Learn how to calculate Value at Risk (VaR) to effectively assess financial risks in portfolios, using historical, variance-covariance, and Monte Carlo methods.

Calculating variance is easy using Python. Before diving into the Python code, I’ll first explain what variance is and how you can calculate it. By the end of this tutorial you’ll have a ...

This paper aims to evaluate the performance of different value-at-risk (VaR) calculation methods, allowing us to identify models that are valid for use in emerging markets. We apply several widely ...

Abstract ABSTRACT Expanding the realized variance concept through realized skewness and kurtosis is a straightforward process. We calculate one-day forecasts for these moments with a simple ...

We study bounds on the Value-at-Risk (VaR) of a portfolio when besides the marginal distributions of the components its variance is also known, a situation that is of considerable interest in risk ...

A new model for financial returns with time varying variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously suggested NIG ...

Conditional Value at Risk is a powerful metric that gives portfolio managers a look at the potential reality of a worst-case scenario.

There are three methods of calculating Value at Risk (VaR), including the historical method, the variance-covariance method, and the Monte Carlo simulation.

If you’re wondering how to find the variance in your data set, look no further. Here’s how to calculate variance in a snap with Pandas.