On the causes of Brexit

We analyse the voting pattern in the June 23rdreferendum on the continued participation of the United Kingdom in the European Union and evaluate the reasons for the results. We find that output, education and the share of older people at the regional level can explain attitudes towards immigrants and the European Union. Thus, regions where GDP per capita is low, a high proportion of people has low education, a high proportion is over the age of 65 and there is strong net immigration are more likely to be apprehensive of the European Union, be suspicious of immigrants and not want them as neighbours and, most importantly, to vote for Brexit. The fear of immigration does not seem to be fully justified in terms of the literature on the labour market effects of immigrants in the UK. Using the British Election Study we find similar results. Thus negative attitudes towards immigration and EU enlargement are correlated with voting for Brexit using data on individuals

On the causes of Brexit

We analyse the voting pattern in the June 23rd referendum on the continued participation of the United Kingdom in the European Union and evaluate the reasons for the results. We find that regions where GDP per capita is low, a high proportion of people have low education, a high proportion is over the age of 65 and there is strong net immigration are more likely to be apprehensive of the E.U., consider the enlargement of the E.U. as having gone too far, be suspicious of immigrants and not want them as neighbours and, most importantly, to vote for Brexit. The fear of immigration does not seem to be fully justified in terms of the literature on the labour market effects of immigrants in the UK. Looking at the response of the sterling exchange to poll numbers we find that investors appear to view Brexit as a negative event

The effect of Brexit on the UK Economy (so far)

The political turmoil in the UK following the referendum on future membership of the European Union in 2016 provides a natural experiment for studying the effects of political uncertainty (in the Knightian sense) on the economy. We find that the subsequent confusion and infighting in British politics has not affected the real economy much – employment is at a historical high and output growth is positive – but there are some signs of slowing investment and house price increases. The stock market has also not been much affected although it did fall after the referendum of 2016. The main effect of the Brexit vote and the subsequent political developments is found in the currency market where news that make a hard Brexit more likely cause the currency to depreciate. We conclude that leaving the European Union without an agreement is likely to make the currency depreciate and the stock market fall while output declines. In contrast, leaving with an agreement that gives continued access to the Single Market would likely make the currency appreciate, the stock market rise and employment and output increase further

Forecasting redenomination risk: unleashing the superior predictor via machine learning

Treball fi de màster de: Master's Degree in Economics and Finance. Finance Program. Curs 2022-2023Tutors: Filippo Ippolito i Christian BrownleesWe are the first to construct a general model for forecasting redenomination risk by using the difference in a country’s EUR-denominated CDS spreads compared to Germany. The goal of this work is to further close this gap by harnessing the power of machine learning algorithms, with a particular focus on techniques such as Random Forests, XGBoosting, and Lasso regressions. The results have important implications for policymakers, financial institutions and investors. Relying mainly on daily financial market data, we find that machine learning models significantly improve forecasting accuracy in both a structural crisis and a period of economic recovery. We conclude that XGB and Lasso are the best performing forecasting models, as for the latter modeling approach we get correlations between predicted and actual values as high as 0.6 for some countries. Finally, when we look at the measures of variable importance in our machine learning models, we indeed find that Lasso selects different types of predictors with much larger macroeconomic reference as relevant, which seems to explain the difference in prediction accuracy.Somos los primeros en construir un modelo general para pronosticar el riesgo de redenominación utilizando la diferencia entre los diferenciales de los CDS denominados en euros de un país en comparación con Alemania. El objetivo de este trabajo es cerrar aún más esta brecha aprovechando el poder de los algoritmos de aprendizaje automático, con especial atención en técnicas como Random Forests, XGBoosting y regresiones Lasso. Los resultados tienen implicaciones importantes para los responsables de la formulación de políticas, las instituciones financieras y los inversores. Basándonos principalmente en datos diarios del mercado financiero, encontramos que los modelos de aprendizaje automático mejoran significativamente la precisión de los pronósticos tanto en una crisis estructural como en un período de recuperación económica. Concluimos que XGB y Lasso son los modelos de pronóstico de mejor rendimiento, ya que para el último enfoque de modelado obtenemos correlaciones entre los valores previstos y reales de hasta 0,6 para algunos países. Finalmente, cuando observamos las medidas de importancia variable en nuestros modelos de aprendizaje automático, encontramos que Lasso selecciona diferentes tipos de predictores con una referencia macroeconómica mucho mayor como relevantes, lo que parece explicar la diferencia en la precisión de la predicción

Gas Chemistry in Geothermal Systems

Five new gas geothermometers are introduced. They are useful for predicting subsurface temperatures in water dominated geothermal systems. The geothermometers use data on CO{sub 2}, H{sub 2}S and H{sub 2} concentrations in fumarole steam as well as CO{sub 2}/H{sub 2} and H{sub 2}S/H{sub 2} ratios. It is demonstrated that the gas composition of fumarole steam may be used with or withour drillhole data to evaluate steam condensation in the upflow zones of geothermal systems. Uncertainty exists, however, in distinguishing between the effects of steam condensation and phase separation at elevated pressures. The gas content in steam from discharging wells and the solute content of the water phase can be used to evaluate which boiling processes lead to "excess steam" in the discharge and at which temperature this "excess steam" is added to the fluid moving through the aquifer and into the well. Examples, using field data, are given to demonstrate all the mentioned applications of geothermal chemistry