Volatility modeling

Volatility is the statistical measure of a risk security. It is calculated by analyzing the standard deviation of the annual returns over a specific period of time. Volatility modeling, therefore, is the technique of analyzing the increase or decrease of the price of a security. It is mostly used in option pricing to determine the increase or decrease of returns of the available assets. Volatility modeling helps identify the pricing behavior of securities and predict fluctuations faster. If the price of a security fluctuates quickly over a short period of time, it is said to have a high volatility. On the other hand, if the price of a security fluctuates sluggishly over a long period of time, it is said to have a low volatility.

Standard deviation and volatility models

The standard deviation is a statistical technique used to determine the amount of variability or dispersion around the price of an asset, which makes it a fundamental tool in volatility modeling. Dispersion is the measure of the difference between the average value of an asset and its actual value. A higher dispersion level is an indication that the level of standard deviation is also high, and the lower the dispersion, the lower the standard deviation. Researchers incorporate standard deviation in volatility modeling to measure the expected risk and determine how substantial a price movement is. To learn more about standard deviation as a method of assessing volatility, contact our volatility modeling online tutors.

Types of volatility

  • Price volatility: Volatility in price is caused by twomain factors that lead to an increased swing in demand and supply. These include:
  • Seasonality: For instance, beach resort prices will hike in summer when people want to spend more time in the beach. They drop in winter when people are not traveling to the beach. This is an example of changes in demand.
  • Weather: For instance, the price of agricultural products depend on the supply. The increase in supply will depend on the atmospheric conditions being favorable to crops.
  • Stock volatility: Some stock prices are highly volatile, and this unpredictability makes investing in stock a risky undertaking. Consequently, investors want a much higher return for the high uncertainty levels. Companies whose stocks are highly volatile need to grow profitably. They have to register a significant increase in earnings as well as in stock prices over time to avoid paying high dividends.
  • Historical volatility: Just as the name hints, historical volatility is the measure of the level of volatility a stock has recorded over the last one year. If the price of the stock varied greatly in the past twelve months, it is more volatile, and hence more risky.
  • Implied volatility: This describes the amount of volatility the options traders think a stock they are analyzing is going to have in the future. One can determine the implied volatility of a given stock by looking at how much the future prices of options vary. If the prices of options is starting to rise, this means that the implied volatility is increasing, everything else being equal.

The significance of volatility modeling

Volatility modeling enables businesses to forecast volatility. Typically, it is used to predict the absolute magnitude of returns. It can also be used to forecast quantiles. Volatility models are used in financial activities such as:

  • Derivative pricing and hedging
  • Risk management
  • Market timing
  • Market making
  • Portfolio selection

In each of these activities, it is the predictability of the volatility that is necessary.  Here is why volatility modeling is essential:

  • A risk manager will want to know the likelihood of his portfolio to decline in the future
  • An options trader must know how much volatility to expect over the life of the contract
  • A manager may need to sell aportfolio or a stock before it becomes too volatile

Using a volatile model ensures that volatility is determined effectively to help businesses and individuals make informed financial decisions. Below are some prediction and estimation approaches used in volatility modeling:

  • Historical/sample volatility measuring
  • Poisson jump diffusion model
  • Geometric Brownian motion model
  • ARCH model
  • GARCH model
  • Stochastic volatility model
  • Implied volatility from derivatives/options

A good volatility model should capture and reflect these estimation approaches for effective data analysis and interpretation.

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