**Authors:** Deep [[ML]] (Medium article)
**Published:** September 13, 2024
**Link:** [Medium](https://medium.com/@deepml1818/python-for-regime-switching-models-in-quantitative-finance-c54d2710f71b)
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### **What is the Article About?**
This article explains how to use a **Markov Regime-Switching Model** to identify different market regimes (e.g., high vs. low volatility) in financial data. It walks through the theory and provides Python code to build and visualize a two-regime model using **S&P 500 returns**.
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### **Key Points:**
- **What is a Regime-Switching Model?**
- It assumes financial markets switch between **hidden states** (regimes) like bull/bear or high/low volatility.
- The switches follow a **Markov process**, meaning the probability of a new regime depends only on the current one.
- **Model Used:**
- **MarkovRegression** from statsmodels.tsa.regime_switching with k_regimes=2.
- States represent **high volatility** and **low volatility**.
- Allows **switching variance** between regimes.
- **Data Source:**
- S&P 500 daily closing prices from Yahoo Finance using the yfinance API.
- Returns computed as **log(price / previous price)**.
- **Outputs:**
- Estimated **means and variances** for each regime.
- **Transition probabilities** between regimes.
- **Smoothed probabilities** showing the likelihood of being in each regime over time.
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### **Example Application:**
- **Low-Volatility Regime:** Use **mean-reversion** or conservative strategies.
- **High-Volatility Regime:** Apply **trend-following** or **momentum** strategies.
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### **Code Summary:**
```python
from statsmodels.tsa.regime_switching.markov_regression import MarkovRegression
# Fit the regime-switching model
model = MarkovRegression(data['Returns'], k_regimes=2, switching_variance=True)
results = model.fit()
# Display summary
print(results.summary())
# Plot regime probabilities
results.smoothed_marginal_probabilities[0] # Low volatility
results.smoothed_marginal_probabilities[1] # High volatility
```
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### **Strengths:**
- Detects **structural shifts** in market behavior.
- Aids in **risk management** by adjusting exposure based on regime.
- Can **backtest strategies** based on regime classification.
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### **Limitations:**
- Assumes regime-switching follows a **Markov property** (no memory).
- Limited to **two regimes** unless extended.
- Dependent on **quality of input data** and correct parameter tuning.