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The extraordinarily fast-paced growth in government spending during Covid 19 highlights the need for effective measures to ensure fiscal transparency during emergencies. Governments must improve emergency fiscal transparency by disclosing more detailed information on the types and conditions of funding sources, extra- and off-budget revenue sources, and recipients and impacts of spending. Institutional procedures should be established to safeguard fiscal transparency during emergencies, and to increase the effective involvement of legislatures, auditing agencies, and citizens in the management of fiscal rescue packages. This article revisits the concept of fiscal transparency in an emergency context. Examining the fiscal reporting practices of 120 national governments during Covid 19, as compiled by the International Budget Partnership, the authors found under-reporting of critical information on external funding sources; limited reporting on spending levels and impacts on vulnerable groups and areas; and lack of sufficient institutional safeguards to ensure fiscal transparency. They discuss the theoretical implications of their findings and provide policy recommendations for improvement. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of different forecasting formulas with fractional Brownian motion based on discrete and finite samples. Existing literature presents two formulas for generating optimal forecasts when continuous records are available. One formula relies on a history over an infinite past, while the other is designed for a record limited to a finite past. In reality, only observations at discrete time points over a finite past are available. In this case, the forecasting formula, which has been widely used in the literature, is the one obtained by Gatheral et al. (Volatility is rough. Quant. Finance, 2018, 18(6), 933–949) that truncates and discretizes the formula based on continuous records over an infinite past. The present paper advocates an alternative forecasting formula, which is the conditional expectation based on finite past discrete-time observations. The findings suggest that the conditional expectation approach produces more accurate forecasts than the existing method, as demonstrated by both simulated data and actual daily realized volatility (RV) observations. Moreover, we also provide empirical evidence showing that the conditional expectation approach can lead to larger economic values than the existing method. [ABSTRACT FROM AUTHOR]