses of time-series models, and characterize the different components of a time series (trend, seasonality, autocorrelation, volatility, mean reversion)
  • Fit various autoregressive models to historical data (ARCH, GARCH, more)
  • Use various technique to include correlations within simulation models
  • Fit appropriate probability distributions to historical data, and assess the fit (AIC, etc.)
  • Interpret results from simulations models, including eNPV, Value at Risk (VaR), and the probability of negative NPV
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1: Introduction

  • Introduction to quantitative risk analysis and Monte Carlo
    • Core ideas of risk analysis
    • What is a probability distribution
    • How scenarios are generated, outputs produced and analyzed, why it works
  • Distributions
    • Most common univariate distributions in finance
    • Introduction to statistical descriptors-mean,mode, standard deviation, skewness, kurtosis
    • Example financial model and exercise


WEEK 2:
Forecasting variables over time (time-series)

  • Conceptual underpinning of timeseries, trend, volatility, seasonality, autocorrelaton, cyclicity, mean reversion
  • Various techniques for forecasting future sales
  • GBM, GBM with mean reversion, jump diffusion, both, seasonality
  • Autoregressive models: AR, ARCH, GARCH
  • Markov chains
  • Discussion of attributes and application of different stochastic time series
  • Example model and exercise


WEEK 3:
How to Deal with Correlations

  • Understanding why correlations are important
  • Various techniques of including correlations:
    • (Spearman) Rank order
    • Lookup tables
    • Envelop methods
    • Logical relationships
    • Copulas
  • Example financial model and exercise

WEEK 4: Model Fitting and Conclusion

  • Fitting distributions, time series and copulas to historical data
    • Distributions (MLE)
    • Time series (MLE)
    • Copulas (MLE)
    • Fit comparisons with information criteria (i.e. AIC, BIC)
    • Example model and exercise
  • Emphasis on examples model and practical case
    • VAR, expected shortfall examples
    • Some time series examples (including fitting to past financial datasets)
    • Analyzing correlations between stochastic variables, fitting copulas and applying then in a simulation model
    • Basel II example with operational risk

Risked NPV model of the development, launch and commercialization of a new product


HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

In addition to assigned readings, this course also has supplemental readings available online, and an end of course data modeling project.

Financial Risk Modeling

Who Should Take This Course:
Anyone in corporate finance, investment banking, asset/investment/fund mangement, merchant banking, insurance companies, software/technology, government/public body and academia with an interest in applying quantitative probabilistic techniques in the fields of finance and risk.
Level:
intermediate/advanced
Prerequisite:
  • Risk Simulation and Queueing
  • All models are developed using Excel and @Risk. It is therefore essential that all participants be proficient in Excel, including the use of Excel functions.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses

Specialization:
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Optimization Specialization which discusses linear programming, nonlinear programming, network flow, decision analysis, queuing, simulation.

College credit:
Financial Risk Modeling has been evaluated by the American Council on Education (ACE) and is recommended for the upper division baccalaureate degree category, 3 semester hours in financial risk management, financial econometrics or applied statistics. Note: The decision to accept specific credit recommendations is up to each institution. More info here.

INFORMS CAP:
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP® analysts accrue Professional Development Units to maintain their certification .
Course Text:

Course participants will be provided with weekly digital reading material, as well as the required course text once enrolled in the course. Though completely optional and not required, the following texts are recommended as additional resources  that will give more in-depth understanding of the model as well as instruction for building good and practical models.

Risk Analysis: A Quantitiative Guide, 2nd Edition by David Vose, from Wiley.

Make sure to get the 2nd version (which uses @RISK), and not the 3rd version, since the latter uses a different software tool.

Software:
Course illustrations and homework assignments will use @lRisk, the Monte Carlo simulation and financial risk analysis tool from Pallisade Corporation. A 60 day free trial license will be provided to all course participants at the start of the course.
Instructor(s):

Dates:

June 12, 2020 to July 10, 2020

Financial Risk Modeling

Instructor(s):

Dates:
June 12, 2020 to July 10, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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