We examine how the implied volatility in the US financial market has been affected by the COVID-19 pandemic. We decompose the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) into two implied volatility conditions (i.e., low and high), and COVID-19 pandemic cases and deaths into two categories (i.e., low and high). Our novel quantile-on-quantile regression approach allows us to better examine the dynamic relationship between the COVID-19 pandemic and implied volatility. Our empirical results show that increased death rates tend to increase fear in the US financial market. Specifically. we find that high COVID-19 cases have a significant impact on implied volatility under high uncertainty conditions, but low COVID-19 cases appear to have no impact on implied volatility in the US market. Our findings offer support to the US policy response by the Federal Reserve Board and the government to limit the instability effect of the COVID-19 shock on the financial markets.

In this paper, we analyze the impact of the ongoing COVID-19 pandemic on the information flow among the main cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and those of the fear index (VIX), Gold price, and the US equity market (S&P500). We use the transfer entropy measure to determine the information flow by allowing for nonlinear dynamics and extreme tail values in the series. Our results indicate that information flow and sharing have changed during the COVID-19 pandemic with the following main findings: i) cryptocurrencies show more correlation with VIX, Gold, and the US equity markets during the COVID-19 period; ii) Gold and VIX maintain their position as safe hedging tools against the pandemic; iii) during COVID-19, S&P500 is the dominant flow transmitter to the four cryptocurrencies, and iv) Ripple plays the dominant role of information flow to VIX, Gold, and S&P500.

This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.

By using a large international firm-level data set this paper aims to make a contribution in better understanding how COVID-19 related stringency and economic support measures actually affected the corporate sector. Our most important findings can be summarized as follows: First, we find robust evidence that stringency measures had a statistically and economically significant positive impact on listed firms. Second, with respect to the effects of economic support measures the evidence seems, at best, to be weakly in favor of a positive impact. Third, small and employment intensive companies profited most from economic support measures. Fourth, also highly leveraged or even Zombie firms profited more from these support measures than others. Overall, the results are in line with official policies aimed at sheltering SMEs and human capital intensive firms from the Corona shock waves. However, it also seems that governments unintentionally supported firms in financial difficulties or with non-viable business models already before the pandemic.

The massive contagion of new coronavirus (Covid-19) has disrupted many businesses across the European Union. This has resulted in an immense drag on the revenues and cash flows that may lead to a significant increase in corporate bankruptcies. In this paper, we investigate the impact of Covid-19 on the solvency profile of the firms in the EU member states. We introduce multiple stress scenarios on the non-financial listed firms and report a progressive increase in the probability of default, an increase of debt payback, and declining coverages. Our results indicate that the solvency profile of all firms deteriorates. The manufacturing, mining, and retail sector are most vulnerable to a decline in market capitalization and a reduction in sales revenues. The paper also examines the possible policy interventions to sustain solvency at a pre Covid-19 level. Our findings suggest that for a moderate deterioration in economic conditions, a tax deferral is sufficient. However, in the event of exacerbating business shocks, there should be hybrid support through debt and equity to avoid a meltdown. This study has important implications for policymakers, corporate managers, and creditors.

In this paper, we exploit the natural experiment of the COVID-19 outbreak and investigate the role of collaborative integration and workplace flexibility in scholarly productivity. Using data on the quantity and quality of the journal and working paper submissions, we first identify a discontinuity pattern in the productivity of Chinese scholars around the Chinese New Year (CNY). Second, we find that COVID-19 has a negative impact on the productivity of Chinese scholars in terms of quantity and quality post-CNY. Furthermore, the short-term detrimental effect on scholarly productivity is induced mainly through the channel of collaborative integration and workplace flexibility due to mitigation policy shocks in terms of social distancing and working-from-home arrangements. The results suggest while advances in virtual communication technologies can facilitate productivity by lowering collaboration costs, virtual team communication cannot be a perfect substitute for face-to-face communication in collaborative integration. In addition, higher workplace flexibility might hinder productivity in sectors relying more on the skills of self-management and discipline.

We investigate a novel dataset of more than half a million 15 seconds transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics discussed in US TV news. We find that several topics identified by the LDA predict significant and economically meaningful market reactions in the next day, even after controlling for the general TV tone derived from a field-specific COVID-19 tone dictionary. Our results suggest that COVID-19 related TV content had nonnegligible effects on financial markets during the pandemic.

This paper analyses the directional spillover effects and connectedness for both return and volatility of nine US dollar exchange rates of globally most traded currencies under the influence of trade policy uncertainty. We find two interesting results over the study period ranging from December 1993 to July 2019. First, there exists asymmetric spillovers and connectedness among the considered exchange rates when trade policy uncertainty is present. Second, the volatility spillover is stronger than the return connectedness between exchange rate and trade policy uncertainty. These findings are robust to the presence of economic policy uncertainty effects. Concomitantly, the trade policy uncertainty patterns are also found to be useful for predicting currency market dynamics. Our findings contribute to the debate on the impact of trade policy uncertainty on the global economy and financial sector.

This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period (up to February 2021). We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.

In this study, I constitute a search based COVID-19 sentiment index using Google search volume. I develop an alternative Scared COVID-19 Attitude Revealed by Eager Search (SCARES) index using the household search volume i.e. "coronavirus pandemic", "coronavirus epidemic", and "coronavirus outbreak" of United States (US) during the COVID-19 pandemic. Using daily data from May 1, 2020 to July 30, 2021, I find that SCARES index negatively explains stock market return and subsequent return reversals, implying that households' increased pandemic sentiment negatively affects equity market return. Furthermore, decile regressions on characteristics-sorted portfolio returns show that SCARES index predicts the return reversals of firms that are small, less profitable, and with low investment. I also report that COVID-19 search shocks of households do not significantly predict any of the Fama-French five-factors except SMB (small-minus-big). Moreover, I use two state Markov switching model and find that structural breaks associated with pandemic phases make SCARES positively related to indices i.e. twitter based uncertainty, volatility index, economic policy uncertainty, and business condition in high volatility regime. Finally, sub-period analysis reports that, in stock market context, people start to react slowly and become relatively less responsive to the COVID-19 search keywords. The findings of this paper can assist key stakeholders in the market to carefully analyze the asset return pattern during pandemic regimes.

This paper investigates the potential hedging and safe-haven properties of several alternative investment assets, including gold, Bitcoin, oil, and the oil price volatility index (OVX), against the risks of the Saudi stock market and its constituent sectors in different phases of the COVID-19 pandemic. Using daily data, we employ the bivariate dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) technique to model volatilities and conditional correlations. Our findings show that all investigated alternative investment assets had a time-varying hedging role in the Saudi stock market, which became expensive during the early stages of the COVID-19 pandemic. Our results also show that the optimal weights for gold were substantially higher than those of other assets, reaching a peak during the pandemic, implying that investors consider gold a flight-to-safety asset. Additionally, we find that gold and OVX were strong hedges and could have served as weak safe havens for investors during the early stages of the COVID-19 pandemic, while the remaining assets generally lacked these properties and could be merely used as diversifiers. Our empirical findings offer several key implications for policymakers and portfolio managers in Saudi Arabia that may be applicable to similar markets. In particular, we show that OVX-based products can serve as a promising hedging asset for stock markets in oil-exporting countries.

The current global COVID-19 pandemic is adversely affecting financial markets, including commodities, conventional stocks, and Islamic stocks. This paper empirically investigates the extent to which COVID-19 effects may drive interdependence in markets. We fit copulas to pairs of returns before and during the ongoing epidemic shock, analyze the observed changes in the dependence structure, and discuss asymmetries on the propagation of crisis. We also use the findings to construct portfolios possessing desirable expected behavior. We find that the dependence structure changes significantly during the global pandemic providing valuable information on how the COVID-19 crisis affects inter-dependencies. The selected portfolio, including gold and Islamic return indices, has the best performance outside the COVID-19 crisis, and slightly more performing during the bear markets validating gold's intrinsic characteristic to be a safe haven. However, the portfolio performances, when combining the Brent with Islamic or conventional indices, have the same trend for the whole period. Our findings contribute to help investors better adjust their investment strategies.

We contribute to the literature on financial networks by presenting empirical evidence that the global shock of the COVID-19 pandemic caused changes in the forms and intensity of banking sector connections between different countries. These changes include providing the highest level of connectivity observed in the timeline initiated in 2005. We used a comprehensive set of information containing data from 35 countries (developed and emerging economies) and showed the change in the classification of transmitting and receiving spillover during the COVID-19 crisis. Our results provide relevant insights into systemic integration between countries' banking markets, especially during difficult times. Our results are significant to Central Banks, banking sector investors, and governments seeking assistance from banks in the solutions for the resumption of the economy in the face of the COVID-19 shock.

The purpose of this study is to determine the value-based price of a COVID-19 vaccine from a societal perspective in Germany.

A shutdown of businesses enacted during the SARS-CoV-2 pandemic can serve different goals, e.g., preventing the intensive care unit (ICU) capacity from being overwhelmed ('flattening the curve') or keeping the reproduction number substantially below one ('squashing the curve'). The aim of this study was to determine the clinical and economic value of a shutdown that is successful in 'flattening' or 'squashing the curve' in Germany.

The COVID-19 pandemic has caused an unprecedented human and health crisis. The measures taken to contain the damage caused a global economic slowdown. Investors face liquidity pressures resulting from the general downturn in the financial markets, and might change their risk appetite. This paper reassesses the safe haven property of gold as a traditional asset, and Bitcoin which is gradually imposing itself as a new class of asset with unique characteristics. The empirical results, applied on major world stock market indices and currencies, and based on the multivariate asymmetric dynamic conditional correlation model, show the effectiveness of Bitcoin and gold as hedging assets in reducing the risk of international portfolios. Moreover, the analysis provides evidence that during the COVID-19 pandemic, gold is a weak safe haven for the considered assets, while Bitcoin cannot provide shelter due to its increased variability.

The Federal Reserve is exposed to a greater degree of political influence under its new operating regime. We survey the relevant literature and describe the Fed's new operating regime. Then we explain how the regime change reduced de facto central bank independence. In brief, the regime change increased the appointment power of the President and improved the bargaining power of Congress. We offer some suggestions for bolstering de facto independence at the Fed.

Health care expenditures have been increasing sharply in the last ten years, with spending on mental health disorders being particularly prominent. Over the same time period, a number of new antipsychotic medications have been added to the armamentarium for treatment of persons diagnosed with schizophrenia. Due in part to the sharply increased expenditures by Medicaid on mental health disorders such as schizophrenia, controversies have arisen as to the use of these more costly innovative medications, particularly their impact on the annualized cost of treating patients.Using Medicaid data on 12,864 person years from two counties in Florida over the 1994-95 to 1999-2000 time period, in this study we address three issues: (i) On a per person year basis, what is happening over time to the mental health-related costs of treating schizophrenia? (ii) How is the composition and quality of care changing over time? and (iii) Holding quality of care constant, on a per person year basis, by how much are the costs for the ongoing treatment of schizophrenia changing?We find that unadjusted for changes in quality of care over time, the annualized costs for the ongoing treatment of schizophrenia per person have increased about 0.5% per year. The composition of treatments for schizophrenia has changed substantially over this six-year time period, toward more intensive use of atypical antipsychotics, and away from psychosocial treatments. Holding treatment quality type and patient characteristics constant over time, mean treatment costs have fallen about 5.5% per year between 1994-1995 and 1999-2000.

Health Maintenance Organizations (HMOs) have emerged as a major vehicle to reduce transaction costs associated with defining the limits of health insurance coverage and to provide appropriate provider incentives. This article explains the heterogeneous set of incentives used by HMOs to reimburse providers and performs empirical tests of their effectiveness. The empirical analyses reveal that utilization of health care services is reduced when (1) physician compensation is based on salary or capitation arrangements rather than some measure of output; (2) bonuses and paybacks are based on individual rather than group performance; and (3) when the HMO operates as a proprietary (for-profit) organization. Utilization is not significantly affected by incentives placed on the hospital. Finally, physician ownership of the HMO was found to lead to higher levels of utilization.