In the early days of quantitative finance, a main assumption of volatility models was that volatility is driven by Brownian motion. Since 2014, thanks to Jim Gatheral, Thibault Jaisson, and Mathieu Rosenbaum, the idea of rough volatility has become commonplace. The main idea is that volatility is far more jittery, or rough, than regular Brownian motion.
This matters quite a lot, as the previous models didn’t fit empirically observed patterns well, like sharply steepening ATM skew for short-dated options. But as you know from my articles, just having a model fit well and calling it a day is not enough for us. We want to know WHY a certain model works well or doesn’t from an economic perspective. This reasoning is what allows us to develop truly robust models.
So in this article we will:
measure how much BTC volatility actually moves across different timescales and let the data tell us what kind of process it is,
ask what that process does to an option book, derive the short-dated skew it implies, and check the prediction against Deribit,
ask where the process comes from in the first place, build the mechanism up from three facts about how markets trade, and estimate it from Binance data.
I write about quantitative trading the way it’s actually practised:
Robust models and portfolios, combining signals and strategies, understanding the assumptions behind your models.
Topics I write about include portfolio construction, market making, risk management, research methodology, and more.
If this way of thinking resonates, you’ll probably like what I publish.
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