Impact of the Covid-19 induced panic on the Environmental,Social and Governance leaders equity volatility: A time-frequency analysis

We apply wavelet analyses to study how the Covid-19 fueled panic influenced the volatility of ESG (environmental, social and governance) leaders’ indices encompassing the World, the USA, Europe, China, and the Emerging Markets. We document intervals of the low, medium, and high coherence between the Coronavirus Panic Index and the price moves of the ESG Leaders indices. The low coherence intervals signify the diversification potential of ESG investments during a systemic pandemic such as Covid-19. We document differences in the pattern exhibited by various geographical indices highlighting their potential role for designing cross-geography hedge strategies, both now and in the future.     

Economic policy uncertainty and environmental governance company volatility: Evidence from China

This study primarily investigates whether China’s economic policy uncertainty (EPU) can predict the environmental governance index volatility, which selects companies regarding environmental protection such as sewage treatment, solid waste treatment, air treatment, and energy saving. Empirical results reveal that China’s EPU index can predict the environmental governance index volatility. Furthermore, even during periods of fluctuating volatility and the COVID-19 pandemic, China’s EPU index can reliably forecast the environmental governance index volatility. This paper tries to provide new evidence regarding the connection between EPU and environmental governance companies’ stock volatility.     

A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-Covid-19

This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era.     

Impact of stock market trading on currency market volatility spillovers

This research aims to detect the volatility linkages among various currencies during operating and non-operating hours of three major stock markets (Tokyo, London and New York) by employing bivariate VAR-BEKK-GARCH model in selected currency pairs. In particular, the aim is to analyze whether the major stock markets have a differential impact on volatility linkages in currency markets. The results indicate that volatility linkages in intraday are far stronger then in daily results. One remarkable result is that rather than major currencies, some minor and exotic currencies play a leading role in volatility transmission during trading hours of major stock markets.     

Financial market interdependencies: A quantile regression analysis of volatility spillover

This paper investigates the degree and structure of interdependence between emerging (Asian and Latin American) and developed (USA and Japan) stock markets through the study of volatility spillovers for the period spanning from January 1, 1993 to October 13, 2010. Using both standard GARCH model and quantile regression approach, we find the evidence of significant interdependence between financial markets which may give evidence of volatility transmission. The volatility transmission is closely associated with geographical proximity as well as with crisis periods which confirm the presence of contagion. The analysis of upper and lower quantiles allows observing that the interdependence increases during bullish markets while decreases during bearish markets. Accordingly, the structure of interdependence is asymmetric for both Asian and Latin American emerging markets. These findings open up new insights for government policy makers as well as for managerial purposes.     

Idiosyncratic return volatility and the role of firm fundamentals: A cross-country analysis

We investigate the relation between fundamental idiosyncratic volatility and stock returns idiosyncratic volatility using data from 56 countries. We find a strong positive relation between fundamental idiosyncratic volatility and idiosyncratic volatility of returns. This association, however, seems to be entirely concentrated in the developed economies, and we find no effect in the emerging markets. Specifically, fundamental idiosyncratic volatility does not lead to more idiosyncratic return volatility in countries with poor legal institutions and weak shareholder protection laws.     

Economic freedom and the stability of stock prices: A cross-country analysis

This paper investigates the link between economic freedom and the price stability of individual securities in a unique setting. Using a sample of 327 American Depositary Receipts (ADRs), we find an inverse relation between the economic freedom of a ADRs' home country and the price volatility of the ADR. This negative correlation is driven primarily by certain components of economic freedom, such as property right protection, the soundness of the money, and the level of free trade in the home country. Further, we find evidence that less regulation and less government control of markets in the home country leads to more stable ADR prices.     

Can financial innovation help to explain the reduced volatility of economic activity?

The stabilization of economic activity in the mid 1980s has received considerable attention. Research has focused primarily on the role played by milder economic shocks, improved inventory management, and better monetary policy. This paper explores another potential explanation: financial innovation. Examples of such innovation include developments in lending practices and loan markets that have enhanced the ability of households and firms to borrow and changes in government policy such as the demise of Regulation Q. We employ a variety of simple empirical techniques to identify links between the observed moderation in economic activity and the influence of financial innovation on consumer spending, housing investment, and business fixed investment. Our results suggest that financial innovation should be added to the list of likely contributors to the mid-1980s stabilization.     

Evaluation of volatility predictions in a VaR framework

The evaluation of volatility forecasts is not straightforward and some issues can arise. A standard approach relies on statistical loss functions. Another approach bases the evaluation of the volatility predictions on utility functions or Value at Risk (VaR) measures. This work aims to combine the two approaches, using the VaR measures within the loss functions. By means of this method, the VaR measures obtained from a set of competing models are plugged into two loss functions, the magnitude loss function and a proposed new one. This latter loss function more heavily penalizes the models with a number of VaR violations greater than the expected one. The loss function values are evaluated against a benchmark obtained from the inclusion of a consistent estimate of the VaR measures in the loss function. In order to investigate the performance of the proposed method and the new loss function, a Monte Carlo experiment and an empirical analysis of a stock listed on the New York Stock Exchange are provided. The proposed strategy helps with the selection of a superior model, in terms of forecast accuracy, when the cited approaches do not clearly and uniquely identify it. Moreover, the new asymmetric loss function allows a greater discrimination with regard to models, helping to find the best volatility model.     

Modeling interest rate volatility: A Realized GARCH approach

We propose using a Realized GARCH (RGARCH) model to estimate the daily volatility of the short-term interest rate in the euro–yen market. The model better fits the data and provides more accurate volatility forecasts by extracting additional information from realized measures. In addition, we propose using the ARMA–Realized GARCH (ARMA–RGARCH) model to capture the volatility clustering and the mean reversion effects of interest rate behavior. We find the ARMA–RGARCH model fits the data better than the simple RGARCH model does, but it does not provide superior volatility forecasts.     

The Eurozone banking sector in the time of COVID-19: Measuring volatility connectedness

This article investigates the volatility connectedness of the Eurozone banking system over the last 15 years (from 2005 to 2020). Applying the Diebold-Yilmaz Connectedness Index model to the daily stock return volatilities of 30 major Eurozone banks, we are able to measure the risk spillover effects and to capture the COVID-19 outbreak's impact on banking stability. The empirical findings show that the 30 banks are highly interconnected. Furthermore, we show the strong impact of the COVID-19 pandemic on the volatility dynamics, i.e., on the structure of the Eurozone banking system. Dynamically, we find that volatility connectedness increases during crises, reaching its maximum peak at the time of COVID-19. The analysis points out the critical role of volatility transmission played by large banks, highlighting the “too-big-to-fail” characteristic of this banking system. However, we find that small-medium banks are important actors of contagion, supporting the thesis that the Eurozone banking system is also “too-interconnected to fail.” Finally, we document the heterogeneity effect of the COVID-19 pandemic between Eurozone banking systems. This heterogeneity impact could be a future source of financial instability within the Eurozone.     

测量中国的金融不确定性——基于大数据的方法

本文基于 Jurado et al.(2015)提出的大数据分析方法,采用280个月度经济金融变量构造了2002-2017的中国金融不确定性指数,并从股票市场波动和金融机构系统性风险两个方面对中国的金融不确定性指数进行了实证分析。本文发现,在控制了滞后波动率后,金融不确定性指数仍然对股票市场的波动率有显著的预测作用;同时,金融不确定性的增加会显著提升金融机构的系统性风险,尤其是规模较大的金融机构。实证结果表明,金融不确定性是金融市场波动的一个重要来源。     

Impact of macroeconomic announcements on implied volatility slope of SPX options and VIX

This paper examines the impact of macroeconomic announcements on the high-frequency behavior of the observed implied volatility skew of S&P 500 index options and VIX. We document that macroeconomic announcements affect VIX significantly and slope at a lesser extent. We also find evidence that good and bad announcements significantly and asymmetrically change implied volatility slope and VIX.     

管理浮动汇率制背景下人民币名义汇率波动分析

本文运用描述统计方法比较了1994年1月至1997年12月、2005年7月至今管理浮动汇率制背景下两个不同阶段内人民币/美元汇率波动的情况,而后结合中国实际、采用自回归分布滞后方法建模,分析了2005年汇改新政之后影响汇率波动变化的因素。     

Cash cycle: A cross-country analysis

We study the cross-country differences in the cash cycles of companies and find a negative relation between a country's development and the cash cycles of its corporations. The ability of companies to obtain raw materials on credit and to better manage inventory plays significant roles in shortening the cash cycle. Various country-specific factors affect cash cycles. Firms with shorter cash cycles invest more in R&D and participate in more acquisitions. They also have a higher valuation and lower leverage. Overall, our findings indicate a close relation between a company's working capital management, its valuation, and the country's level of development.     

Impact of central bank independence and transparency on international equity portfolio allocation: A cross-country analysis

In this study, we analyse the effects of central bank independence (CBI) and central bank transparency (CBT) and their interactions with institutional quality on foreign equity portfolio inflows. Employing a dataset from 42 countries over the period from 2001 to 2014, we find strong evidence that independent and transparent central bank has a positive and significant influence on foreign equity investment inflows. Further analysis shows that institutional quality interacts with central bank independence and transparency in attracting foreign equity portfolio. Our results are robust to alternative specifications, endogeneity concerns and that economic policy uncertainty increases asymmetric information and deters foreign equity portfolio investment inflows.     

Day of the week effect on foreign exchange market volatility: Evidence from Turkey

This paper assesses the day of the week effect of the daily depreciation of the Turkish lira (TL) against the US dollar (USD) and its volatility. The empirical evidence from Turkey presented here suggests that Thursdays are associated with higher and Mondays with lower depreciation rates compared to those of Wednesdays. Moreover, Mondays and Tuesdays are associated with higher volatility than Wednesdays.     

Responses of REITs index and commercial property prices to economic uncertainties: A VAR analysis

We use market participants’ perceived uncertainty to investigate the response of real estate investment trusts index (REITs Index) and commercial property prices to shocks in economic uncertainty. Using US quarterly data and applying a vector autoregression (VAR) model, our results show that an increase in market participants’ perceived uncertainty leads to a significant drop in the REITs Index and commercial property prices. In addition, we show that the REITs Index responds quicker to the uncertainty shocks than the commercial property prices. Our findings provide important implications for investors.     

A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering

We introduce a new factor model for log volatilities that considers contributions, and performs dimensionality reduction, at a global level through the market, and at a local level through clusters and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree algorithm to fix the number of factors. We use the factor model to study how the log volatility contributes to volatility clustering, quantifying the strength of the volatility clustering using a new nonparametric integrated proxy. Indeed finding a link between volatility and volatility clustering, we find that a global analysis reveals that only the market contributes to the volatility clustering. A local analysis reveals that for some clusters, the cluster itself contributes statistically to the volatility clustering effect. This is significantly advantageous over other factor models, since it offers a way of selecting factors in a statistical way, whilst also keeping economically relevant factors. Finally, we show that the log volatility factor model explains a similar amount of memory to a principal components analysis factor model and an exploratory factor model.