This article examines the sensitivity of U.S. sector equity indices to changes in nominal interest rates and in the corresponding principal components (level, slope and curvature of the U.S. yield curve) over the period 1990–2013 using factor models and a nonlinear autoregressive distributed lag (N.A.R.D.L.) approach. Furthermore, for robustness, this research analyses whether the sensitivity of sector stock returns is different depending on the stage of the economy, splitting the whole sample period into two sub-periods: pre-crisis and subprime crisis. In general, the empirical results confirm a substantial exposure to interest rate risk that depends on the model used and the period analysed. In addition, considering the three principal components of the U.S. yield curve, the sensitivity to changes in these components tends to be stronger during the subprime crisis sub-period. Finally, in the N.A.R.D.L. context, about 50% of sectors show long-run relations between sector stock returns and the explanatory factors, mainly during the whole sample and the pre-crisis sub-period. Nevertheless, short-run responses may be mostly shown in the subprime crisis sub-period. Therefore, our results evidence that nominal interest rates and its three components would have asymmetric effects on the U.S. stock returns at sector level, depending on the stage of the economy.

This paper presents the results of a bibliometric analysis concerning the application of blockchain technology to logistics. It aims to discern the relational structure among past and current topics, predict emerging trends, and provide a longitudinal perspective on this research domain. Using co-word analysis techniques, we employ SciMAT software to analyze over 800 papers from 2016 to March 2023. The investigation enhances the understanding of blockchain technology, revealing that scientific production in this area revolves around eight primary thematic clusters. Visual representations of the results are provided through longitudinal maps and strategic diagrams, illustrating pivotal themes across the three study periods: blockchain and sustainability. Smart contracts emerge as a focal topic in the final period under consideration.

Research background: The study examines the impact of geopolitical tensions, in particular the Russia-Ukraine conflict, on agricultural commodity markets at a global level. The research focuses on the period from February 2021 to February 2024, a period characterized by significant economic instability due to the ongoing conflict. The research covers global agricultural commodity markets, focusing on three main categories: soft commodities, grains and livestock. Purpose of the article: The purpose of this paper is to examine the asymmetric interactions between crude oil and gas prices and agricultural commodity yields from February 2021 to February 2024. The study aims to analyze these interactions on a global scale, encompassing the world markets for soft commodities, grains and livestock. The research aims to provide insights into how geopolitical tensions, in particular the Russia-Ukraine conflict, are affecting these global agricultural markets and their linkages to energy prices. Methods: This study uses the Nonlinear Autoregressive Distributed Lag (NARDL) model to analyze the asymmetric interactions between crude oil and gas prices and agricultural commodity yields, capturing both short-run and long-run asymmetries. The study divides the sample period into three distinct sub-periods of the Russia-Ukraine conflict, allowing for a detailed examination of how energy price fluctuations affect agricultural commodities under different economic conditions. Findings & value added: Our main findings are the following: (1) positive correlations with oil and gas prices for soft commodities and grains; (2) weaker but significant relationship for livestock; (3) short-term asymmetries are particularly pronounced during periods of high economic turbulence (e.g. Russia-Ukraine conflict); (4) grain and livestock yields show stronger responses to negative oil price shocks; (5) no long-run equilibrium relationship found by cointegration tests. The present paper is unique in combining the Nonlinear Autoregressive Distributed Lag (NARDL) model with a detailed analysis of the Russia-Ukraine conflict, providing unprecedented insights into the asymmetric impact of geopolitical tensions on agricultural commodity markets, which is essential for understanding market dynamics during crises.

This paper focuses on analysing the sensitivity and behaviour of some of the leading insurers currently operating in the Euro area to changes in benchmark interest rates. The methodology used is the Quantile Regression (QR) approach and the period analysed covers from 2003 to 2015 around the recent global financial crisis. The empirical results show that European insurance market returns have a statistically significant sensitivity to interest rate variations and that there are important differences according to the period analysed, being the sensitivity most pronounced in extreme market conditions.

This paper examines the behavior of the interest rate risk management measures for bonds with embedded options and studies factors it depends on. The contingent option exercise implies that both the pricing and the risk management of bonds requires modelling future interest rates. We use the Ho and Lee (HL) and Black, Derman, and Toy (BDT) consistent interest rate models. In addition, specific interest rate measures that consider the contingent cash-flow structure of these coupon-bearing bonds must be computed. In our empirical analysis, we obtained evidence that effective duration and effective convexity depend primarily on the level of the forward interest rate and volatility. In addition, the higher the interest rate change and the lower the volatility, the greater the differences in pricing of these bonds when using the HL or BDT models.