Data Analysis for
RISK MANAGEMENT
Course Outline
In current market conditions an understanding of the key econometrics principles is essential to risk management. This programme introduces the main concepts in econometrics that are needed to understand and manipulate data sets and basic models to tackle practical problems faced daily by organisations operating in the capital markets. The course is highly relevant for anyone who wishes to increase their understanding of analysing or interpreting financial market data.
Who The Course is For
- Traders
- Risk Managers
- Strategists
- Consultants
- Middle and Senior Managers
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Prior Knowledge
Basic knowledge of finance and statistics
This
program is eligible for 16 Continuing Education credit hours from the
CFA Institute. If you are a CFA Institute member, CE credit for your participation
in this program will be automatically recorded in your CE Diary.
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Day One
Understanding the Data - Descriptions and Distributions
- Types of data
- Descriptive measures of data
- Measures of association
- Visualising data
- Variance-Covariance matrix
- Introduction to statistical distributions of data
Workshop: Visualising non-normality
Making Links and Drawing Conclusions
- Statistical distributions of data continued
- Continuous distributions
- Fat tailed distributions
- Sampling from distributions
- Central limit theorem
- Interval estimation
- Hypothesis testing
Workshop: Testing for the presence of non-normality
Day Two
Fundamental Techniques - Linear Regression
- Basics of regression
- Least squares method
- Residual analysis
- Heteroskedasticity, multicollinearity and autocorrelation
- Forecasting
Workshop: Regression and issues with regression
Fundamental Techniques - Value at Risk and Time Series Analysis
- Basic time series patterns
- Essential time series models
- Forecasting using time series models
- Stationarity
- Complex time series models
- Measuring correlation in time series data
- Problems with time series models
- Introduction to Value at Risk
Workshop: Application of time series models
