In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.

No abstract

Climate change, the accelerated industrialization of emerging countries, as well as the growing demand for transparency from stakeholders, are all factors that influence the environmental performance of companies. Thus, eco-efficient behavior can improve financial performance by increasing wealth generation and decreasing the volatility of listed financial assets. There is a lot of previous literature showing diverse results of the effect of eco-efficiency on corporate profitability, but this is not the case when we refer to risk. This study analyzes the relationship between eco-efficient behavior and the share price volatility of companies traded in emerging markets. For this purpose, a sample of 346 companies listed in 24 countries was studied for the period between 2010 and 2017. The results show a positive effect. Thus, the recommendation is that a clear commitment to eco-efficient investment can improve the environmental impact of companies, from the private, public, and institutional spheres.

<p align="LEFT">En este estudio, analizamos si las fusiones disminuyen el costo de capital en las empresas públicas de México. Para ello se parte de una muestra formada por empresas de diversos sectores que componen el Índice de Precios y Cotizaciones (IPC) que realizaron una operación de adquisición aprobada por las autoridades mexicanas en los años 2010 y 2011. Para estimar el costo de capital se utilizó el CAPM tradicional y el D-CAPM, el cual considera una métrica de riesgo a la baja. Ambas estimaciones se realizaron 3 años antes y 3 años después de la adquisición con dos métodos de medición: Mínimos Cuadrados Ordinarios y Regresión Borrosa. Los resultados muestran la ventaja del modelo de regresión borrosa sobre mínimos cuadrados ordinarios, principalmente en períodos con mayor incertidumbre. Además, considerando las estimaciones del modelo D-CAPM, podemos concluir para las empresas de la muestra que existe una posibilidad entre 0.62 y 0.65 de reducción del costo de capital después de la fusión.</p>