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PurposeThis study examine the response of liquidity of Bitcoin and Ethereum to the Russia-Ukraine war in an event study context and investigate whether the war had a transitory or a permanent effect on cryptocurrency liquidity.Design/methodology/approachA event study was applied to hourly transactions on Bitcoin and Ethereum cryptocurrencies from 1/02/2022 to 31/03/2022. This is period is subdivided in two sample periods to capture transitory and permanent effects. The transitory effect is investigated over a window spanning -20 and +20 days. For a more extended post-event period, a linear regression model was applied to analyze the effects of other factors on the liquidity risk of BTC and ETH.FindingsThe findings reveal a significant but temporary impact of the Russia–Ukraine war on the liquidity of Bitcoin and Ethereum. Liquidity levels have increased within the first two days around the event day and then returned to the pre-event level after that. However, the response of BTC and ETH cryptocurrencies' liquidities to the Russian invasion of Ukraine is not uniform.Originality/valueThis is the first paper that assesses the liquidity level of two major cryptocurrencies (Bitcoin and Ethereum) in response to an extreme event: the Russia–Ukraine war. The hypothesis is that trading in the cryptocurrency market will increase due to market participants' goal of evading regulatory sanctions. Furthermore, market participants may also take advantage of cryptocurrencies' popularity as safe-haven assets.

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In this study, the wavelet multiscale model is applied to selected assets to hedge time‐dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in‐sample and out‐of‐sample portfolio variances, the wavelet‐based generalized autoregressive conditional heteroskedasticity (GARCH) model produces the lowest variances. From a utility standpoint, wavelet networks combined with GARCH have the highest utility. Finally, the wavelet‐GARCH model has the lowest minimum capital risk requirements. Overall, the wavelet GARCH and wavelet networks offer improvements over traditional hedging models.

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Cross-country differences in the choice of an invoicing currency in international trade is one reason for cross-country differences in estimated exchange rate coefficients in short-run balance of trade equations. If exports are invoiced in domestic currency while imports are invoiced in a foreign currency, a depreciation will increase the domestic currency value of outstanding import contracts, and may cause the balance of trade to fall in the short run. Countries with different invoicing patterns will have different effects on the short-run trade balance following a depreciation. We explore a simple theory of invoicing currency choice, drawing inferences regarding the likely choices for 14 countries. This allows a classification of countries according to the expected short-run balance of trade effect of a currency depreciation. Empirical estimates support the hypothesized groupings based on suggested currency invoicing patterns. Copyright Kluwer Academic Publishers 1990exchange rates, international trade, invoicing practices,