Catastrophe bond pricing in the primary market: The issuer effect and pricing factors

COVID pandemic has highlighted the importance of hedging against catastrophic events, for which the catastrophe bond market plays a critical role. Our paper develops a two-level modelling and uses a unique, hand-collected dataset, which is one of the largest and most detailed datasets to date containing: 101 different issuers, 794 different bonds, spanning 1997–2020. We identify issuer effects robustly, isolating them from bond specific pricing effects, therefore providing more credible pricing factor results. We find that bond pricing and volatility are heavily impacted by the issuer, causing 26% of total price variation. We also identify specific issuer characteristics that significantly impact bond pricing and volatility, such as the issuer’s line of business accounting for up to 36% of total price variation. We further find that issuer effects are significant over different market cycles and time periods, causing substantial price variation. The size and content of our data also enables us to identify the counter-intuitive relation between bond premiums and maturity, and bond premiums and hybrid bond triggers.     

A new theoretical framework of shopping motives and channel preference behaviour in the digital era

The unprecedented dissemination of digital technology has changed people's psychology including their shopping behaviour in the last two decades. Smartphone led digital applications and advancements have disrupted consumers' shopping processes, purchase decisions, and priorities as well as increased their exposure, aspirations and expectations inevitably. Therefore, it is imperative to examine the relevance of various elements of shopping motives holistically. Therefore, the present study aims to develop a new theoretical framework based on significant elements of shopping motives for physical products in the digital technology era. For this purpose, an exploratory study, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modelling (SEM) were used. EFA generated five latent factors by grouping 18 independent variables. CFA validated latent factor construct and measured model fit. SEM visualized the path analysis and portrayed the pattern of relations between latent factors into a single factor structural model (consumer shopping motives framework). Results show that consumers' shopping priorities are changing as 3 conventional variables (‘role-playing’, ‘status and authority’, and ‘pleasure of bargaining’) became obsolete and 3 new (‘anywhere and any-time shopping’, ‘safe and secure digital transaction’, and ‘unbiased reviews and ratings”) came into existence prominently. The format and perspective of ‘fun, entertainment and recreation’ ‘social interaction and communication’, and ‘exposure to new and latest trends' have been changing. Consumers are becoming more technology-dependent in their shopping processes and purchase decisions. The validation of the framework on channel preference behaviour revealed that consumers largely prefer offline channels for the fulfilment of risk-free and social sub-motives, and online channels for convenience sub-motive. Therefore, broad structural change and clarity, specification of priorities, a shift in the format and perspective of few elements of shopping motives, intrinsic passion for the use of digital technology and web service in the shopping journey, and simplification of antecedents for growing popularity of multi-channel shopping paradigm are the key novelty of this study.     

Measuring farm productivity under production uncertainty

This research introduces a novel empirical application to the assessment of farm productivity growth. While the existing research on productivity change has primarily focussed on ex post output observations, it has been shown that ignoring production uncertainty can lead to unreliable results. Using a state-contingent framework to represent the stochastic production environment, we extend the recent line of research that merged the state-contingent approach and efficiency measurement to productivity change using the Malmquist and Luenberger productivity indices. Using a balanced panel of 117 arable crop farms surveyed in 2011 and 2015, we show through the study results that productivity decreased, with technological regress being the major source of productivity change. Differences in productivity change between nonstochastic and stochastic modelling show the relevance to consider the state-contingent framework when assessing farms' productivity.     

Heterogenous beliefs with sentiments and asset pricing

We present a dynamic equilibrium model with two irrational investors: an extrapolator and a contrarian, whose beliefs regarding the growth rate of dividend stream are biased by their sentiments. The key contribution is to connect two disagreements with the degree of irrationality of investors and to provide novel insights into the predictability of stock return. We show that the higher level of sentiment disagreement is, the more stock price is overvalued. However, the future stock price will decline because the extrapolator’s sentiment will cool down over time. Therefore, the sentiment disagreement negatively predicts future return. At the meanwhile, our model not only shows that the survey expectations about cashflows increase the variations in asset price and dampen the corresponding volatility, but also helps to explain the mixed results about the relationship between the investors’ belief dispersions and stock return predictability.     

Reassessing the dependence between economic growth and financial conditions since 1973

Adrian, Boyarchenko and Giannone ((2019), ABG) adapt quantile regression (QR) methods to examine the relationship between US economic growth and financial conditions. We confirm their empirical findings, using their methodology and their pre-2016 sample. Mindful of the importance of the Covid-19 pandemic, we extend the sample to 2021Q3 and find attenuation of the key estimated coefficients using ABG's empirical methods. Given the pandemic observations, we provide robust QR analysis of dependence based on ranked data and explain the relationship with extant copula modelling methods.     

Exploring customer heterogeneity with a scale-extended latent class choice model: Experimental evidence drawn from urban water users

The search for improved water pricing is central to urban water reform in many countries. Establishing efficient water prices is notoriously difficult, not least because different customers have different demands for water and yet they are presently faced with a one-size-fits-all approach to pricing and service. This is especially challenging where water availability fluctuates widely, as is the case in many parts of Australia, because the impacts of exposure to episodic periods of scarcity can differ markedly. There is now substantial interest in the notion of ‘unbundling’ the water product to provide a better fit between customers' preferences and the level of service received. Following this trend, this study provides important insights into householders' willingness to pay for a range of flexible water options using a choice experiment. The paper reports a relatively underemployed extension to the latent class modelling framework to investigate preference heterogeneity towards urban water products, including purchasing services that involve the provision of environmental and amenity outcomes. The work adds to studies that use choice data to reveal heterogeneity while improving our understanding of household customers' demands. Overall, it also brings into focus questions about the future management of water in urban contexts.     

It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customers

The adaptive clothing market, which focuses on the inclusive design of clothing and footwear for people with varying degrees of disability, has grown substantially in recent years. However, few scholars have sought to understand the perspectives of online adaptive clothing consumers. This study employed topic modelling, sentiment analysis and collocation analysis to discover common themes and insights emerging from online customer reviews, scraped from a third-party review platform and three retailing web sites. We utilized customer value and functional, expressive and aesthetic theories to group the results from topic modelling into key themes. Clothing function is the most frequently discussed theme in online customer reviews, followed by customer service and clothing aesthetics. Collocation analysis revealed the cause underlying each theme vis-à-vis customer satisfaction (e.g., fit and material quality) and dissatisfaction (e.g., sewing defects and lost shipment). The findings contribute to understanding the clothing needs and wants of people living with disabilities. It also provides practical guidelines on product offerings and online service optimization for adaptive clothing retailers.     

Predictability of tail risks of Canada and the U.S. Over a Century: The role of spillovers and oil tail Risks☆

We examine the tail risk spillovers between Canada and U.S. stock markets using over a century data, and also account for the roles of tail risks of other advanced economies (France, Germany, Japan, Italy, Switzerland, and the UK) and oil-market tail risk. We use the “best” tail risk measure obtained from different variants of the Conditional Autoregressive Value at Risk (CAViaR) model developed by Engle and Manganelli (2004) in the predictive model and compare its performance with that of an AR(1) benchmark model. We find strong evidence of risk spillovers between the two stock markets. We find contrasting evidence for the predictability of oil-market tail risk, with positive predictability in case of the net oil exporter and negative in case of the net oil importer. Further results using tail risks of other advanced economies (combined) support possible diversification potential for Canadian stocks in the presence of market risks of advanced economies other than the U.S. Our findings have implications for investors and are robust to various out-of-sample forecast horizons, alternative data frequencies, data splits, and 1% and 5% VaRs.     

Interbank contagion risk in China under an ABM approach for network formation

This paper uses an agent-based model to construct an interbank network for the Chinese interbank market using a sample of 299 commercial banks from 2014 to 2019. We analyze the importance and vulnerability of banks using the DebtRank algorithm. Our results show that the Chinese interbank market bears a certain level of systemic risk, especially among lower-tiered banks. The results also show a bank is more vulnerable if it has a higher interbank lending ratio and greater financial connectivity. Meanwhile, a bank is more influential if it has a larger net worth and greater financial connectivity.