This post presents how to estimate Value at Risk via a variance – covariance method. The following steps outline how to calculate Value at Risk using this method. 1.) Gather stock data and calculate periodic returns...

# Category - Python For Finance

Link to Project: The following Python project allows a user to track their stock portfolio in Python. Since this is a beta version and will continually need adjustments, any changes suggested will be considered. You can also...

Intuition: Rebalancing a portfolio can be thought of as resetting the weights of individual assets chosen. For example, we could have a portfolio of four different assets with an even 25% allocated in each. These weightings can...

Background: Value at risk is the maximum loss possible with a given level of confidence over a predetermined time frame. This post will specifically cover historical VaR, or nonparametric method due to the fact we are using prior...

Value at Risk is the maximum loss on an investment over a given time period with a given confidence level. There are a few different approaches we can take to estimating Value at Risk. Parametric Approach: A historical simulation...

in previous posts, I have discussed Monte Carlo simulations specifically in regards to generating future stock prices. We will now examine a bootstrap sampling technique in regards to simulating stock...

A Monte Carlo simulation is a method that allows for the generation of future potential outcomes of a given event. In this case, we are trying to model the price pattern of a given stock or portfolio of assets a...

Given today’s frothy market environment, it is relatively hard to find value stocks within a thick mire of growth opportunities. As investors, we can a examine a representative market index in order to gauge where the...

The S&P 500 Index: The Standard & Poors 500 Index (S&P 500) consists of 500 of the largest U.S. publicly traded companies. It is widely used as a benchmark index for U.S. investors and arguably one of the most...

Intraday data is especially valuable to algorithmic traders. In general, the more granular the data, the better. And for this purpose, the more data, the better. The problem is that retail investors don’t have an access to...