Real options and asymmetric volatility in light of the firm’s growth opportunities

Akron, Sagi; Demir, Ender; Taussig, Roi D.

doi:10.1080/10293523.2020.1755928

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Despite the stringent nature of these fundamental assumptions in the Black-Scholes model, many derivative platforms, including LedgerX, employ it for pricing options related to cryptocurrencies (Del Castillo, 2017, Akron, et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Evaluating Bitcoin Price Volatility Forecasting Models: A Comparative Analysis

CHINAZZO,

JELESKOVIC

2024

JORIT

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This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's important to emphasise the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme money or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.

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Section: Methodsmentioning

confidence: 99%