# Parkinson Historical Volatility Calculation – Volatility Analysis in Python

In the previous post, we discussed the close-to-close historical volatility. Recall that the close-to-close historical volatility (CCHV) is calculated as follows, where xi are the logarithmic returns calculated based on closing prices, and N is the sample size. A disadvantage of using the CCHV is that it does not take …

# Close-to-Close Historical Volatility Calculation – Volatility Analysis in Python

In a previous post, we touched upon a stock’s volatility through its beta. In this post, we are going to discuss historical volatilities of a stock in more details. If you want to use the online version, go to Historical Volatility Online Calculator. Also referred to as statistical volatility, historical …

# Equity Beta: What Is, Example, Formula, How to Calculate Stock Beta in Python

What is equity (or stock) beta? In finance, beta measures a stock’s volatility with respect to the overall market. It is used in many areas of financial analysis and investment, for example in the calculation of the Weighted Average Cost of Capital, in the Capital Asset Pricing Model and market-neutral …

# Value At Risk – Financial Risk Management in Python

Value at Risk (VaR) is a tool for measuring a portfolio’s risk. 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 …

# Another Misuse of Financial Derivatives

Just like any financial derivatives that were initially designed for risk management purposes, interest rate swaps are an effective tool for managing and transferring interest rate risks as long as those risks are well understood.  But as banks and financial institutions are constantly trying to invent new financial products to …

# Valuing European Options Using Monte Carlo Simulation-Derivative Pricing in Python

In a previous post, we presented a methodology for pricing European options using a closed-form formula. In this installment, we price these options using a numerical method. Specifically, we will use Monte Carlo simulation. Recall that, A call option gives the buyer the right, but not the obligation to buy …

# Black-Scholes-Merton Option Pricing Model-Derivative Pricing in Python

The Black-Scholes-Merton model is one of the earliest option pricing models that was developed in the late 1960s and published in 1973 . The most important concept behind the model is the dynamic hedging of an option portfolio in order to eliminate the market risk. First, a delta-neutral portfolio is …

# Derivative Pricing and Valuation

A derivative is a financial instrument whose price is derived from one or more underlying assets. Thus, in very simple words, the price and value of a derivative stem from its underlying assets. The underlying assets can be anything that have some value. In the first part of this article, …

# Are Collateralized Loan Obligations the New Debt Bombs? Part Two

In a previous post, we discussed the risks of Collateralized Loan Obligations, a type of complex credit derivatives.  Since then, the trend in securitizing loans is still upward. Nowadays, not only performing loans but also non-performing loans are being securitized and sold to investors. A non-performing loan is a loan …

# Valuing a Convertible Bond-Derivative Pricing in Python

In a previous post, we presented a theoretical framework for pricing convertible bonds and preferred shares.  We also provided an example of pricing a convertible bond in Excel. In this installment, we present an example of pricing a convertible bond in Python. Recall that a convertible bond (or preferred share) …