Journal of Risk and Financial Management

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them.

The coronavirus crisis has damaged the U.S. economy. This paper uses the stock returns of 125 sectors to investigate its impact. It decomposes returns into components driven by sector-specific factors and by macroeconomic factors. Idiosyncratic factors harmed industries such as airlines, aerospace, real estate, tourism, oil, brewers, retail apparel, and funerals. There are thus large swaths of the economy whose recovery depends not on the macroeconomic environment but on controlling the pandemic. Macroeconomic factors generated losses in industries such as production equipment, machinery, and electronic and electrical equipment. Thus, reviving capital goods spending requires not just an end to the pandemic but also a macroeconomic recovery.

The pandemic caused by COVID-19 has changed the mindset of many consumers. They are increasingly aware of the risks of not caring for the planet. Before the pandemic, there was a perceived increase in collective environmental concern and sustainability, but COVID-19 has further accelerated this process and motivated more people to assume this responsibility. Thus, the health crisis could trigger the consumption of organic foods, which are foods produced through environmentally friendly agricultural methods and that have not been artificially altered. It is essential for retailers to know how these consumers of organic foods behave in order to try to modify their strategies. In this context, the objective of this research is to analyze the relationship between attitude, satisfaction, trust, purchase and word-of-mouth (WOM) intentions towards organic products. The results of a survey administered a survey to a sample of 195 consumers show that trust is influenced by satisfaction and attitude. In relation to the behavioural variables, satisfaction is the variable that has the greatest influence on purchase intentions and WOM intentions. In addition, a moderating effect of environmental concern is observed on the proposed relationships.

Using individual level transaction data and a revised difference-in-differences method with nonparametric smoothing, we study the effect of COVID-19 on house prices. The analyses are performed on the areas of Houston, Santa Clara, Honolulu, Irvine, and Des Moines in the US, which vary in the economic features and the implementation of stay home orders. The results show that only Honolulu experienced noticeable house price declines from the outbreak, suggesting that a heavier reliance on service industries might be correlated with higher vulnerabilities. Santa Clara and Irvine lead the house price increase rates, followed by Des Moines and Houston, indicating that stronger housing market fundamentals, better amenities and less dependence on service industries are associated with more positive house price effects.

The green bond market develops rapidly and aims to contribute to climate mitigation and adaptation significantly. Green bonds as any asset are subject to transition climate risk, namely, regulatory risk. This paper investigates the impact of unexpected political events on the risk and returns of green bonds and their correlation with other assets. We apply a traditional and regression-based event study and find that events related to climate change policy impact green bonds indices. Green bonds indices anticipated the 2015 Paris Agreement on climate change as a favorable event, whereas the 2016 US Presidential Election had a significant negative impact. The negative impact of the US withdrawal from the Paris agreement is more prominent for municipal but not corporate green bonds. All three events also have a similar effect on green bonds performance in the long term. The results imply that, despite the benefits of issuing green bonds, there are substantial risks that are difficult to hedge. This additional risk to green bonds might cause a time-varying premium for green bonds found in previous literature.