  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.
