Recent Advancements in Section “Economics and Finance”

The section “Economics and Finance” brings together a collection of papers that cover a variety of topics both in the areas of economics and finance. In particular we will showcase some of the published papers in this section that best capture the quality and the importance of these contributions as they cover many research areas of interest such as cryptocurrencies, primary and secondary corporate bond markets, new methods for modeling financial transactions, emerging markets, the link between real estate and stock markets, hedging and realized volatility to name a few. Below we present the main findings of these papers as they seem to span a wide area of applications in financial economics.

More specifically, Kyriazis (2019) provides a systematic survey on return and volatility spillovers of cryptocurrencies based on the empirical results of relevant academic literature and finds that that Bitcoin is the most influential among digital coins, mainly as a transmitter towards digital currencies, but also as a receiver of spillovers from virtual currencies and alternative assets. Goldstein et al. (2019) look at the herding of investors as one major risk factor that is typically ignored in statistical approaches to portfolio modelling and risk management and they stress promising and novel approaches of modelling herding risk which merit empirical analysis. Men et al. (2019) propose a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. They develop a novel Markov chain Monte Carlo method (MCMC) for parameter estimation of the model and simulations demonstrate that the proposed TSCD model and MCMC method work well in terms of parameter estimation and duration forecasting. The proposed model and method are applied to two classic data sets that have been studied in the literature, namely IBM and Boeing transaction data. Pinto and Rastogi (2019) examine whether a firm’s dividends are influenced by the sector to which it belongs. They find that size, profitability, and interest coverage ratios have a significant positive relationship to dividend policy, while business risk and debt reveal a significant negative relationship with dividends. The results of this study can be used by financial managers and policymakers in order to make appropriate dividend decisions and they can also help investors make portfolio selection decisions based on sectoral dividend paying behavior. Liow et al. (2019) revisit the relationship between securitized real estate and local stock markets by focusing on their time-scale co-movement and contagion dynamics across five developed countries, using a wavelet-based method allowing for the relationship between the two asset markets to be time–frequency varying. Leistikow and Chen (2019) investigate whether the traditional futures hedge ratio (h_T) and the carry cost rate futures hedge ratio (h_c) vary in accordance with the Sercu and Wu (2000) and Leistikow et al. (2019) “h_c” theory and they find their results to be consistent with the theory. Finally, Eriksson et al. (2019) introduce a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model is semiparametric in the sense that the distributional and functional forms of its error component are partially unspecified and the statistical properties of the model are discussed. Extensive simulation studies were undertaken to validate the new method, the results of which suggest that it works reasonably well in finite samples. The out-of-sample forecasting performance of the proposed model was evaluated against a number of standard models, using data on S&P 500 monthly realized volatilities, concluding that the new model generally generates highly competitive forecasts relative to other methods.