GARCH vs LSTM for Bitcoin Volatility Forecasting

# garch# lstm# bitcoin# volatilityforecastin
GARCH vs LSTM for Bitcoin Volatility ForecastingTildAlice

GARCH Won. I Didn't Expect That. I spent last weekend comparing GARCH and LSTM for Bitcoin...

GARCH Won. I Didn't Expect That.

I spent last weekend comparing GARCH and LSTM for Bitcoin volatility forecasting. The conventional wisdom says deep learning beats everything for time series these days. Turns out that's not quite right when you're predicting variance instead of price.

GARCH (Generalized Autoregressive Conditional Heteroskedasticity) outperformed LSTM on my Bitcoin dataset across every metric I tested. The gap wasn't close—GARCH's mean absolute error on realized volatility was 32% lower. For a model from 1986 to beat a neural network in 2026 feels counterintuitive, but the results are clear.

Let me show you why this happened and when you should actually use each approach.

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The Setup: Daily Bitcoin Returns

I pulled Bitcoin daily close prices from Yahoo Finance (2017-01-01 to 2025-12-31, ticker BTC-USD). The goal: predict tomorrow's volatility using the last 30 days of returns. I split the data 70/20/10 for train/validation/test.

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