Bear beta

We test whether bear market risk, time variation in the probability of future bear market states, is priced. We construct an Arrow–Debreu security that pays off in bear market states (AD Bear) from traded Standard & Poor’s (S&P) 500 index options and use its returns to measure bear market risk. We find that bear beta (exposure to bear market risk) has a strong relation with expected stock returns that is robust, persistent, and remains strong among liquid and large stocks. Historical bear beta also predicts future bear market risk exposure. We conclude that bear market risk is priced in the cross section of stock returns.     

The characteristic function of rough Heston models

It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with small Hurst parameter, provide very relevant dynamics in order to reproduce the behavior of both historical and implied volatilities. However, due to the non‐Markovian nature of the fractional Brownian motion, they raise new issues when it comes to derivatives pricing. Using an original link between nearly unstable Hawkes processes and fractional volatility models, we compute the characteristic function of the log‐price in rough Heston models. In the classical Heston model, the characteristic function is expressed in terms of the solution of a Riccati equation. Here, we show that rough Heston models exhibit quite a similar structure, the Riccati equation being replaced by a fractional Riccati equation.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

ASSESSING THE IMPACT OF DISTRIBUTIVE POLICIES ON THE BRAZILIAN ECONOMY USING AN SCGE MODEL

This study investigates the impact of macroeconomic policies on the Brazilian economy. We present a two-sector, open-economy, Structuralist Computable General Equilibrium model that distinguishes among three economic classes and assumes no financial sector. The Social Accounting Matrix for Brazil in 2006 serves as a benchmark for our model. We compare the medium-run effects of five experiments: an income transfer towards formal workers, a transfer to informal labour, an investment shock, an exchange rate depreciation, and a policy mix that combines (exchange rate) depreciation with income transfer towards modern (sector) workers. The policy measures reinforce each other in terms of their potential to enhance growth. Our findings underscore the importance of redistributive policies to foster economic expansion.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.     

企业社会责任与企业价值——组织惰性与行业敏感度的调节作用

以2012—2016年沪深两市A股上市公司的数据为依据,通过实证分析研究了企业社会责任对企业价值的影响,并从组织层面和行业层面的视角探讨了组织惰性和行业敏感度对该影响的调节作用。研究结果表明,企业社会责任对企业价值具有显著的促进作用;组织惰性减弱了企业社会责任对企业价值的促进作用;行业敏感度阻碍了企业通过履行社会责任进而提升企业价值的过程。     

A functional time series analysis of forward curves derived from commodity futures

We study forward curves formed from commodity futures prices listed on the Standard and Poor’s-Goldman Sachs Commodities Index (S&P GSCI) using recently developed tools in functional time series analysis. Functional tests for stationarity and serial correlation suggest that log-differenced forward curves may be generally considered as stationary and conditionally heteroscedastic sequences of functions. Several functional methods for forecasting forward curves that more accurately reflect the time to expiry of contracts are developed, and we found that these typically outperformed their multivariate counterparts, with the best among them using the method of predictive factors introduced by Kargin and Onatski (2008).     

Reluctant Generalised Additive Modelling

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.     

Designing business models in circular economy: A systematic literature review and research agenda

The concept of circular economy is increasingly receiving attention in different domains, including strategic management, operations management, and technology management. It requires companies to design their business model (i.e., the value network, the relationships with the supply chain partners, and the value propositions towards customers) around a new concept of sustainable development that reduces consumption of natural resources and preserves the environment. However, extant research falls short in terms of explaining how companies design their business model according to the circular economy principles. Starting from this premise, the present paper provides a systematic review of the literature on the design of business models in the context of circular economy, aiming to offer an overview of the state of research and outline a promising research agenda.     

Organizational characteristics explaining participation in sustainable business models in the sharing economy: Evidence from the fashion industry using conjoint analysis

Environmental and social challenges require new sustainable business models, like sharing platforms. However, sharing platforms differ widely in their contribution to a more sustainable society. Whereas idealistic sharing platforms have dominant social goals, other sharing platforms are, or became, commercial. We explore the attractiveness of the typical organizational characteristics of idealistic sharing platforms in the fashion industry context, an industry with negative environmental and social impacts. Based on a literature review and exploratory focus groups, we conduct an online survey using conjoint analysis, completed by 1,512 respondents. Our results reveal that potential users prefer clothes sharing platforms to be small, to not partner with large clothing retailers, to have the possibility to participate in decision‐making, but to not require shareholding.