Spread Curves Research Articles

Credit default swaps and corporate carbon emissions in Japan

We examine the relationship between carbon emissions and the market perception of firms’ default risk, measured by corporate credit default swap (CDS) spreads in Japan. While corporate revenue size is the most significant factor of carbon emissions, pressure from investors has a significant decreasing effect on carbon emissions, which is greater for investment-grade companies. We find that carbon emissions have time-varying effects on corporate CDS spreads, which supports the “investor awareness” hypothesis across sectors and credit quality. The sectoral impacts indicate that carbon emissions are priced prominently in the CDS spreads of firms in sectors where the transition to carbon-free energy sources appears to be relatively less complicated and less expensive. Finally, we report the impacts of carbon emissions on the CDS spread curve, where they are priced in both short- and long-term CDS spreads, and high carbon emissions steepen the CDS spread curve.

A unified constraint-based approach for plan and goal recognition from unreliable observations

Given a sequence of observations over a plan execution, plan and goal recognition are considered as interchangeable tasks in AI planning. However, strictly speaking, the former tries to identify a plan, and the latter a set of goals, that explain the observations. Both recognition tasks are data-driven, where data comprises the plan observations, and are specially useful in proactive systems. Depending on the source of knowledge about the agents under observation, these tasks are traditionally solved by two different approaches, which require a large plan library or a planning model. In between these approaches, we propose a unified novel constraint-based approach, which distinguishes between the two tasks but is valid for both. We present a formulation, based on Partial Order Causal Link planning, that is compiled from a small plan library, to approximate a model that learns the essential causality of the original planning model. We deal with unreliable observations, which include missing and noisy observations on the real world. Modeling the observations in our formulation is straightforward. The use of the learned model allows us to address a data-driven optimization task to find the plans that most satisfy those observations (plan recognition) and the goals that are sufficiently supported by the causal relationships of the observations (goal recognition). We perform a complete evaluation of our approach in IPC domains under several indicators (accuracy, spread and ROC curves) with varying degrees of partial observability and noise on the observations. We also perform a comparison with other model-based approaches from literature.

Default risk transmission in the travel and leisure industry

Default risk in the Travel and Leisure (T&L) industry remains understudied despite its implications for the industry’s health and stability. This paper investigates the transmission of default risk among US T&L firms over various credit horizons from July 22, 2008 to December 9, 2022, paying special attention to the impact of COVID-19. The short-, medium-, and long-term default risk factors are extracted from the Credit Default Spread (CDS) curve of the US T&L industry then used within a connectedness approach. The results reveal considerable default risk transmission, particularly in the long-term. Default risk transmission has spiked across all horizons since the pandemic, reflecting the deterioration in credit quality of T&L firms under the pandemic. Analysis of the drivers of default risk transmission shows that several macro-financial variables, especially news market sentiment and stock market volatility induced by the pandemic, have an important explanatory role.

Effect of copper and stainless steel slags on fresh, mechanical and pore structure properties of alkali activated ground granulated blast furnace slag

Reusing and recycling waste materials in the construction industry can provide a significant contribution in terms of environmental and economical aspects. This article reports a material case study on the combined use of three industrial by-products (metallurgical slags), namely copper slag (CS), stainless steel slag (SS) and ground granulated blast furnace slag (GGBFS) as solid precursors to produce binary blend alkali activated materials (AAMs). A total of seven binary AAMs mixtures with different ratios (0%, 25%, 50%, and 75%) of CS and SS as partial replacement of GGBFS were activated by a combination of sodium hydroxide and sodium silicate as an alkali solution. The behaviour of the mixtures is reported in terms of flow spread and flow curve, compressive and flexural tensile strengths, mercury intrusion porosimetry (MIP) and water absorption by submerging. The flow spread and dynamic yield stress of the mixtures increased and decreased by partially replacing GGBFS with CS or SS, respectively, leading to better workability properties of the fresh mixture. The use of CS or SS up to 50% reduced the compressive and flexural tensile strengths of the specimens between 30‐42% and 14‐46%, respectively. Despite this drop in strength, the 28 days compressive strength of the studied mixtures was always higher than 50 MPa. The capillary porosity of the specimens increased with higher contents of CS or SS. Water absorption results of the specimens appeared highly influenced by the applied drying temperature in the test method. Overall, partial replacement of GGBFS by CS or SS, targeting binary precursor systems, is promising for replacement levels up to 50%.

Practices of Corporate Social Responsibility in the Readymade Garment Industry during COVID-19: A Study on Bangladesh Garment Industry

The Ready-Made Garment (RMG) industry has performed the concentrated fundamental role for the economic development of Bangladesh. To obtain the finale result, this exertions-in complexity industry wishes to experience after COVID0-19 for sustainable practices with the adoption of Corporate Social Responsibility (CSR) in its Garment production chain. CSR, being a fast-developing phenomenon in industrial arena, is drawing the concern of world-renowned clothing customers working with Bangladesh. To flatten the coronavirus spread curve, the authorities declared kingdom-huge vacations up to twenty-five April 2020 and enterprise and industrial activities have come to a grinding halt except emergency offerings. Many global customers are cancelling or postponing showed procurement orders as their shops are substantially closed in Europe, North America, Asia and some other place. According to Bangladesh Garment Manufacturers and Exporters Association (BGMEA), worldwide buyers have both cancelled or suspended $3.16 billion really worth of shipments related to 1,142 factories affecting 2.26 million people as of 18 April 2020.7 Millions of workers stare at joblessness as new orders dried up given the disintegrate in international call for apparels. This scenario is being changed after COVID-19 considering that a vast variety of Bangladeshi garment manufacturers have followed CSR practices spending a huge sum of money. This study will find out the current reputation of sustainable financial enchantment through the implementation of CSR practices inside the RMG enterprise after COVID -19 of Bangladesh. Keywords : Corporate Social Responsibility, Ready-Made Garments, COVID-19 DOI: 10.7176/EJBM/14-5-07 Publication date: March 31 st 2022

The credit spread curve distribution and economic fluctuations in Japan

Predicting the future economy is of great interest to practitioners and policymakers. In this study, we confront this problem by examining the relationship between credit spread curves and future economic activity. To this end, we construct a monthly empirical distribution of credit spread curves by calculating credit spreads of corporate bonds at the firm level in Japan and examine whether it can be used to predict a Japanese business cycle. We find that the credit spread curve information in higher deciles (implying lower credit quality) provides more predictive power for the future economy than the information of government bond yield curve or the credit spread index suggested by previous studies. In addition, the smooth-transition predictive regression analysis demonstrates that credit spread curves have more predictive power under the low uncertainty regime, and depict a significant predictive power for a short horizon for both regimes. Finally, our component-wise analysis shows that the credit spread curve information has robust predictive power for producer-side indicators under the low uncertainty regime and for labor market conditions, regardless of the regime.

COVID-19 pandemic, predictions and control in Saudi Arabia using SIR-F and age-structured SEIR model.

COVID-19 has affected every individual physically or physiologically, leading to substantial impacts on how they perceive and respond to the pandemic’s danger. Due to the lack of vaccines or effective medicines to cure the infection, an urgent control measure is required to prevent the continued spread of COVID-19. This can be achieved using advanced computing, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), cloud computing, and edge computing. To control the exponential spread of the novel virus, it is crucial for countries to contain and mitigate interventions. To prevent exponential growth, several control measures have been applied in the Kingdom of Saudi Arabia to mitigate the COVID-19 epidemic. As the pandemic has been spreading globally for more than a year, an ample amount of data is available for researchers to predict and forecast the effect of the pandemic in the near future. This article interprets the effects of COVID-19 using the Susceptible-Infected-Recovered (SIR-F) while F-stands for ‘Fatal with confirmation,’ age-structured SEIR (Susceptible Exposed Infectious Removed) and machine learning for smart health care and the well-being of citizens of Saudi Arabia. Additionally, it examines the different control measure scenarios produced by the modified SEIR model. The evolution of the simulation results shows that the interventions are vital to flatten the virus spread curve, which can delay the peak and decrease the fatality rate.

A multi-curve HJM factor model for pricing and risk management

In this paper, we introduce a multi-curve model under the historical probability based upon multiplicative relative spreads, inspired by the HJM and affine factor approaches, which implies positive and ordered spreads. In particular, we focus upon -XIBOR relative (instantaneous) forward rates and appropriate XIBOR HJM drift constraints, and we describe the dynamics of the different forward rates and spreads under different measure changes (including forward measures). We introduce an explicit model satisfying both the XIBOR HJM drift constraints as well as the property of positive and ordered spreads. We demonstrate the flexibility of this model for derivative pricing by focusing upon the price of a caplet and of options with a payoff based upon XIBOR forward prices with different tenors. We perform on one hand a calibration of the model based upon cap prices. On the other hand, we do an estimation of a spread curve in our proposed model under the historical probability by using a Kalman filter approach. Numerical results are included, and they confirm that the model performs very well.

The Homeless-Poor and the COVID-19 Stay-at-Home Policy of Government: Rethinking the Plight of Homelessness in Nigeria

The Government Stay-at-Home policy regarding the COVID-19 pandemic has seemingly aggravated the plight of the homeless, especially in Nigeria that has about 108 million homeless people who require special attention. Observations show that most of them situate randomly across urban centers in Nigeria and have no access to pronounced shelter, healthcare facilities, hygiene materials, and defined source of income. In response to the global COVID-19 pandemic currently ravaging many countries, most governments adopted the Stay-at-Home policy among some other policy measures that were recommended by the World Health Organization toward containing the spread of the virus. Given the COVID-19 Stay-at-Home policy of Nigerian Government, this study looked into the plight of the homeless-poor with a view to evolve an inclusive pro-poor housing strategy for the country. Findings show that the homeless poor have negative perceptions of the Stay-at-Home policy, which they see as being rather inimical to their survival. The study highlights lack of space and shelter as major reasons for their aversion to social distancing. Their reasons have serious implications for compliance to the COVID-19 Stay-at-Home policy as well as the spread curve of the virus. The study therefore recommends a review of the COVID-19 Regulation toward incorporating “Pro-Poor Housing Provisions” measures strictly for the homeless poor in the country. Strong collaboration between government and established institutions, such as Government-Church/Mosque strategy, is also recommended.

Methodology for risk assessment of COVID-19 pandemic propagation

This paper proposes a methodology to perform risk analysis of the virus spread. It is based on the coupling between CFD modelling of bioaerosol dispersion to the calculation of probability of contact events. CFD model of near-field sneeze droplets dispersion is developed to build the SARS-CoV-2 effect zones and to adequately capture the safe distance. The most shared classification of droplets size distribution of sneezes was used.Droplets were modeled through additive heating/evaporation/boiling laws and their impact on the continuous phase was examined. Larger droplets move behind the droplet nuclei front and exhibit greater vertical drop due to the effect of gravity. CFD simulations provided the iso-risk curves extension (i.e., the maximum distance as well as the angle) enclosed by the incident outcome effect zone. To calculate the risk indexes, a fault tree was developed and the probability of transmission assuming as of the top event “COVID-19 infection” was calculated starting from the virus spread curve, as main base case. Four phases of virus spread evolution were identified: initiation, propagation, generalised propagation and termination. For each phase, the maximum allowable close contact was computed, being fixed the values of the acceptable risk index. In particular, it was found that during the propagation case, the maximum allowable close contacts is two, suggesting that at this point lockdown should be activated. The here developed methodology could drive policy containment design to curb spread COVID-19 infection.

Common Factors in the Term Structure of Credit Spreads and Predicting the Macroeconomy in Japan

This study extracts the common factors from firm-based credit spreads of major Japanese corporate bonds and examines the predictive content of the credit spread on the real economy. Instead of employing single-maturity corporate bond spreads, we focus on the entire term structure of the credit spread to predict the business cycle. We extend the dynamic Nelson-Siegel model to allow for both common and firm-specific factors. The results show that the estimated common factors are important drivers of individual credit spreads and have substantial predictive power for future Japanese economic activity. This study contributes to the literature by examining the relationship between firm-based credit spread curves and economic fluctuation and forecasting the business cycle.

A study of the possible factors affecting COVID-19 spread, severity and mortality and the effect of social distancing on these factors: Machine learning forecasting model.

BackgroundsSARS‐CoV‐2 is affecting different countries all over the world, with significant variation in infection‐rate and death‐ratio. We have previously shown a presence of a possible relationship between different variables including the Bacillus Calmette–Guérin (BCG) vaccine, average age, gender, and malaria treatment, and the rate of spread, severity and mortality of COVID‐19 disease. This paper focuses on developing machine learning models for this relationship.MethodsWe have used real‐datasets collected from the Johns Hopkins University Center for Systems Science and Engineering and the European Centre for Disease Prevention and Control to develop a model from China data as the baseline country. From this model, we predicted and forecasted different countries' daily confirmed‐cases and daily death‐cases and examined if there was any possible effect of the variables mentioned above.ResultsThe model was trained based on China data as a baseline model for daily confirmed‐cases and daily death‐cases. This machine learning application succeeded in modelling and forecasting daily confirmed‐cases and daily death‐cases. The modelling and forecasting of viral spread resulted in four different regions; these regions were dependent on the malarial treatments, BCG vaccination, weather conditions, and average age. However, the lack of social distancing resulted in variation in the effect of these factors, for example, double‐humped spread and mortality cases curves and sudden increases in the spread and mortality cases in different countries.The process of machine learning for time‐series prediction and forecasting, especially in the pandemic COVID‐19 domain, proved usefulness in modelling and forecasting the end status of the virus spreading based on specific regional and health support variables.ConclusionFrom the experimental results, we confirm that COVID‐19 has a very low spread in the African countries with all the four variables (average young age, hot weather, BCG vaccine and malaria treatment); a very high spread in European countries and the USA with no variable (old people, cold weather, no BCG vaccine and no malaria). The effect of the variables could be on the spread or the severity to the extent that the infected subject might not have symptoms or the case is mild and can be missed as a confirmed‐case. Social distancing decreases the effect of these factors.

Dynamic rumor spreading of public opinion reversal on Weibo based on a two-stage SPNR model

Exploring the dynamic mechanism of rumor reversal in several public emergency events can support managers to control and guild the spread of public opinion. In this study a two-stage rumor model, namely, susceptible, positive, negative, and recovered state (SPNR) is built to analyze rumor spread and reversal of rumors regarding emergencies on Weibo. The model considers the hysteresis of official news and public swing mentality based on epidemic models. To validate the proposed model, we compare the simulated data and the spread curve of rumor reversal on the basis of the real event “Chongqing bus plunging into the river”. The result shows the proposed model well simulates this real situation of news breakout, confirming the change of rumor-infected rate and probability of rumor disseminators transforming into positive public opinion disseminators, and the time of official statement for truth affecting rumor propagation in varying degrees. To effectively control and dispel rumors, proper guidance of official statement or authority release for truth in the public event is necessary.

Estimation of COVID-19 spread curves integrating global data and borrowing information.

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.

Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19.

We perform a statistical analysis for understanding the effect of the environmental temperature on the exponential growth rate of the cases infected by COVID-19 for US and Italian regions. In particular, we analyze the datasets of regional infected cases, derive the growth rates for regions characterized by a readable exponential growth phase in their evolution spread curve and plot them against the environmental temperatures averaged within the same regions, derive the relationship between temperature and growth rate, and evaluate its statistical confidence. The results clearly support the first reported statistically significant relationship of negative correlation between the average environmental temperature and exponential growth rates of the infected cases. The critical temperature, which eliminates the exponential growth, and thus the COVID-19 spread in US regions, is estimated to be TC = 86.1 ± 4.3 F0.

Credit Spreads, Rating Downgrades, and Downside Performance: A Market-Informed Approach to Monitoring Credit Risk

This paper studies the differential credit risks embedded in the cross-section of credit spreads. Using corporate bond data from 1999 to 2018, we find that credit spreads relative to those of peers — defined as bonds with the same stated credit rating — contain reliable information about future bond performance and credit migration. Bonds with substantially higher credit spreads relative to those of their peers have higher rates of future downgrades. Using the midpoint between the peer spread curve and the next-lower-rated spread curve as a threshold, we observe that the downgrade frequency in the next three to 12 months is three to four times higher, on average, for bonds above the midpoint compared to those below. We also find that bonds with considerably wider credit spreads behave more in line with bonds with lower credit ratings in terms of average return, volatility, and downside performance. Our results suggest that complementing stated credit ratings with real-time market price data can improve credit risk monitoring.

The impact of intervention strategies and prevention measurements for controlling COVID-19 outbreak in Saudi Arabia.

On 11 March 2020, the World Health Organization announced the novel coronavirus COVID-19 outbreak as a pandemic due to the rapid growth in the number of cases worldwide. The ability of countries to contain and mitigate interventions is crucial in controlling the exponential spread of the novel virus. Several social distancing and control measurements have been applied in Saudi Arabia to mitigate COVID-19 epidemic such as quarantine, schools closure, suspending travels, reducing crowds, people movement restrictions, self-isolation and contacts tracing. This research aims to study the country interventions in Saudi Arabia and their impact on decreasing the spread of COVID-19. This paper examined different control measurements scenarios produced by a modified SEIR mathematical model with an emphasis on testing capacity expansion and number of critical cases. The modified SEIR mathematical model is solved numerically using Rung-Kutta analysis method for solving the modified SEIR system of ordinary differential equations. The simulation results revealed that the interventions are vital to flatten the virus spread curve. Early implementation of country interventions can delay the peak and decrease the population fatality rate.

Daily Spread Curves and Ester

In this short note, we describe a simple yet accurate way to set up a rate curve defined by daily forward rates that are computed as a spread over the daily forward rates of a reference rate curve. One current use case of interest is to build an Ester curve from an Eonia curve using the (constant) Ester-Eonia spread defined by the ECB (-8.5 bp). We derive error bounds and test the method with real market data in ORE resp. QuantLib.

The term structure of sovereign credit default swap and the cross‐section of exchange rate predictability

AbstractWe provide novel evidence on exchange rate predictability by using the term premia of the sovereign credit default swap (CDS). Using a sample of 29 countries, we find that the sovereign CDS term premia significantly predict the exchange rates out‐of‐sample. On average, a steeper CDS spread curve for a country predicts its currency appreciation against the U.S. dollar (USD). Empirically, although the sovereign CDS level mainly reflects global risk, the information in the term premia of the sovereign CDS spreads reveals country‐specific risk. Notably, the predictive power of the term premia is robust after controlling for the sovereign CDS level and other conventional global macroeconomic and financial factors. Further analysis shows that the information in the sovereign CDS term premia is also helpful for forecasting international stock market returns.

No-arbitrage determinants of credit spread curves under the unconventional monetary policy regime in Japan

We introduce an affine term structure model with observed macroeconomic factors for credit spread curves under the unconventional monetary policy regime in Japan. Empirical results based on the model selection using Japanese data demonstrate that the credit spread curves are dominated by the monetary policy and suggest that global economic forces, such as the U.S. Treasury yield and Baa-Aaa credit spread, play a major role in the dynamics of credit spread curves, complementing a growing body of literature explaining what drives credit spread curves. Our contemporaneous response and historical decomposition analyses find that monetary policy and global economic and financial forces have large impacts on credit spread curves at all maturities and rating classes.