# Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing

arXiv: https://arxiv.org/abs/2208.12614 · HTML: https://mlsanity.com/paper/2208.12614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data](https://mlsanity.com/paper/1706.03161/) (2017)
- [Network Filtering for Big Data: Triangulated Maximally Filtered Graph](https://mlsanity.com/paper/1505.02445/) (2015)
- [Parsimonious modeling with Information Filtering Networks](https://mlsanity.com/paper/1602.07349/) (2016)
- [Learning Clique Forests](https://mlsanity.com/paper/1905.02266/) (2019)
- [Sparse causality network retrieval from short time series](https://mlsanity.com/paper/1706.01954/) (2017)
- [Forecasting market states](https://mlsanity.com/paper/1807.05836/) (2018)

## Abstract (arXiv)

The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM). Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we applied this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options at the popular trading platform Deribit. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion.