harq_realised_vol.py

#!/usr/bin/env python3
"""
HARQ Model Implementation with Future Volatility Predictions
"""

from datetime import datetime, timedelta

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import yfinance as yf


class HARQModel:
    def __init__(self):
        self.params = None
        self.beta0 = None
        self.beta1 = None
        self.beta1q = None
        self.beta2 = None
        self.beta3 = None

    def compute_realized_volatility(self, returns):
        """Compute realized volatility"""
        return np.sqrt(np.sum(returns**2)) * np.sqrt(252)

    def compute_realized_quarticity(self, returns):
        """Compute realized quarticity"""
        return np.sum(returns**4) * (252**2)

    def prepare_features(self, rv_series):
        """Prepare HAR features"""
        features = pd.DataFrame(index=rv_series.index)

        # Daily (previous day) volatility
        features["daily_rv"] = rv_series.shift(1)

        # Weekly (previous 5 days) average volatility
        features["weekly_rv"] = rv_series.rolling(window=5).mean().shift(1)

        # Monthly (previous 22 days) average volatility
        features["monthly_rv"] = rv_series.rolling(window=22).mean().shift(1)

        return features.fillna(method="ffill")

    def fit(self, returns, rv_series):
        """Fit the HARQ model"""
        # Calculate realized quarticity
        rq_series = returns.rolling(window=22).apply(self.compute_realized_quarticity)

        # Prepare features
        features = self.prepare_features(rv_series)
        features["daily_rv_rq"] = features["daily_rv"] * rq_series

        # Remove NaN values
        features = features.dropna()
        y = rv_series[features.index]

        # Fit model using OLS
        X = features.values
        X = np.column_stack([np.ones(len(X)), X])
        betas = np.linalg.pinv(X.T @ X) @ X.T @ y

        # Store parameters
        self.beta0 = betas[0]
        self.beta1 = betas[1]
        self.beta1q = betas[2]
        self.beta2 = betas[3]
        self.beta3 = betas[4]

        self.params = {
            "beta0": self.beta0,
            "beta1": self.beta1,
            "beta1q": self.beta1q,
            "beta2": self.beta2,
            "beta3": self.beta3,
        }

        return features, y

    def forecast_n_days(self, returns, rv_series, n_days=5):
        """Forecast volatility for next n days"""
        if self.params is None:
            raise ValueError("Model must be fitted before forecasting")

        # Initialize forecasts
        forecasts = []

        # Get latest values
        latest_rv = rv_series.iloc[-1]
        latest_weekly = rv_series.iloc[-5:].mean()
        latest_monthly = rv_series.iloc[-22:].mean()
        latest_rq = self.compute_realized_quarticity(returns.iloc[-22:])

        # Generate forecasts
        for _ in range(n_days):
            forecast = (
                self.beta0
                + self.beta1 * latest_rv
                + self.beta1q * (latest_rv * latest_rq)
                + self.beta2 * latest_weekly
                + self.beta3 * latest_monthly
            )

            forecasts.append(forecast)

            # Update for next iteration
            latest_rv = forecast
            latest_weekly = (latest_weekly * 4 + forecast) / 5
            latest_monthly = (latest_monthly * 21 + forecast) / 22

        return forecasts


def main():
    # Download data
    symbol = "SPY"
    end_date = datetime.now()
    start_date = end_date - timedelta(days=365)

    df = yf.download(symbol, start=start_date, end=end_date)

    # Calculate daily returns
    df["returns"] = df["Adj Close"].pct_change()

    # Calculate realized volatility
    df["rv"] = df["returns"].rolling(window=22).std() * np.sqrt(252)

    # Initialize and fit model
    model = HARQModel()
    _, _ = model.fit(df["returns"], df["rv"])

    # Make predictions
    n_days = 5
    forecasts = model.forecast_n_days(df["returns"], df["rv"], n_days)

    # Generate forecast dates
    last_date = df.index[-1]
    forecast_dates = [
        (last_date + timedelta(days=i + 1)).strftime("%Y-%m-%d") for i in range(n_days)
    ]

    # Print results
    print(
        f"\nCurrent volatility ({last_date.strftime('%Y-%m-%d')}): {df['rv'].iloc[-1]:.1%}"
    )
    print("\nForecasted annualized volatility:")
    for date, forecast in zip(forecast_dates, forecasts):
        print(f"- {date}: {forecast:.1%}")

    # Plot results
    plt.figure(figsize=(12, 6))
    plt.plot(df.index[-60:], df["rv"].iloc[-60:], label="Historical Volatility")

    # Plot forecasts
    forecast_dates = [pd.to_datetime(date) for date in forecast_dates]
    plt.plot(forecast_dates, forecasts, "r--", label="Forecast")

    plt.title(f"{symbol} Volatility Forecast")
    plt.legend()
    plt.grid(True)
    plt.show()


if __name__ == "__main__":
    main()