What is the value of value-at-risk after all?: A conditional approach using quantile regressions

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Wage adjustments under extreme downward nominal wage rigidity

Using a rich and highly accurate dataset for Portugal spanning from 1986 to 2013, this paper analyzes the determinants of downward nominal wage rigidity, mainly focusing on macroeconomic factors. The data supports the hypothesis that recessionary periods alongside with low in ation contribute to a higher degree of wage rigidity, as measured by the incidence of nominal wage freezes. It is further highlighted how this lack of wage adjustments con- tributed to an increase in labor costs which culminated in a wage markup of 6-7%. This paper, thus seems to corroborate the argument that low in ation did exacerbated the downward in exibility of (real) wages after the Great Recession.

Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation -intramural and external R&D- on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140. Keywords: Innovation sources, R&D, Productivity, Quantile regression

Innovation sources and productivity: a quantile regression analysis

This paper explores the effects of two main sources of innovation —intramural and external R&D— on the productivity level in a sample of 3,267 Catalan firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and services industries.

Global Warming And Extreme Events: Rethinking The Timing And Intensity Of Environmental Policy

The possibility of low-probability extreme events has reignited the debate over the optimal intensity and timing of climate policy. In this paper we therefore contribute to the literature by assessing the implications of low-probability extreme events on environmental policy in a continuous-time real options model with “tail risk”. In a nutshell, our results indicate the importance of tail risk and call for foresighted pre-emptive climate policies.

Quantile forecasts of inflation under model uncertainty

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for different predictors to affect different quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future inflation by providing superior predictive densities compared to mean regression models with and without BMA.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation - intramural and external R&D— on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140 Keywords: Innovation sources, R&D, Productivity, Quantile Regression

Quantiles of the Realized Stock-Bond Correlation

Abstract: We scrutinize the realized stock-bond correlation based upon high frequency returns. We use quantile regressions to pin down the systematic variation of the extreme tails over their economic determinants. The correlation dependence behaves differently when the correlation is large negative and large positive. The important explanatory variables at the extreme low quantile are the short rate, the yield spread, and the volatility index. At the extreme high quantile the bond market liquidity is also important. The empirical fi…ndings are only partially robust to using less precise measures of the stock-bond correlation. The results are not caused by the recent …financial crisis. Keywords: Extreme returns; Financial crisis; Realized stock-bond correlation; Quantile regressions; VIX. JEL Classifi…cations: C22; G01; G11; G12

Stable isotope geochemistry and formation mechanisms of quartz veins; Extreme paleoaltitudes of the Central Alps in the Neogene

Quartz veins ranging in size from less than 50 cm length and 5 cm width to greater than 10 m in length and 5 m in width are found throughout the Central Swiss Alps. In some cases, the veins are completely filled with milky quartz, while in others, sometimes spectacular void-filling quartz crystals are found. The style of vein filling and size is controlled by host rock composition and deformation history. Temperatures of vein formation, estimated using stable isotope thermometry and mineral equilibria, cover a range of 450 degrees C down to 150 degrees C. Vein formation started at 18 to 20 Ma and continued for over 10 My. The oxygen isotope values of quartz veins range from 10 to 20 permil, and in almost all cases are equal to those of the hosting lithology. The strongly rock-buffered veins imply a low fluid/rock ratio and minimal fluid flow. In order to explain massive, nearly morromineralic quartz formation without exceptionally large fluid fluxes, a mechanism of differential pressure and silica diffusion, combined with pressure solution, is proposed for early vein formation. Fluid inclusions and hydrous minerals in late-formed veins have extremely low delta D values, consistent with meteoric water infiltration. The change from rock-buffered, static fluid to infiltration from above can be explained in terms of changes in the large-scale deformation style occurring between 20 and 15 Ma. The rapid cooling of the Central Alps identified in previous studies may be explained in part, by infiltration of cold meteoric waters along fracture systems down to depths of 10 km or more. An average water flux of 0.15 cm 3 cm(-2)yr(-1) entering the rock and reemerging heated by 40 degrees C is sufficient to cool rock at 10 km depth by 100 degrees C in 5 million years. The very negative delta D values of < -130 permil for the late stage fluids are well below the annual average values measured in meteoric water in the region today. The low fossil delta D values indicate that the Central Alps were at a higher elevation in the Neogene. Such a conclusion is supported by an earlier work, where a paleoaltitude of 5000 meters was proposed on the basis of large erratic boulders found at low elevations far from their origin.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.