This video illustrates how to calculate the historical volatility (moving average volatility), using the example of historical returns. Historical daily volatility is the square root of the daily variance estimate. This video is developed by David from Bionic Turtle.

# Volatility

## Approaches to Estimating Volatility

There are lots of ways to estimate volatility. This video provides you an overview of the different approaches. It talks about implied volatility (forward looking) and deterministic (constant) and focus on stochastic volatility: volatility that changes over time, either via (conditional) recent volatility and/or random shocks. This video is developed by David from Bionic Turtle.

## Using Excel’s Goal Seek Function to Estimate Implied Volatility

In this video, you will learn how to estimate implied volatility. Using the market price for an option on Google’s stock, the video demonstrates how to use Excel’s GOAL SEEK function to estimate implied volatility. Implied volatility is a reverse-engineering exercise: we find the volatility that produces a Model Value = Market Price. This video […]

## Volatility: Moving Average Approaches

Within stochastic volatility, moving average is the simplest approach. It simply calculates volatility as the unweighted standard deviation of a window of X trading days. This video demonstrates three “flavors:” population variance (volatility = SQRT[variance]), sample, and simple. This video is developed by David from Bionic Turtle.

## Volatility: Exponentially Weighted Moving Average (EWMA)

The EWMA approach to volatility is an improvement over simple volatility because it assigns greater weight to more recent observations (in fact, the weights are proportional). This video explains the EWMA approach. This video is developed by David from Bionic Turtle.

## Using GARCH (1,1) Approach to Estimate Volatility

This video provides an introduction to the GARCH approach to estimating volatility, i.e., Generalized AutoRegressive Conditional Heteroskedasticity. GARCH is a preferred method for finance professionals as it provides a more real-life estimate while predicting parameters such as volatility, prices and returns. GARCH(1,1) estimates volatility in a similar way to EWMA (i.e., by conditioning on new […]

## How to Forecast Volatility Using GARCH (1,1)

This video discusses how to use GARCH(1,1) to forecast future volatility. The key parameter is persistence (alpha + beta): high persistence implies slow decay toward the long run average. GARCH models were developed by Robert Engle to deal with the problem of auto-correlated residuals (which occurs when you have volatility clustering for example) in time-series […]

## Calculate Historical Volatility Using EWMA

Volatility is the most commonly used measure of risk. Volatility in this sense can either be historical volatility (one observed from past data), or it could implied volatility (observed from market prices of financial instruments.) The historical volatility can be calculated in three ways, namely: Simple volatility, Exponentially Weighted Moving Average (EWMA) GARCH One of […]