Calculate the Corsi (2009) Heterogeneous Autoregressive Realized Volatility (HAR-RV) forecast of future daily Realized Variance, per asset.

Volatility clustering happens across multiple, overlapping time horizons at once. HAR-RV captures this cheaply – without the numerical optimization a GARCH-family fit requires (see get_garch) – by regressing future daily Realized Variance on trailing daily, weekly and monthly average Realized Variance components. The daily Realized Variance itself can be constructed in several ways via estimator: the simplest is the squared daily return, while the OHLC range-based estimators (see get_volatility, which exposes the same estimators via its own method parameter) use the daily (pre-period-aggregation) term behind each of those estimators instead, which is more statistically efficient since it uses the daily trading range rather than only the close-to-close move.

For more information about the method, see the following paper:

  • Corsi, F. (2009). “A Simple Approximate Long-Memory Model of Realized Volatility.” Journal of Financial Econometrics, 7(2), 174-196.

Also known as: HAR-RV model, Corsi’s HAR model, Heterogeneous Autoregressive model.

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Calculate the HAR-RV Forecast in Python

The HAR-RV Forecast is available in the Risk module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_har_rv_forecast as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_har_rv_forecast(estimator="squared_return").tail()

Which returns:

Date AAPL MSFT Benchmark
2022-12-23 0.0004 0.0002 0.0001
2022-12-27 0.0004 0.0002 0.0001
2022-12-28 0.0006 0.0003 0.0002
2022-12-29 0.0006 0.0006 0.0002
2022-12-30 NaN NaN NaN

The last row is NaN since there is no 2022-12-31 return yet to forecast against.

Parameters

get_har_rv_forecast accepts the following parameters:

  • estimator (str, optional): How to construct the daily Realized Variance input, one of “squared_return”, “parkinson”, “garman_klass” or “rogers_satchell”. Defaults to “squared_return”.
  • weekly_window (int, optional): The trailing window (in trading days) for the weekly RV component. Defaults to 5.
  • monthly_window (int, optional): The trailing window (in trading days) for the monthly RV component. Defaults to 22.
  • horizon (int, optional): The number of days ahead to forecast. Defaults to 1.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
  • growth (bool, optional): Whether to calculate the growth of the HAR-RV forecast values over time. Defaults to False.
  • lag (int | list[int], optional): The lag to use for the growth calculation. Defaults to 1.
  • standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
GJR-GARCH Volatility Model GJR-GARCH Volatility Forecast GJR-GARCH Parameters EGARCH Volatility Model EGARCH Volatility Forecast EGARCH Parameters Hill Estimator Best Fitting Copula Copula Parameters Copula Simulation Tail Dependence Coefficient Amihud Illiquidity

The Risk module page introduces the module, and the sidebar lists all of its functions.

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