To understand a predictive factors predictive power, we create a simple long/short strategy and simulate its past performance (with daily rebalancing):
The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.
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Predictive Strength
Implied Volatility (IV) is a metric derived from options that reflects market expectations of future price movement — is calculated by reverse-engineering options pricing models to determine the volatility level that justifies current market prices for derivatives.
High Implied Volatility: Traders anticipate larger price swings, and it is traditionally associated with prices falling.
Low Implied Volatility: Market participants expect relatively mild price fluctuations over the near term. This typically translates to lower options premiums, as traders price in a period of reduced uncertainty and risk.
A prolonged period of low IV might also suggest market complacency, where potential risks or future shocks are being underestimated. We can confirm this empirically.
IV reflects the market's collective expectation of future price turbulence, incorporating insights from sophisticated traders and institutions. Unlike historical volatility, which lags, IV prices in anticipated catalysts (e.g., regulatory shifts, macroeconomic events, or protocol upgrades) before they materialize in spot prices. For example, a sudden IV spike might signal insider positioning ahead of news, while suppressed IV could indicate complacency before a volatility surge.
IV tends to peak during panic sell-offs and trough during periods of greed, creating contrarian opportunities. Elevated IV often coincides with market bottoms (e.g., fear-driven put buying), while depressed IV may precede corrections as traders underestimate tail risks. This cyclical pattern mirrors traditional markets, where "buy when there's blood in the streets" strategies exploit fear premiums.
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Get started by validating the historical performance of the strategy with our transparent code snippets.
Copy and paste the code snippets below into your Python environment or download the files below.