Quarterly Review of Economics and Finance最新文献

This study examines the lower and upper return spillovers and connectedness between important commodity (crude oil and gold) and main international stock markets using the quantile connectedness approach by Ando et al. (2018). The results show stronger return spillovers during bearish and bullish market conditions. Crude oil and gold are net receivers of return spillovers irrespective of quantiles. Furthermore, an intense spillover is observed during high stress periods including COVID-19 in March 2020, and global economic slowdown and the Brexit referendum of 2016, oil price crash in 2014–2016, Tapering Tantrum in 2013, and the subprime crisis of 2008. Moreover, the spillovers in lower quantiles are stronger than in upper quantiles. The volatility uncertainty index (VIX) has a negative impact on the connectedness index at low and median quantiles. Gold and economic policy uncertainty indexes have a positive impact on lower quantile spillovers. In contrast, oil uncertainty index has a negative impact on quantile spillovers. COVID-19 crisis has a positive impact on lower, medium, and upper spillovers.

This paper examines the efficiency of the market for non-fungible tokens (NFTs) against the backdrop of the market for fungible tokens (FTs) that includes Bitcoin and Ethereum. We focus on two important shocks: the outbreak of COVID-19 and the Russia-Ukraine conflict. To this end, we employ martingale difference sequence and conditional heteroscedasticity estimation techniques. We find that the efficiency of both markets fluctuates in time and the aforementioned shocks have a profound effect on FTs and NFTs. More specifically, we find that the effect of COVID-19 is heterogeneous for both markets, whereas that of the Russian invasion of Ukraine is homogenous for NFTs but heterogeneous for FTs.

This research explores plausible spillover effects among S&P 500 Index, stable coins, and selected cryptocurrency time series by examining observable lead and lag interrelationships among the series. Considering the heteroscedastic and “fat-tailed” nature of the data distributions underlying the empirical analyses, we employ quantile Granger Causality tests to improve the validity of our statistical findings. Our test results suggest that stable coins, USDT, and USDC offer diversification benefits by decreasing portfolio risk. The log returns of the S&P 500 Index, stable coins, Bitcoin, Ethereum, and Binance coins demonstrate clear bidirectional causality and spillover effects in low and high quantiles. Interestingly, however, stable coin and USDT returns strongly lead S&P 500 Index returns in nearly all quantiles for post COVID-19 time periods. These findings indirectly support intuition based upon market co-movement or integration assertions and suggest that investors can obtain added diversification benefits deriving from causality or spillover effects of holding stable coins when forming investment portfolios.

In this paper, we employ an option-pricing model that considers the effects of autocorrelation, economic policy uncertainty, and macroeconomic conditions to derive closed-form formulas of mortgage insurance (MI). When fitting the U.S. housing and mortgage data, our pricing model produces significantly well-fitting MI market quotes. Then, we design a framework to measure the magnitudes of these three effects on MI valuation. The autocorrelation effect dominates economic uncertainty and macroeconomic effects. On average, the effects of autocorrelation, economic uncertainty, and macroeconomic factors increase the MI premium rate by 65.173 bps, 14.616 bps, and 12.114 bps, respectively. During periods of heightened monetary policy uncertainty, the magnitude of the economic uncertainty effect is greater than that of the macroeconomic effects. Moreover, the magnitude of the economic uncertainty effect increases rapidly for a higher loan-to-value ratio (LTV), particularly when the LTV exceeds a threshold of 0.8.

Based on 35,344 news articles published in the Financial Times that cover 40 companies that have been included in the Dow Jones Industrial Average, we find that a negative media sentiment in the form of a negative language tone in news articles is a priced factor in five of nine asset-pricing models that aim to explain the cross-section of stock returns. In particular, the sentiment factor is a priced factor in the market model augmented with the sentiment factor in all three samples—the 2005–09 subsample, the 2010–18 subsample, and the 2005–18 full sample—and in the Fama-French three- and five-factor models augmented with the sentiment factor in the 2010–18 subsample. In addition, factor-spanning regressions with the Fama-French five-factor model as the right-hand-side model confirm that the sentiment factor contributes to the model’s explanation of the stocks’ mean excess returns in the 2005–09 subsample and the 2005–18 full sample.

The so-called ‘missing trader intra-community’ (MTIC) fraud causes enormous losses in value-added tax (VAT) revenue. The fraudsters take advantage of the zero-rated cross-border supplies within the European Union (EU) and resell the goods domestically without paying the received VAT to the tax authorities. One of the most prominent measures to combat this scheme is the optional reverse charge mechanism (RCM) that shifts the VAT liability from the supplier to the customer in business-to-business transactions. Using asymmetries in international trade (trade data gap, TDG), we identify the fraud-reducing effect of the RCM. For the observation period (2003 – 2019) within the EU, we quantify this effect in terms of the VAT revenue between 7.5 and 7.7 billion euros using a midpoint estimate. Additionally, we are the first to provide empirical evidence of a harmful fraud relocation from RCM countries to non-RCM countries. This explains the domino effect of RCM introductions in the EU and calls for a unified approach to VAT fraud.

Investigating the nexus between the stock market and the real economy is vital, as financial markets can play a critical role in economic growth. However, the country's risk factors (political, economic and financial) influence economic growth and financial markets’ nexus. This study examines the role of stock market development and country risk (political, economic and financial) on economic growth, and the moderating influence of country risk in the stock market and economic growth nexus in BRICS economies (except Russia) over the period of 2000 to 2020. The study employed various latest econometrics techniques, including Covariate Augmented Dickey-Fuller (CADF) for identifying unit root problem, Westerlund (2007) test for examining long-run relationship, Cross-sectional auto-distributive lag (CS-ARDL) for estimating long-run relationship, and finally, Konya (2006) for identifying the causal relationship among the variables. The study explored that financial risk, economic risk and political risk is negatively associated with economic growth. However, stock market does not play a significant role in economic growth in this case. The study highlighted a bi-directional causality between economic growth and stock market development. The study also suggests a unidirectional causality from political, economic and financial risk towards economic growth. Finally, the study suggests some policy recommendations based on the empirical results.

In this study, we use segregated endogenous and exogenous shocks to large banks’ returns to compare the effect of each on financial sector systemic risk. We use the copula-CoVaR methodology and GARCH (1,1) with time-varying moments to model the marginal distribution function and bivariate probability distribution of the tail returns. We find that endogenous risk dominates exogenous risk in the financial system. A comparison of the 2008 global financial crisis and COVID-19 reveals that the crisis aggravates only as exogenous shocks to the system persist. Additionally, we find that large banks reduce the total risk of the system in normal times but increase the risk of the financial system in crisis times. Our findings have important implications for policymakers, investors, and portfolio managers.

The significance of political stability as a fundamental political institution within a country is widely acknowledged. However, the intricate interplay between political stability and the development of financial institutions and markets remains inadequately explored. Through an analysis encompassing 123 countries spanning the temporal interval of 1980 to 2017, a discernible pattern emerges: heightened levels of political stability substantively foster the advancement of financial development. This positive association is pronounced in countries characterized by greater government effectiveness, those enacting financial liberalization reforms, and those exhibiting a more democratic political structure. The results are robust to alternative measures of political stability, controlling for country heterogeneity, a two-stage least squares technique using internally generated instruments to address endogeneity issues, among several other robustness tests. Our results emphasize that countries should prioritize the creation of a more politically stable environment, as it not only improves growth prospects, but also creates a climate of confidence for economic agents to actively engage in financial markets.

In this paper, we examine correlations between major European government bonds during the sovereign debt crisis. We apply an intraday Dynamic Conditional Correlation (DCC) model to the high-frequency quote data of the MTS market. We find that the Italian and Spanish government bonds become less correlated with other countries’ debts and the correlation between the two countries’ debts fluctuates heavily over time, ranging from 0.1 to 0.9. The Securities Markets Programme of the ECB is successful in restoring the market confidence for the integrity of the Eurozone, increasing the correlations towards the level before the crisis. In addition, we examine four different methods for computing and forecasting intraday VaR, namely, historical simulation, the Constant Conditional Correlation (CCC) model, the bivariate DCC model, and the multivariate DCC model estimated by composite likelihood. We demonstrate that the bivariate DCC model is most capable of forecasting intraday VaR for the tail of the distribution.