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CBOE Volatility Index (VIX) from December 1985 to May 2012 (daily closings) In finance, volatility (usually denoted by "σ") is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns. Historic volatility measures a time series of past market prices.
Rank Date Close Change Net % 1 1987-10-19 : 224.84 −57.86 −20.47 2 1929-10-28: 22.74 −3.20 −12.34 3 2020-03-16: 2,386.13 −324.89 −11.98 4
CBOE also calculates the Nasdaq-100 Volatility Index (VXNSM), CBOE DJIA Volatility Index (VXDSM) and the CBOE Russell 2000 Volatility Index (RVXSM). [6] There is even a VIX on VIX (VVIX) which is a volatility of volatility measure in that it represents the expected volatility of the 30-day forward price of the CBOE Volatility Index (the VIX). [10]
S&P 500 with 20-day, two-standard-deviation Bollinger Bands, %b and bandwidth. Bollinger Bands (/ ˈ b ɒ l ɪ n dʒ ər /) are a type of statistical chart characterizing the prices and volatility over time of a financial instrument or commodity, using a formulaic method propounded by John Bollinger in the 1980s.
The volatilities in the market for 90 days are 18% and for 180 days 16.6%. In our notation we have , = 18% and , = 16.6% (treating a year as 360 days). We want to find the forward volatility for the period starting with day 91 and ending with day 180.
Historical volatility is a direct measure of the movement of the underlying’s price (realized volatility) over recent history (e.g. a trailing 21-day period). Implied volatility, in contrast, is determined by the market price of the derivative contract itself, and not the underlying.
February 5, 2018: After months of low volatility, S&P 500 registers a new largest daily point loss of 113.19 points, equivalent to more than 4%. Three days later, the index suffered another heavy loss of nearly the same amount. [48] October 13, 2008: S&P 500 marks its best daily percentage gain, rising 11.58 percent.
Starting from a constant volatility approach, assume that the derivative's underlying asset price follows a standard model for geometric Brownian motion: = + where is the constant drift (i.e. expected return) of the security price , is the constant volatility, and is a standard Wiener process with zero mean and unit rate of variance.