A stock trader will generally have access to daily, weekly, monthly, or quarterly price data for a stock or a stock portfolio. Using this data he can calculate corresponding returns from the stock (daily, weekly, monthly, quarterly returns). He can use this data to calculate the standard deviation of the stock returns. The standard deviation
Bonds with embedded options such as call options and put options also have volatility risk. This happens because any factor that affects the value of the embedded option will also impact the value of the bond. We earlier learned that interest rates affect embedded options. When interest rates rise, the price of the embedded call
GARCH, Generallized AutoRegressive Conditional Heteroskedasticity, is one of the popular methods of estimating volatility in finance. GARCH estimates volatility similar to EWMA, however, it adds more information to the series related to mean reversion. Also some people use both EWMA and GARCH, EWMA has been widely superceded by GARCH. There are three main steps in
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
Both interest rates and underlying stock’s volatility have an influence on the option prices. Impact of Interest Rates When interest rates increase, the call option prices increase while the put option prices decrease. Let’s look at the logic behind this. Let’s say you are interested in buying a stock which sells at $10 per share.
We know that the prices of different financial assets such as currencies and stocks are constantly fluctuating as traders buy and sell these assets. The variation in the prices over a period of time is called volatility. The volatility tells us about how turbulent the price is and is an indicator of the risk involved.
The simple answer is the standard deviation of periodic returns. This video takes some sample data for closing prices of a stock and demonstrates how volatility is calculated in Excel. In finance, such as for price series, usually log returns are used, where log is the natural logarithm.
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
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.
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.