TRADING

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 into account the information about…

Read More Parkinson 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. Also referred to as statistical volatility, historical volatility gauges the fluctuations of underlying securities by measuring price changes over predetermined periods of time. It is the…

Read More Close-to-Close Historical Volatility Calculation – Volatility Analysis in Python

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 trading. Beta of an investment is a measure of the risk…

Read More What is Stock Beta and How to Calculate Stock Beta in Python

In the previous post, we presented a system for trading VXX, a volatility Exchange Traded Note. The trading system was built based on simple moving averages.  In this post, we are going to examine the time series properties of VXX in more details. The figure below shows the VXX and its 200-day moving average for…

Read More Stationarity and Autocorrelation Functions of VXX-Time Series Analysis in Python

Time series analysis is an important subject in finance. In this post, we are going to apply a time series technique to a financial time series and develop an investment strategy.  Specifically, we are going to use moving averages to trade volatility Exchange Traded Notes (ETN). Moving averages are used on financial time series data…

Read More A Volatility Trading System-Time Series Analysis in Python

We have written many blog posts about the increase in volatility of volatility. See, for example Is Volatility of Volatility Increasing? What Caused the Increase in Volatility of Volatility? Similarly, last week Bloomberg reported, The sudden rise in volatility in February and March showed that even with strong growth fundamentals, financial markets remain vulnerable. Since…

Read More Black Swan and Volatility of Volatility

Peter Carr recently gave a talk on volatility trading at the Fields institute. Summary: In general, an option’s fair value depends crucially on the volatility of its underlying asset. In a stochastic volatility (SV) setting, an at-the-money straddle can be dynamically traded to profit on average from the difference between its underlying’s instantaneous variance rate…

Read More Volatility, Skew, and Smile Trading

The volatility index was created more than 30 years ago. Since then it has become a favorite tool for both speculation and risk management.  There is now strong evidence that VIX futures and related exchange-traded products are changing the market dynamics. Specifically, in the early days of the VIX, the cash market led the futures.…

Read More What Do Creators of the VIX Think of Volatility?

Last Thursday witnessed, again, another dramatic increase in volatility. The volatility index VIX spiked 44 percent to 16.04%, its highest daily close for the year. As shown below, the VIX futures term structure inverted in the short end. Two days before the event, Helen Bartholomew of Reuters warned that the net short position in the…

Read More VIX Futures Leads Cash Market: Tail Wags Dog