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lib/volatility/ccv/Ccv.md
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# CCV: Close-to-Close Volatility
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Volatility |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `method` (default 1) |
| **Outputs** | Single series (Ccv) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period + 1` bars |
### TL;DR
- Close-to-Close Volatility (CCV) calculates the annualized standard deviation of logarithmic returns using only closing prices.
- Parameterized by `period`, `method` (default 1).
- Output range: $\geq 0$.
- Requires `period + 1` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The simplest volatility measure is often the most robust—when all you have is closing prices, make the most of them."
Close-to-Close Volatility (CCV) calculates the annualized standard deviation of logarithmic returns using only closing prices. This is the foundational volatility measure in quantitative finance, serving as a benchmark against which more sophisticated estimators are compared. The implementation supports three smoothing methods (SMA, EMA, WMA) and annualizes using the standard √252 factor for daily data.
@@ -196,4 +213,4 @@ CCV is a standard volatility measure implemented consistently across platforms:
- Black, F., & Scholes, M. (1973). "The Pricing of Options and Corporate Liabilities." *Journal of Political Economy*.
- Parkinson, M. (1980). "The Extreme Value Method for Estimating the Variance of the Rate of Return." *Journal of Business*.
- Garman, M., & Klass, M. (1980). "On the Estimation of Security Price Volatilities from Historical Data." *Journal of Business*.
- Yang, D., & Zhang, Q. (2000). "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices." *Journal of Business*.
- Yang, D., & Zhang, Q. (2000). "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices." *Journal of Business*.