Time-varying higher moments in Bitcoin

Vieira, Leonardo Ieracitano; Laurini, Márcio Poletti

doi:10.1007/s42521-022-00072-8

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…More recently, refs. [5,20] used GAS models to model Bitcoin returns and manage momentum-based portfolios. However, unlike our proposal, GAS models are suitable for including time-varying higher moments within a GARCH-like framework, while our proposal is designed for SV models.…”

Section: Sv Model With Time-varying Skewnessmentioning

confidence: 99%

Stochastic Volatility Models with Skewness Selection

Martins,

Freitas Lopes

2024

Entropy

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This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks’ mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.

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Section: Sv Model With Time-varying Skewnessmentioning

confidence: 99%