By the end of this training course, trainees will be able to :
- Describe independent events and mutually exclusive events and explain the difference between independent events and conditionally independent events.
- Calculate the probability of an event for a discrete probability function and calculate a conditional probability.
- Define and distinguish between volatility, variance, correlation and covariance.
- Distinguish between important distribution functions (e.g. Uniform, Normal, Lognormal Distribution, Student’s t, Binomial, Poisson)
- Compute and interpret basic statistical measures relating to risk management (e.g. mean, standard deviation, kurtosis, correlation. etc.).
- Construct simple and multiple liner regression and be able to apply these tools in finance.
- Estimate simple volatility using historical financial data and identify the issues with traditional volatility estimation methods.
- Professionals working in:
- Treasury.
- Risk .
- Capital Management.
- Regulatory Compliance.
- Audit.
- Probability Theory in Finance:
- Definition and rules:
- Continuous and discrete data.
- Types of discrete Distribution.
- The Binomial Distribution.
- The Poisson Distribution.
- The Uniform Distribution.
- Types of Continuous Distribution:
- The Normal Distribution.
- The Lognormal Distribution.
- Probability Distributions:
- Random Variables.
- Probability Density Functions and Histograms.
- Joint Distributions (Covariance and Correlation) .
- Descriptive Statistics:
- The Measures of Central Tendency:
- The Arithmetic Mean.
- The Geometric Mean.
- The Median and Mode.
- The Measures of dispersion:
- Variance.
- Standard Deviation.
- The Measures of Shape (Skewness and Kurtosis).
- Regression Analysis in Finance:
- Simple Liner Regression.
- Multiple Liner Regression .
- Hypothesis Testing .
- Breakdown of the (Ordinary Least Squares) OLS Assumptions.
- Time Variation in Risk:
- Moving Average:
- Simple Moving Average (SMA).
- Exponential Moving Average (EMA).
- Practical exercises .