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Miha Kralj
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lib/volatility/cv/Cv.md
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| **PineScript** | [cv.pine](cv.pine) |
- Conditional Volatility (CV) implements the GARCH(1,1) model for volatility forecasting, the most widely used time-varying volatility model in finan...
- Parameterized by `period` (default 20), `alpha` (default 0.2), `beta` (default 0.7).
- Output range: $\geq 0$.
- Requires `period + 1` bars of warmup before first valid output (IsHot = true).
- **Similar:** [HV](../hv/hv.md) | **Complementary:** Volatility analysis | **Trading note:** Coefficient of Variation; ratio of std dev to mean.
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
Conditional Volatility (CV) implements the GARCH(1,1) model for volatility forecasting, the most widely used time-varying volatility model in financial econometrics. Unlike simple historical volatility measures, GARCH captures two key empirical features of financial returns: volatility clustering (large moves tend to follow large moves) and mean reversion (volatility eventually returns to a long-run average). The output is annualized volatility expressed as a percentage.
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- Engle, R. F. (1982). "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation." *Econometrica*, 50(4), 987-1007.
- Bollerslev, T. (1986). "Generalized Autoregressive Conditional Heteroskedasticity." *Journal of Econometrics*, 31(3), 307-327.
- Engle, R. F. (2001). "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics." *Journal of Economic Perspectives*, 15(4), 157-168.
- Hansen, P. R., & Lunde, A. (2005). "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?" *Journal of Applied Econometrics*, 20(7), 873-889.
- Hansen, P. R., & Lunde, A. (2005). "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?" *Journal of Applied Econometrics*, 20(7), 873-889.