This paper suggests that business cycles may be a manifestation of coupled real economy and stock market dynamics and describes a mechanism that can generate economic fluctuations consistent with observed business cycles. To this end, we seek to incorporate into the macroeconomic framework a dynamic stock market model based on opinion interactions (Gusev et al., 2015). We derive this model from microfoundations, provide its empirical verification, demonstrate that it contains the efficient market as a particular regime and establish a link through which macroeconomic models can be attached for the study of real economy and stock market interaction. To examine key effects, we link it with a simple macroeconomic model (Blanchard, 1981). The coupled system generates nontrivial endogenous dynamics, which exhibit deterministic and stochastic features, producing quasiperiodic fluctuations (business cycles). We also inspect this system's behavior in the phase space. The real economy and the stock market coevolve dynamically along the path governed by a stochastically-forced dynamical system with two stable equilibria, one where the economy expands and the other where it contracts, resulting in business cycles identified as the coherence resonance phenomenon. Thus, the incorporation of stock market dynamics into the macroeconomic framework, as presented here, allows the derivation of realistic behaviors in a tractable setting.

Anomalous diffusions arise as scaling limits of continuous-time random walks (CTRWs) whose innovation times are distributed according to a power law. The impact of a non-exponential waiting time does not vanish with time and leads to different distribution spread rates compared to standard models. In financial modelling this has been used to accommodate for random trade duration in the tick-by-tick price process. We show here that anomalous diffusions are able to reproduce the market behaviour of the implied volatility more consistently than usual L\'evy or stochastic volatility models. We focus on two distinct classes of underlying asset models, one with independent price innovations and waiting times, and one allowing dependence between these two components. These two models capture the well-known paradigm according to which shorter trade duration is associated with higher return impact of individual trades. We fully describe these processes in a semimartingale setting leading no-arbitrage pricing formulae, and study their statistical properties. We observe that skewness and kurtosis of the asset returns do not tend to zero as time goes by. We also characterize the large-maturity asymptotics of Call option prices, and find that the convergence rate is slower than in standard L\'evy regimes, which in turn yields a declining implied volatility term structure and a slower decay of the skew.

The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implements a two-consensus scheme to enforce upper-layer economic policies and achieve fundamental layer performance and robustness: the DPoEV incentive consensus on the application and resource layers, and the DABFT distributed consensus on the fundamental layer. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to achieve the best balance of performance, robustness, and security. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets.

The literature suggests that autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing activities while travelling. As a consequence, the generalised cost of car travel may decrease, and car users may become less sensitive to travel time. By facilitating private motorised mobility, AVs may eventually impact land use and households' residential location choices. This paper seeks to advance the understanding of the potential impacts of AVs on travel behaviour and land use by investigating stated preferences for combinations of residential locations and travel options for the commute in the context of autonomous automobile travel. Our analysis draws from a stated preference survey, which was completed by 512 commuters from the Sydney metropolitan area in Australia and provides insights into travel time valuations in a long-term decision-making context. For the analysis of the stated choice data, mixed logit models are estimated. Based on the empirical results, no changes in the valuation of travel time due to the advent of AVs should be expected. However, given the hypothetical nature of the stated preference survey, the results may be affected by methodological limitations.

We construct two examples of shareholder networks in which shareholders are connected if they have shares in the same company. We do this for the shareholders in Turkish companies and we compare this against the network formed from the shareholdings in Dutch companies. We analyse the properties of these two networks in terms of the different types of shareholder. We create a suitable randomised version of these networks to enable us to find significant features in our networks. For that we find the roles played by different types of shareholder in these networks, and also show how these roles differ in the two countries we study.

We propose a novel framework of estimating systemic risk measures and risk allocations based on a Markov chain Monte Carlo (MCMC) method. We consider a class of allocations whose j-th component can be written as some risk measure of the j-th conditional marginal loss distribution given the so-called crisis event. By considering a crisis event as an intersection of linear constraints, this class of allocations covers, for example, conditional Value-at-Risk (CoVaR), conditional expected shortfall (CoES), VaR contributions, and range VaR (RVaR) contributions as special cases. For this class of allocations, analytical calculations are rarely available, and numerical computations based on Monte Carlo methods often provide inefficient estimates due to the rare-event character of crisis events. We propose an MCMC estimator constructed from a sample path of a Markov chain whose stationary distribution is the conditional distribution given the crisis event. Efficient constructions of Markov chains, such as Hamiltonian Monte Carlo and Gibbs sampler, are suggested and studied depending on the crisis event and the underlying loss distribution. Efficiency of the MCMC estimators are demonstrated in a series of numerical experiments.

Recent technology advances have enabled firms to flexibly process and analyze sophisticated employee performance data at a reduced and yet significant cost. We develop a theory of optimal incentive contracting where the monitoring technology that governs the above procedure is part of the designer's strategic planning. In otherwise standard principal-agent models with moral hazard, we allow the principal to partition agents' performance data into any finite categories and to pay for the amount of information that the output signal carries. Through analysis of the trade-off between giving incentives to agents and saving the monitoring cost, we obtain characterizations of optimal monitoring technologies such as information aggregation, strict MLRP, likelihood ratio-convex performance classification, group evaluation in response to rising monitoring costs, and assessing multiple task performances according to agents' endogenous tendencies to shirk. We examine the implications of these results for workforce management and firms' internal organizations.

We develop a model of electoral selection and accountability in the presence of mainstream and alternative media outlets. In addition to standard high and low competence types, the incumbent may be an aspiring autocrat, who controls the mainstream media and will cause substantial harm if not removed from office. Alternative media can help voters identify and remove aspiring autocrats and can enable voters to focus on honest mainstream media assessments of incumbents' competence. But malicious alternative media that peddle false conspiracy theories about the incumbent and the mainstream media can induce voters to mistakenly remove nonautocratic incumbents, which in turn demotivates incumbent effort and undermines accountability. The alternative media is most dangerous when it is sufficiently credible that voters pay attention to it, but sufficiently likely to be malicious that it undermines accountability.