This paper proposes an auction type resolution for smart derivatives. It has been discussed to migrate derivatives contracts to smart contracts (smart derivatives). Automation is often discussed in this context. It is also important to prepare to avoid disputes from practical perspectives. There are controversial issues to terminate the relationship at defaults. In OTC derivative markets, master agreements define a basic policy for the liquidation process but there happened some disputes over these processes. We propose to define an auction type resolution in smart derivatives, which each participant would find beneficial.

Novel blockchain technology provides the infrastructure layer for the creation of decentralized appli-cations. A rapidly growing ecosystem of applications is built around financial services, commonly referred to as decentralized finance. Whereas the intangible concept of decentralization is presented as a key driver for the applications, defining and measuring decentralization is multifaceted. This pa-per provides a framework to quantify decentralization of governance power among blockchain appli-cations. Governance of the applications is increasingly important and requires striking a balance be-tween broad distribution, fostering user activity, and financial incentives. Therefore, we aggregate, parse, and analyze empirical data of four finance applications calculating coefficients for the statistical dispersion of the governance token distribution. The gauges potentially support IS scholars for an objective evaluation of the capabilities and limitations of token governance and for fast iteration in design-driven governance mechanisms.

Economic shocks due to Covid-19 were exceptional in their severity, suddenness and heterogeneity across industries. To study the upstream and downstream propagation of these industry-specific demand and supply shocks, we build a dynamic input-output model inspired by previous work on the economic response to natural disasters. We argue that standard production functions, at least in their most parsimonious parametrizations, are not adequate to model input substitutability in the context of Covid-19 shocks. We use a survey of industry analysts to evaluate, for each industry, which inputs were absolutely necessary for production over a short time period. We calibrate our model on the UK economy and study the economic effects of the lockdown that was imposed at the end of March and gradually released in May. Looking back at predictions that we released in May, we show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent. We discuss the relative extent to which the model's dynamics and performance was due to the choice of the production function or the choice of an exogenous shock scenario. To further explore the behavior of the model, we use simpler scenarios with only demand or supply shocks, and find that popular metrics used to predict a priori the impact of shocks, such as output multipliers, are only mildly useful.

In this paper aspects related to handling of intraday imbalances for hydro and wind power are addressed. The definition of imbalance cost is established and used to describe the potential benefits of shifting from plant-specific schedules to a common load requirement for wind and hydropower units in the same price area. The Nordpool intraday pay-as-bid market has been the basis for evaluation of imbalances, and some main characteristics for this market has been described. We consider how internal handling of complementary imbalances within the same river system with high inflow uncertainty and constrained reservoirs can reduce volatility in short-term marginal cost and risk compared to trading in the intraday market. We have also shown the the imbalance cost for a power producer with both wind and hydropower assets can be reduced by internal balancing in combination with sales and purchase in a pay-as-bid intraday market

We consider a class of stochastic control problems with a delayed control, both in drift and diffusion, of the type dX t = $\alpha$ t--d (bdt + $\sigma$dW t). We provide a new characterization of the solution in terms of a set of Riccati partial differential equations. Existence and uniqueness are obtained under a sufficient condition expressed directly as a relation between the horizon T and the quantity d(b/$\sigma$) 2. Furthermore, a deep learning scheme is designed and used to illustrate the effect of delay on the Markowitz portfolio allocation problem with execution delay.

Indeed, the global production (as a system of creating values) is eventually forming like a gigantic and complex network/web of value chains that explains the transitional structures of global trade and development of the global economy. It's truly a new wave of globalisation, and we term it as the global value chains (GVCs), creating the nexus among firms, workers and consumers around the globe. The emergence of this new scenario asks: how an economy's firms, producers and workers connect in the global economy. And how are they capturing the gains out of it in terms of different dimensions of economic development? This GVC approach is very crucial for understanding the organisation of the global industries and firms. It requires the statics and dynamics of diverse players involved in this complex global production network. Its broad notion deals with different global issues (including regional value chains also) from the top down to the bottom up, founding a scope for policy analysis (Gereffi & Fernandez-Stark 2011). But it is true that, as Feenstra (1998) points out, any single computational framework is not sufficient to quantification this whole range of economic activities. We should adopt an integrative framework for accurate projection of this dynamic multidimensional phenomenon.

Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized the events extracted from news, social media, and discussion board to forecast the stock trend in recent years. However, existing event-driven methods have two main shortcomings: 1) overlooking the influence of event information differentiated by the stock-dependent properties; 2) neglecting the effect of event information from other related stocks. In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks. The experimental studies on the real-world data demonstrate the efficiency of our REST framework. The results of investment simulation show that our framework can achieve a higher return of investment than baselines.

The presence of Super-Apps have changed the way we think about the interactions between users and commerce. It then comes as no surprise that it is also redefining the way banking is done. The paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, two experiments with different graph-based methodologies are proposed, the first uses graph based features as input in a classification model and the second uses graph neural networks. Our results show that variables of centrality, behavior of neighboring users and transactionality of a user constituted new forms of knowledge that enhance statistical and financial performance of credit risk models. Furthermore, opportunities are identified for Super-Apps to redefine the definition of credit risk by contemplating all the environment that their platforms entail, leading to a more inclusive financial system.

Decentralised government levels are often entrusted with the management of public works and required to ensure well-timed infrastructure delivery to their communities. We investigate whether monitoring the activity of local procuring authorities during the execution phase of the works they manage may expedite the infrastructure delivery process. Focussing on an Italian regional law which imposes monitoring by the regional government on "strategic" works carried out by local buyers, we draw causal claims using a regression-discontinuity approach, made unusual by the presence of multiple assignment variables. Estimation is performed through discrete-time survival analysis techniques. Results show that monitoring does expedite infrastructure delivery.

The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model flexibility when controlling for wage determinants, and iii) the choice of different parametric and semi-parametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39% when we enforce comparability between men and women and use a more flexible specification of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and also less sensitive to the way wage determinants are included.

I study the impact of corona populism -- politics aimed at denying or downplaying the danger posed by COVID-19 for strategic reasons -- on the evolution of the pandemic using regional data from Austria. The right-wing FPOE first vocalized strong support for strict lockdown measures, but made a corona populist turn at the end of the first wave of infections. Using panel regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths per capita after the FPOE switched their policy stance, while there is no correlation before the policy switch. Interestingly, I do not find a statistically significant correlation between the FPOE vote share and the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing. To explore this hypothesis, I extend the classical SIRD model to incorporate conditional quarantine and two groups of agents: the majority and the corona sceptics, where the latter are less inclined to get tested and engage in social distancing. Such a model can explain the puzzling empirics: if mixing is sufficiently homophilic, an increase in the share of corona sceptics can cause an increase in the number of deaths without increasing the number of reported infections. I finally discuss the partly non-trivial implications of the interplay between group sizes, behavioral differences and the degree of homophily on public health outcomes within the theoretical model.

This study presents new analytic approximations of the stochastic-alpha-beta-rho (SABR) model. Unlike existing studies that focus on the equivalent Black-Scholes (BS) volatility, we instead derive the equivalent constant-elasticity-of-variance (CEV) volatility. Our approach effectively reduces the approximation error in a way similar to the control variate method because the CEV model is the zero vol-of-vol limit of the SABR model. Moreover, the CEV volatility approximation yields a finite value at a zero strike and thus conveniently leads to a small-time asymptotics for the mass at zero. The numerical results compare favorably with the BS volatility approximations in terms of the approximation accuracy, small-strike volatility asymptotics, and no-arbitrage region.

In modern life insurance, Markov processes in continuous time on a finite or at least countable state space have been over the years an important tool for the modelling of the states of an insured. Motivated by applications in disability insurance, we propose in this paper a model for insurance states based on Markov jump processes with more general state spaces. We use this model to derive a new type of Thiele's differential equation which e.g. allows for a consistent calculation of reserves in disability insurance based on two-parameter continuous time rehabilitation rates.