---
title: Forecasting Parameters in SABR Model
url: https://www.ml-quant.com/papers/repec/bba-j00001-v-1-y-2022-i-1-p-66-78-d-13/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bba:j00001:v:1:y:2022:i:1:p:66-78:d:13
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.anserpress.org%2Fjournal%2Fjea%2F1%2F1%2F6%2Fpdf%3Bh%3Drepec%3Abba%3Aj00001%3Av%3A1%3Ay%3A2022%3Ai%3A1%3Ap%3A66-78%3Ad%3A13
featured: 2023-12-06
citations: unknown
topic: Derivatives & Volatility
---


# Forecasting Parameters in SABR Model

Two methods for predicting parameters in the SABR model, the vector autoregressive moving-average model and epsilon-support vector regression, both provide accurate fits, with the SABR model yielding superior pricing results.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.anserpress.org%2Fjournal%2Fjea%2F1%2F1%2F6%2Fpdf%3Bh%3Drepec%3Abba%3Aj00001%3Av%3A1%3Ay%3A2022%3Ai%3A1%3Ap%3A66-78%3Ad%3A13
- Identifier: RePEc:bba:j00001:v:1:y:2022:i:1:p:66-78:d:13
- Released: 2022-02-26
- First featured: Quant Letter No. 28 (2023-12-06): https://www.ml-quant.com/issues/2023-12-06/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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