Portfolio & Investment Analysis with Python

Risk Analysis

Value at risk modeling:

Value at risk (VaR) is a measure of the risk of loss for investments. It estimates how much a set of investments might lose (with a given probability), given normal market conditions, in a set time period such as a day. The value at risk can be assessed with four approaches:

Historic (Non-parametric)

Parametric Gaussian Model

Cornish-Fisher (Semi-Parametric)

Skewness & Kurtosis

There is has been enough evidence that stock returns and hedge fund indices are not normally distributed. Thus,the Gaussian model cannot be applied without considering for the skewness and kurtosis.Computating of volitality,the skewness and kurtoisis of the returns for each fund provides us insights about the probability of losses made,as positive excess kurtosis and the negative skewness suggest that large losses are likely.

Maximum Drawndown Analysis:

Maximum Drawdown is the maximum loss from the previous high to the subsequent low.Alternatively, drawdown can be also measured in terms of the longest period the secuirty has gone between two peak prices.

Extracting returns:

We would study the divergence between the returns offered by large and small cap stocks over past decades by compuating the monthly & annualized returns for stocks achieving the 10th and 90th percentaile of market capitalization in the dataset.