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PurposeThe paper provides new evidence for Bitcoin’s safe-haven property by examining the relationship between currency price, return and Bitcoin trading volume.Design/methodology/approachA unique dataset from a person-to-person (p2p) exchange is used to investigate association between Bitcoin trading volume and currency prices. Currency returns are used to identify local economic crises, the 8 crisis affected currencies are Venezuela Bolivar (VES), Iranian Rial (IRR), Ukrainian Hryvnia (UAH), Argentine Peso (ARS), Egyptian Pound (EGP), Nigerian Naira (NGN), Turkish Lira (TRY) and Kazakhstani Tenge (KZT).FindingsThe paper demonstrates that local economic crises are positively associated with increased Bitcoin trading. There is a negative association between trading volume and currency value (and return), suggesting low currency price and currency depreciation are accompanied with increased Bitcoin trading. The results not only hold for the crisis affected currencies but also currencies of advanced economies. Granger causality test also reinforces the negative association results.Originality/valueThe finding indicates some forms of flight-to-safety have occurred during local market crises when capital flight from domestic markets to Bitcoin, strengthening Bitcoin’s hedging asset status. However, total global trading volume declines after the start of the COVID pandemic, suggesting that Bitcoin is still regarded as a speculative asset. Overall, the findings show that Bitcoin is a hedging asset to protect against local currency depreciation, but not a safe-haven asset for the global crisis.

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This paper implements and compares eight American option valuation methods: binomial, trinomial, explicit finite difference, implicit finite difference and quadratic approximation methods. And three Monte Carlo methods: bundling technique of Tilley (1993), simulated tree (ST) of Broadie, Glasserman, and Jain (1997), and least square regression method (LSM) of Longstaff and Schwartz (2001). Methods are compared in terms of computation efficiency and price accuracy. The findings suggest that binomial is the best performing numerical method in terms of accuracy and efficiency. LSM beats the other two simulation methods in terms of efficiency, accuracy and number of discrete exercise opportunities.

In this paper, we develop a two-stage continuous time model of employee stock option (ESO) valuation under different tax regimes. We show that tax rules can have significant effects on ESO exercise behavior. In addition, we find that incentive stock options (ISO) are the optimal form of compensation for all levels of employees in the UK. In the US, restricted stock plans are preferred, and tax breaks offered by incentive schemes are only beneficial to employees with high liquid wealth (or small option holdings relative to wealth) or low risk aversion. We also analyze 83b elections for restricted stock plans in the US and find that making an election is a sub-optimal decision for both the employee and the firm.