Option Panda User Manual

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15. What is Dynamic Sigma Adjustment?

Although we have revealed the formula for option pricing, it is still a challenge to obtain an accurate Sigma as an input parameter. Financial specialists would know that Sigma(Implied Volatility) derived by Black-Scholes Formula is just a very subjective indicator of an option offer’s risk appetite. Anyone can derive the Implied Volatility from the formula given a definite option price, however not the other way around, as Implied Volatility is not obtainable in other ways(That’s the cause that Sigma in the Black-Scholes Formula is called “Implied” volatility).

To obtain as much real-time Sigma as possible to reflect market fluctuations, we use the following method called "Dynamic Sigma Adjustment" to update Sigma at high frequency:

- We use historical volatility as the initial input parameter for option price calculation;(Chainlink data feed or we specify an oberved market value initially)
- If more than 90% options generated within a certain time-frame are sold, that’s an indication of high risk appetite or market heating up. Then at the subsequent auction event, Sigma will be adjusted upward by 5%; If no more than 50% options within a certain time-frame event are sold, that’s an indicator of low risk appetite or market cooling down. Then at the subsequent auction event, Sigma will be adjusted downward by 5%.
- For protection of both the buyer and sellers, there is a lower bound and upper bound for Sigma, which we set it at 50% and 150% respectively. (We set this bound according to observation of Implied Volatility reflected on other option trading platforms like Deribit, Binance, Okex, Opyn etc.) Therefore, when market is superheating, options will not be priced at more than 150% volatility level;when market is supercooling, options will not be priced at less than 50% volatility level.

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