In the last post I mentioned two models that I developed for translating volatility skew of a main, liquid ETF to volatility skew of a leveraged ETF. Even though these models are not in production (or even production quality, at this stage) I cannot disclose the formulas. However I wanted to show something, so I created these plots to illustrate them on a concrete example: fitting volatility data of SPY etf (january expiration, static snapshot of the data taken few days ago) and creating volatility skews for SSO, SH, SDS, and SPXU.
Market implied volatilities (mid) are in blue. Two models - "red" and "green", with maximum absolute error in vol points between model and market. Neither of the models performs excellent, but red model seems to be particularly bad. Models particularly disagree in the extreme tails, which is not really surprising. If I will have the time, in the following post I will compare results from my models to Zhang's non-parametric approach.
Market implied volatilities (mid) are in blue. Two models - "red" and "green", with maximum absolute error in vol points between model and market. Neither of the models performs excellent, but red model seems to be particularly bad. Models particularly disagree in the extreme tails, which is not really surprising. If I will have the time, in the following post I will compare results from my models to Zhang's non-parametric approach.
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