Francisco López Herrera, División de Investigación, Facultad de Contaduría y Administración, Universidad Nacional Autónoma de México, Ciudad de México, México., Luis Guadalupe Macías-Trejo, Oscar V. De la Torre-Torres
Este artículo muestra los resultados de un análisis del desempeño de ocho de los criptoactivos más importantes entre la gran variedad que actualmente existe en el mercado. Se estudia su riesgo de mercado con base en métricas ampliamente utilizadas para activos financieros. El análisis se complementa con la evaluación de su desempeño dentro de portafolios formados con criterios convencionales. Se encuentra un comportamiento bastante heterogéneo entre los activos estudiados, sugiriendo que tal comportamiento obedece a las características específicas de cada uno de ellos, más que a las características comunes como una clase específica de activos.
This thesis examines how Distributed Ledger Technologies (DLTs) could be utilized in capital markets in general and in the Nordic capital market in particular. DLTs were introduced with the so called cryptocurrency Bitcoin in 2009 and has in the last few years been of interest to various financial institutions as a means to streamline financial processes. By combining computer scientific concepts such as public-key cryptography and consensus algorithms DLTs make it possible to keep shared databases with limited trust among the participators and without the use of a trusted third party. In this thesis various actors on the Nordic capital market were interviewed and their stance on DLTs were summarized. In addition to this a Proof of Concept of a permissioned DLT application for ownership registration of securities was constructed. It was found that all the interviewees were generally optimistic about DLTs potential to increase the efficiency of capital markets. The technology needs to be adopted to handle the capital markets demand for privacy and large transaction volumes, but there is a general agreement among the interviewees that these issues will be solved. The biggest challenge for an adoption of DLTs seem to lie in that of finding a common industry-wide standard.
Smart grids enable a two-way energy demand response capability through which a utility company offers its industrial customers various call options for energy load curtailment. If a customer has the capability to accurately determine whether to accept an offer or not, then in the case of accepting an offer, the customer can earn both an option premium to participate, and a strike price for load curtailments if requested. However, today most manufacturing companies lack the capability to make the correct contract decisions for given offers. This paper proposes a novel decision model based on activity-based costing (ABC) and stochastic programming, developed to accurately evaluate the impact of load curtailments and determine as to whether or not to accept an energy load curtailment offer. The proposed model specifically targets state-transition flexible and Quality-of-Service (QoS) flexible energy use activities to reduce the peak energy demand rate. An illustrative example with the proposed decision model under a call-option based energy demand response scenario is presented. As shown from the example results, the proposed decision model can be used with emerging smart grid opportunities to provide a competitive advantage to the manufacturing industry.
This paper studies the effects of stock market valuation on research investment, the rate of innovation, and welfare. In the presence of financing constraints for R&D investment, episodes of high market valuation can ease these constraints and raise the economy-wide investment in R&D and the rate of innovation. If the decentralized equilibrium rate of innovation is inefficiently low, then such episodes may lead to an increase in aggregate welfare even if the higher valuation is not entirely justified by fundamentals. We present a Schumpeterian-style growth model with a costly financial intermediation process to characterize the relationship between market value, entry of new firms, and the aggregate rate of innovation. We use the model to measure the welfare consequences of a stock market run-up that may only partly be justified by fundamentals. In particular, we apply the model to the US economy in the 1990s and calibrate the impact of the NASDAQ boom on the rate of innovation, growth and welfare. The welfare effect depends on the underlying change in fundamentals. We find that with an acceleration in US trend productivity growth from a pre-1995 rate of 1.4% to a rate of 2.0% per annum, the NASDAQ boom will have resulted in a net welfare gain of 0.55%. If the new growth rate is as high as 3%, the net gain was 1.35% of the present discounted value of consumption.
Through analyzing the softness and hardness of budgeting constraints in research and development (R&D) investment under different institutions, we develop a theory of optimal R&D financing. Our theory not only provides a clear comparison of investment efficiency between centralized economies and market economies but also extends the analysis of soft budget constraints to firms in market economy. Based on this theory, we characterize optimal choices of R&D project financing in centralized and decentralized economies. Our results explain why some projects are financed internally by a large firm but others are cofinanced externally by several firms. We also explain what makes a centralized economy inefficient in R&D.