Annals of Financial Economics

This paper examines the forecasting power of daily infectious disease-related uncertainty in predicting the realized volatility of nine foreign exchange futures and the Bitcoin futures series using the heterogeneous autoregressive realized variance model. Our results indicate that the infectious diseases-related uncertainty index plays a crucial role in predicting the future path of foreign exchange and Bitcoin futures realized volatility in all the selected time intervals. These findings have important implications for portfolio managers and investors during periods of high levels of uncertainty associated with infectious diseases.

During the COVID-19 pandemic, Baker et al. (2020) [The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10, 742–758.] proposed the infectious disease equity market volatility (ID-EMV) index, which tracks US equity market volatility caused by infectious diseases. We extended the literature by using this newly developed ID-EMV index to examine its asymmetric effect on the share market returns of the G7 countries, which include the United Kingdom, Italy, Japan, Germany, France, Canada, and the United States of America. Moreover, we used novel techniques like the quantile-on-quantile regression test, quantile cointegration test, and quantile unit root test. The quantile cointegration test indicates that the infectious disease EMV index is cointegrated with G7 stock returns. Moreover, the quantile-on-quantile regression technique reveals that the infectious disease index positively affects stock returns during bullish states of the stock markets. In contrast, it negatively affects stock returns during bearish states of the stock market returns. The negative effect of the bearish states implies that investors may discourage investments during the downturns of the economy, whereas they need to boost their investments during economic booms.

In this research, the U.S. investor sentiment effect on cryptocurrency returns and volatility is examined by separating it into irrational and rational parts. According to the data, an unforeseen rise in the rational part of U.S. individual investor attitude influences cryptocurrency returns statistically and positively. In other words, rational sentiment can result in rising cryptocurrency returns. Additionally, a positive significant association exists between cryptocurrency volatility and the rational part of the individual U.S. investor sentiment. The findings confirm the hypothesis that the behavior of rational investors who utilize and study the impact of economic factors on asset prices reduces cryptocurrency volatility.

This research has focused on examining the connection between uncertainties in economic policies and exchange rates. This research extends the literature to this field by analyzing the impact of small to large negative along with small to large positive variations in the economic policy uncertainty on the currency rates. For this purpose, this research uses the Granger causality in the quantile test and a newly constructed multiple asymmetric threshold nonlinear ARDL (MATNARDL) model. When a nonlinear ARDL model is utilized, our results confirmed the nonlinear impact in three nations only. In contrast, when the MATNARDL technique is utilized, these findings do confirm the nonlinear effect for all nations. Furthermore, when the Granger causality in the quantile test is applied, the impact differs over various quantiles. In general, the enhanced framework encourages us to analyze better how EPU affects the exchange rate in the emerging seven (E7) nations. The findings of our research may be useful for state banks to design policies to make interventions in the foreign currency market.

This study aims to empirically investigate the impact of a set of macroeconomic variables including balance of trade, FDI, exchange rate, and inflation on the balance of payments (BOP) of Afghanistan using quarterly data from the second quarter of 2004 to the fourth quarter of 2020 (2004Q2 to 2020Q4). The paper uses the Vector Error Correction Model (VECM), and Johansen co-integration test for analysis to explore the BOP of Afghanistan and provides comparable literature to other least-developed and low-income developing countries. The findings reveal that balance of trade (BOT), foreign direct investment (FDI), and exchange rate are significant determinants of Afghanistan’s BOP in the long run. More specifically, BOT and FDI positively impact the BOP, whereas the effect of the exchange rate on the BOP is found negative. Yet, inflation has an insignificant impact on the BOP. Though all variables have an insignificant impact on the BOP in the short run, the relevant policy measures ought to consider improvement in BOT, promoting FDI, and exchange rate stability to ensure synchronized improved BOP and economic growth.

The intention of this article is to develop an instrument to overcome the limitations caused by traditional analyses and present a combined STR — Smooth Transition Regression model (EGARCH, STRIGARCH, and STR-FIEGARCH) to analyze the contagion effects of the 2008 financial crisis. The proposed instrument will aid the analysis of contagion and the impact of changes in long-term interest rates on the returns of international stock indices and forecasting, with special emphasis on the effects caused by structural breaks, persistence, and conditioned heteroscedasticity. The methodology begins with unit root tests with one and two structural breaks. In the second step, the asymmetry will be analyzed considering the STR models, which will determine the asymmetry relationship between interest rates and the long term, so that in a later step, these asymmetries will be used in the composition of a volatility estimation model, being based on the ARCH models: (i) EGARCH and (ii) FIEGARCH. This study provides a useful instrument based on modeling techniques to make the decision-making process more efficient and objective, providing a choice of instruments that assess the effect of changes in interest rates on stock market indices when influenced by falls, with structural data and better forecasting performance. The results show that the developed mixture models obtained better performance in predicting the effect or impact of changes in interest rates on stock market indices when influenced by structural breaks. STR and the ARCH family are useful instruments that make the decision-making process clearer and more objective when choosing instruments that assess the spillover effect of long-term interest rates on the profitability of international financial indices.