Credit risk pricing in a consumption-based equilibrium framework with incomplete accounting information

We present a consumption-based equilibrium framework for credit risk pricing based on the Epstein–Zin (EZ) preferences where the default time is modeled as the first hitting time of a default boundary and bond investors have imperfect/partial information about the firm value. The imperfect information is generated by the underlying observed state variables and a noisy observation process of the firm value. In addition, the consumption, the volatility, and the firm value process are modeled to follow affine diffusion processes. Using the EZ equilibrium solution as the pricing kernel, we provide an equivalent pricing measure to compute the prices of financial derivatives as discounted values of the future payoffs given the incomplete information. The price of a zero-coupon bond is represented in terms of the solutions of a stochastic partial differential equation (SPDE) and a deterministic PDE; the self-contained proofs are provided for both this representation and the well-posedness of the involved SPDE. Furthermore, this SPDE is numerically solved, which yields some insights into the relationship between the structure of the yield spreads and the model parameters.     

央行数字货币体系构建对金融系统的影响

本文基于货币演化逻辑以及公共选择理论的视角,论证了央行数字货币产生的必要性;从价值维度、技术维度、运营维度、应用维度的视角,论证了央行数字货币体系构建的充分性。央行数字货币体系构建过程中可能面临三个主要选择:计不计息、匿不匿名、跨不跨境。这三个选择对金融系统的影响体现为:现金、银行存款与央行数字货币的"三角平衡"问题,金融普惠原则下金融监管与用户隐私保护的"二元平衡"问题以及人民币国际化趋势下的全球支付"一点对接"问题。     

Epstein-Zin utility maximization on a random horizon

This paper solves the consumption-investment problem under Epstein-Zin preferences on a random horizon. In an incomplete market, we take the random horizon to be a stopping time adapted to the market filtration, generated by all observable, but not necessarily tradable, state processes. Contrary to prior studies, we do not impose any fixed upper bound for the random horizon, allowing for truly unbounded ones. Focusing on the empirically relevant case where the risk aversion and the elasticity of intertemporal substitution are both larger than one, we characterize the optimal consumption and investment strategies using backward stochastic differential equations with superlinear growth on unbounded random horizons. This characterization, compared with the classical fixed-horizon result, involves an additional stochastic process that serves to capture the randomness of the horizon. As demonstrated in two concrete examples, changing from a fixed horizon to a random one drastically alters the optimal strategies.     

When and how consumers are willing to exchange data with retailers: An exploratory segmentation

Consumer data is a crucial resource for retailers. Yet accessing this data increasingly requires consumers to willingly participate in data exchange. This paper draws on social exchange theory and privacy calculus to investigate differences in consumer willingness to exchange data with retailers. Consumers are also profiled on their perceptions of retailer's use and abuse of data, along with the antecedents and outcomes of these perceptions. We employ a cross-sectional quantitative survey and collect data from a sample of 463 US consumers. For statistical analysis, we employ a latent class segmentation and identify six consumer segments which differ in their perceptions of the consumer-retailer data exchange. The key drivers of these segment differences include privacy concerns, technology readiness, and general trust of, and engagement with, retail brands. The segments also differ in their subsequent views towards the use and abuse of their data by retailers, including willingness to exchange data. Hence, when accessing and utilizing consumer data, it is important that brands identify different segments, and adapt their approach accordingly.     

Can behavioral biometrics make everyone happy?

The market for behavioral biometrics—a technological evolution whereby patterns in human movement and activities can be identified, captured, and analyzed—is expected to exceed US $11 billion by 2031. We highlight the evolution from early physiological biometrics (e.g., fingerprints and iris scans used to verify the identity of individuals) to today’s behavioral biometrics. Technological advancements now turn our retail stores, offices, and warehouses into live data streams that let us closely and automatically monitor employees’ conduct at work. Although this development raises several legitimate surveillance and privacy concerns, behavioral biometrics can potentially benefit organizations and employees alike. Such mutual benefits compel managers to approach behavioral biometrics using our TRUST framework: transparency of intentions, respect for concerns, understanding the importance of choice, sharing the data benefits, and proactively timing the development. Managers who do so will gain a clear focus on organizational and employee well-being.     

An unrealistic and undesirable alternative to the right to be forgotten

In their “Così fan tutte: A better approach than the right to be forgotten”, Martha Garcia-Murillo and Ian MacInnes (2018) present an alternative to the right to be forgotten. It mainly consists of changing social norms to a more open and transparent society and improving anti-discrimination laws. In this reply, I challenge their suggestions both empirically and normatively, arguing that they cannot achieve what they set-out; that their analysis of privacy and the right to be forgotten misses important aspects that go beyond discrimination; and that we have a shared interest in allowing individuals to live a partly opaque life.