Study on the CBOE Volatility Data Forecast Using Statistical and Computational Simulations

Abstract

Economic indexes can be influenced by many different factors; therefore, it is difficult to use a single variable linear regression to determine the effectiveness of patterns. Modeling an economic pattern for a focused area and performing data analysis is especially difficult with a complex data pattern. To predict the effectiveness of such a trend, this paper focuses on a specific, objective main factor that determines the economic status in the field of stock markets. The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the stock market’s expectation of volatility implied by S&P 500 index options. It is calculated and disseminated on a real-time basis by the Chicago Board Options Exchange (CBOE) and is commonly referred to as the fear index, or the fear gauge. In this paper, a statistical method is used to model the distribution of the maximum/minimum of a number of samples. Statistical measurements such as exceedance probability that an event exceeds mean value and return period are found based on historical data.