PayPal Holdings, Inc. (PYPL) Options History
Historical options analytics archive for PYPL with monthly max pain, implied volatility, gamma exposure, and put/call data.
132 months of complete options data available.
PYPL monthly aggregates
Month-by-month rollups derived from the daily snapshot archive for PYPL. Volatility and put/call columns are averages across trading days within the month; max pain, net GEX, and net DEX are the end-of-month values (last trading day of the month).
| Month | Days | Avg ATM IV | Avg IV Rank | End Max Pain | End Net GEX | End Net DEX | Avg P/C |
|---|---|---|---|---|---|---|---|
| 2026-06 | 20 | 35.0% | 29.0% | $44.00 | $21.1M | -$216.8M | 0.54 |
| 2026-05 | 19 | 33.9% | 25.1% | $45.00 | $23.6M | $36.3M | 0.50 |
| 2026-04 | 21 | 50.4% | 65.5% | $50.00 | $31.5M | -$694.1M | 0.42 |
| 2026-03 | 20 | 43.9% | 38.0% | $45.00 | $20.9M | -$75.0M | 0.51 |
| 2026-02 | 19 | 41.8% | 33.4% | $45.00 | $26.4M | -$380.5M | 0.44 |
| 2026-01 | 20 | 44.4% | 39.0% | $60.00 | -$8.4M | $561.7M | 0.54 |
This archive aggregates PYPL's daily end-of-day options snapshots into monthly summaries, spanning 2015-07 through 2026-06. Each month rolls up the underlying snapshot archive, which provides continuous end-of-day coverage from 2007 to present: implied-volatility levels, IV rank, and the put/call ratio are time-averaged across the month; total call and put volume are summed; and dealer positioning (net gamma and delta exposure) and the max-pain strike are taken at the month's final trading day. The result is a long-horizon view of how PYPL option pricing, volatility regime, and dealer hedging pressure evolved month over month, useful for backtesting strategy assumptions and for studying volatility-regime shifts around earnings and macro events. The most recent aggregated month (2026-06) shows an average ATM implied volatility near 35.0%, a month-end max-pain strike around $44.00, an average put/call ratio of 0.54.
2026
Jan | Feb | Mar | Apr | May | Jun
2025
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2024
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2023
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2022
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2021
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2020
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2019
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2018
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2017
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2016
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec
2015
Jul | Aug | Sep | Oct | Nov | Dec
Frequently asked PYPL history questions
- How much options history is available for PYPL?
- This archive holds 132 months of PYPL options analytics, spanning 2015-07 through 2026-06. Each entry is a monthly rollup of PYPL's daily end-of-day options snapshot record, which provides continuous coverage from 2007 to present. Use the year-grouped links on this page to jump to any specific month within the PYPL archive.
- What data does each monthly PYPL aggregate contain?
- Every monthly row summarizes that month of PYPL option activity: time-averaged ATM implied volatility and IV rank, the month-end max-pain strike, end-of-month net dealer gamma (GEX) and delta (DEX) exposure, the average put/call ratio, and total call and put volume. For example, 2026-06 recorded an average ATM implied volatility near 35.0%, an average IV rank of 29.0%, a month-end max-pain strike around $44.00, an average put/call ratio of 0.54.
- How is the PYPL options-history archive built and how often does it update?
- The archive is derived from PYPL's daily end-of-day options snapshots, which capture spot, the full listed chain, implied volatility, and dealer-positioning exposures each trading day. Those daily records are rolled up into the monthly summaries shown here and refreshed as new end-of-day data lands. Traders use the long-horizon view to backtest strategy assumptions, study how PYPL's volatility regime shifts around earnings and macro events, and compare current dealer positioning against historical norms.