VIX - CBOE Volatility Index

core

Files Size Format Created Updated License Source
2 749kB csv zip 5 months ago 1 hour ago Open Data Commons Public Domain Dedication and License v1.0 CBOE VIX Page
CBOE Volatility Index (VIX) time-series dataset including daily open, close, high and low. The CBOE Volatility Index (VIX) is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices introduced in 1993. Data From the VIX FAQ: > In 1993, the read more
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Data Files

File Description Size Last changed Download
vix-daily 125kB csv (125kB) , json (332kB)
finance-vix_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 129kB zip (129kB)

vix-daily  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
VIXOpen 2 number
VIXHigh 3 number
VIXLow 4 number
VIXClose 5 number

Read me

CBOE Volatility Index (VIX) time-series dataset including daily open, close, high and low. The CBOE Volatility Index (VIX) is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices introduced in 1993.

Data

From the VIX FAQ:

In 1993, the Chicago Board Options Exchange® (CBOE®) introduced the CBOE Volatility Index®, VIX®, and it quickly became the benchmark for stock market volatility. It is widely followed and has been cited in hundreds of news articles in the Wall Street Journal, Barron’s and other leading financial publications. Since volatility often signifies financial turmoil, VIX is often referred to as the “investor fear gauge”.

VIX measures market expectation of near term volatility conveyed by stock index option prices. The original VIX was constructed using the implied volatilities of eight different OEX option series so that, at any given time, it represented the implied volatility of a hypothetical at-the-money OEX option with exactly 30 days to expiration.

The New VIX still measures the market’s expectation of 30-day volatility, but in a way that conforms to the latest thinking and research among industry practitioners. The New VIX is based on S&P 500 index option prices and incorporates information from the volatility “skew” by using a wider range of strike prices rather than just at-the-money series.

License

No obvious statement on historical data page. Given size and factual nature of the data and its source from a US company would imagine this was public domain and as such have licensed the Data Package under the Public Domain Dedication and License (PDDL).

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/core/finance-vix
tree core/finance-vix
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/finance-vix/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/finance-vix/r/0.csv

curl -L https://datahub.io/core/finance-vix/r/1.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/finance-vix/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/finance-vix/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/finance-vix/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/finance-vix/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()
Datapackage.json