Gate.io Research: The Application of Options Volatility Arbitrage Strategy in Cryptocurrency Market
In traditional financial markets, by building different models to simulate the market performance of financial products, many investors attempt to predict the future performance of these products in order to actualize bigger profits. As a new trading market, the cryptocurrency market has some similarities with traditional financial markets, which implies the price of cryptocurrencies could potentially also be predicted by conducting different mathematical models. To start with, this report will present an overview of what Options are, including their definition, sources of value, pricing methods, etc., followed by the emphasis of the introduction of volatility features and how arbitrage strategy works. Finally, ARCH and GARCH models will be built to make simulations and predictions on the volatility of historical BTC price. The results show that a volatility arbitrage strategy can also be applied to cryptocurrency markets.
Key Takeaways
- According to the conclusion, the higher the volatility in Options, the more potential value, which means the premium would also be higher. Consequently, Options would be more valuable, if the Options period would be longer, making the range of volatility wider.
- There are requirements that need to be met for the pricing model of Black–Scholes to work perfectly, generally for traditional Options trading. The model can also be applied to cryptocurrency Options trading. Furthermore, compared with traditional financial markets, the cryptocurrency market is more free and open, making the application of the Black–Scholes model more effective on cryptocurrency markets.
- Volatility is the only parameter that could not be obtained by direct observation with the Black–Scholes model. The change in volatility can greatly influence the value of Options. That is why the majority of professional Options traders try to predict changes in the Options market with the aid of volatility. Additionally, based on three major features of volatility, which are Serial Correlation, Mean Reversion and Momentum Effect, most traders can decide to buy or sell by predicting the changes in volatility.
- Gate.io Research built models exploring a logarithmic rate of return of daily BTC prices, conducted a series of verifications on the autocorrelation of BTC volatility, and constructed a GARCH model. With these, it is found that comparatively accurate
predictions on the trend of BTC ROI can be made, while predictions on the exact increase or decrease of ROI are not as effective, and predictions on the volatility of ROI are only applicable over the next 2 consecutive days.
The report will be uploaded soon. Please stay tuned!
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July 19, 2020
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