A fine-tuned Chronos model for predicting stock market volatility. This model predicts quantiles (q10, q50, q90) of log-realized variance for a 20-day forward horizon using 60 days of historical squared returns.

This model is a fine-tuned version of amazon/chronos-t5-mini using LoRA (Low-Rank Adaptation) for volatility prediction on stock market data.

Model Architecture

Base Model: amazon/chronos-t5-mini
Task: Volatility Prediction (Quantile Regression)
Fine-tuning Method: LoRA (Low-Rank Adaptation)
LoRA Config: r=8, alpha=16, target_modules=["q", "k", "v", "o"]
Custom Heads:
- Value Embedding: Linear(1 -> hidden_dim) for embedding raw time series values
- Quantile Head: Linear(hidden_dim -> 3) for predicting q10, q50, q90 quantiles

Input Format

Input: Single-channel time series of squared returns (shape: batch_size, seq_length=60)
Sequence Length: 60 trading days
Preprocessing: Input should be squared log returns: (log(price_t / price_{t-1}))^2

Output Format

Output: Quantiles in log-variance space (shape: batch_size, 3)
- Column 0: q10 (10th percentile)
- Column 1: q50 (50th percentile / median)
- Column 2: q90 (90th percentile)

To convert to annualized volatility:

python

variance = np.exp(log_variance)
volatility = np.sqrt(variance) * np.sqrt(252)  # Annualized

Usage

Loading the Model

python

from src.models.chronos import ChronosVolatility
import torch

# Load model
model = ChronosVolatility(use_lora=True)
model.load_custom_heads("path/to/heads.pt")
model.base.load_adapter("path/to/adapter")  # For PEFT adapter

# Or use from_pretrained (if properly saved):
# from peft import PeftModel
# base_model = AutoModelForSeq2SeqLM.from_pretrained("amazon/chronos-t5-mini")
# model.base = PeftModel.from_pretrained(base_model, "chronos-volatility")
# model.load_custom_heads("path/to/heads.pt")

Making Predictions

python

import numpy as np

# Prepare input: squared returns sequence (60 days)
input_seq = torch.FloatTensor(squared_returns).unsqueeze(0)  # (1, 60)

# Get predictions
model.eval()
with torch.no_grad():
    quantiles_log_var = model(input_seq)  # (1, 3)

# Convert to volatility
quantiles_var = np.exp(quantiles_log_var.numpy())
quantiles_vol = np.sqrt(quantiles_var) * np.sqrt(252)  # Annualized %

print(f"10th percentile: {quantiles_vol[0][0]:.2f}%")
print(f"Median: {quantiles_vol[0][1]:.2f}%")
print(f"90th percentile: {quantiles_vol[0][2]:.2f}%")

Training Details

Trained data from AAPL, GOOG, MSFT, SPY, and TSLA
NVDA was used as a test case

Limitations

Trained on large-cap tech stocks, and will not generalize well to other markets or time periods.
Predictions are probabilistic (quantiles) and should be interpreted with uncertainty
The model requires at least 60 days of historical data for predictions

karkar69/chronos-volatility

chronos-volatility

Model Description

Model Architecture

Input Format

Output Format

Usage

Loading the Model

Making Predictions

Training Details

Limitations

karkar69/
chronos-volatility