Using CAViaR models with implied volatility for value-at-risk estimation

This paper proposes VaR estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market’s expectation of risk. Forecast combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models, a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residual. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P500 daily returns

Using Economic Incentives to Conserve CITES-listed Species: A Scoping Study on ITQs for Sturgeon in the Caspian Sea

Resource /Energy Economics and Policy,

Less disagreement, better forecasts: adjusted risk measures in the energy futures market

This paper develops a generic adjustment framework to improve in the market risk forecasts of diverse risk forecasting models, which indicates the degree to which risk is under- and overestimated. In the context of the energy commodity market, a market in which tail risk management is of crucial importance, the empirical analysis shows that after this adjustment framework is applied, the forecasting performance of various risk models generally improves, as verified by a battery of backtesting methods. Additionally, our method also lessens the risk model disagreement among post-adjusted risk forecasts

The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables\u2019 relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles

Deliverable 2 (SustainAQ)

The European Project SustainAQ (Framework 6) aims to identify the limiting factors for the sustainable production of aquatic origin food in Eastern Europe. It focuses on the possible use of Recirculation Aquaculture Systems (RAS) as sustainable method for the production of aquatic animals as mentioned in the communication of the European Commission on Aquaculture in 2009. RASs already exist mainly in western countries and proved economically feasible. RASs allow controlling the production process including effluents, biosecurity and escapes. Eastern European countries are facing challenges related to their excessive water use waste emission, and others. Therefore, these countries are potential beneficiaries of improved sustainability through RAS use. This project intends to assess the benefits of introducing and applying RAS for Eastern European aquaculture. This project involves three Western European countries (Norway, the Netherlands and France) and six East European countries (Croatia, Turkey, Romania, Hungary, Czech Republic and Poland). Ten research institutions collaborate in different tasks (coordination, data collection, data analysis, etc.), and nine small-medium enterprises (SME) participate in data mining (Table 1). The present data is therefore based on the situation in those countries during 2006 till 2008 before the report got finally compiled in 2008/2009

The impact of short tandem repeat variation on gene expression.

Short tandem repeats (STRs) have been implicated in a variety of complex traits in humans. However, genome-wide studies of the effects of STRs on gene expression thus far have had limited power to detect associations and provide insights into putative mechanisms. Here, we leverage whole-genome sequencing and expression data for 17 tissues from the Genotype-Tissue Expression Project to identify more than 28,000 STRs for which repeat number is associated with expression of nearby genes (eSTRs). We use fine-mapping to quantify the probability that each eSTR is causal and characterize the top 1,400 fine-mapped eSTRs. We identify hundreds of eSTRs linked with published genome-wide association study signals and implicate specific eSTRs in complex traits, including height, schizophrenia, inflammatory bowel disease and intelligence. Overall, our results support the hypothesis that eSTRs contribute to a range of human phenotypes, and our data should serve as a valuable resource for future studies of complex traits

Quantile regression methods in finance: the caviar case

The thesis tries to investigate how quantile regression methods can be apply to measures of riskope

The Emergence of new Successful Export Activities in Uruguay

El proyecto “El surgimiento de nuevas actividades exportadoras exitosas en América Latina” busca identificar los elementos claves en el proceso de descubrimiento de nuevas oportunidades de exportación en diferentes países de la región, con el objetivo de proponer políticas y reformas que permitan aumentar el ritmo de descubrimientos, en particular teniendo en cuenta la importancia relativa de diversas fallas de mercado. El enfoque general del estudio puede resumirse en que “los mercados se desempeñan bien al brindar señales de la rentabilidad de actividades que ya existen, pero su desempeño es pobre cuando se trata de actividades que podrían existir pero no existen. Aun si estas actividades no son nuevas en el sentido de que están presentes en economías más ricas, los productores se ven enfrentados a una considerable incertidumbre respecto a los costos y la productividad bajo las condiciones del mercado local. Introducirse en estos nuevos sectores típicamente requiere un inversor pionero, que indica a otros la rentabilidad de dichas actividades. Llamamos a este proceso de descubrir la estructura de costos interna de la economía auto-descubrimiento” (Hausmann y Rodrik, 2003). “En el proceso de auto-descubrimiento abundan las externalidades de información, debido a que la información de costos descubierta por un empresario no puede conservarse en forma privada. Si la empresa pionera resulta rentable, esto es fácilmente observable por otros. Los imitadores entran entonces en la actividad, la renta del productor establecido se disipa y se establece un nuevo sector. Si, por el contrario, el pionero quiebra, las pérdidas son soportadas en su totalidad por el empresario. En consecuencia, la actividad empresarial de esta naturaleza no es una actividad con alta recompensa: las pérdidas son privadas mientras las ganancias se socializan. Por tanto, los mercados no proporcionan suficiente actividad empresarial en actividades nuevas” (Hausmann, Rodríguez-Clare y Rodrik, 2006). El estudio realizado para Uruguay consistió en analizar cuatro actividades exportadoras nuevas para el país, en el contexto del marco teórico propuesto por el BID y siguiendo la metodología común establecida para todos los casos incluidos en el proyecto regional. Asimismo, en el marco de este estudio se construyó una base de datos armonizada de las exportaciones uruguayas de bienes a nivel de producto y empresa, que permite analizar la actividad exportadora a nivel de empresa, producto y mercado de destino en las últimas dos décadas. La disponibilidad de series de tiempo consistentes permitió superar las limitaciones de información que provocaban en las estadísticas los cambios introducidos en la clasificación de productos en cuatro oportunidades (1985, 1993, 1997, 2002). Este estudio busca una mejor comprensión de estos problemas en el caso de Uruguay, presentando, en primer término, una visión de conjunto del desempeño exportador de Uruguay y su política comercial, y un análisis de la actividad exportadora a nivel de firma. En segundo lugar se analizan en profundidad cuatro sectores: software, forestal, caviar y esturión, y vacunas de origen animal a partir de los cuales se extraen lecciones de políticas públicas.exportaciones, fallas de mercado, fallas de coordinación, proceso de auto-descubrimiento, software, sector forestal, vacunas de origen animal, caviar y esturión

Surgimiento de actividades de exportación exitosas en Uruguay: cuatro casos específicos

En este trabajo se presenta el análisis de cuatro casos específicos de surgimiento de cuatro actividades de exportación exitosas de Uruguay: software de computación, productos madereros, caviar y carne de esturión, y vacunas para animales. En cada uno de esos casos específicos se trata cómo empresas, asociaciones y varios gobiernos a varios niveles han manejado crisis de mercado y facilitado el suministro de los bienes públicos necesarios para cada actividad. El análisis de estos casos específicos presenta además una descripción de las características de los actores principales en cada ramo de actividad así como las externalidades positivas que brindan a los emuladores, especialmente la difusión de conocimientos sobre exportación. También se presenta en cada área un caso opuesto de actividad menos exitosa (electrónica, vino, carne de rana y biotecnología, respectivamente) así como una sección sobre implicaciones de políticas.Agriculture, Exports, Manufacturing, Services, Uruguay

Essays on Machine Learning for Risk Analysis in Finance, Insurance and Energy

[eng] This thesis provides research catalogued in the area of risk assessment. Specifically, it contributes to the fields of international finance and asset pricing in finance, and risk assessment in energy economics and transportation research. We present in this thesis a generalization of the spillover indexes to analyze interconnectedness at firm level, and define the aggregate influence from a sector and a country on a firm. We also discuss which factors are relevant for predicting conditional quantiles across the distribution of returns and present a method for selecting factors based on the investor interests. We study the performance of quantile regression against quantile time-series models. Finally, we present a regression framework which estimates VaR and CTE ensuring noncrossing conditions for various quantile levels, and discuss results on energy and telematics data. Within the financial contagion literature, we aim to provide a better understanding of international spillovers and a method for visualize which country and sector are its main drivers. We show that not all companies are driven by their own country or sector, which should be considered by investors and risk managers when assessing company risk and managing investments. In this paper we show that a large percentage of firms’ stocks are driven by their country. But contrary to the belief where country is the main driver of a company’s return movements, a part depends mainly on its sector. We note that 1) the financial services and energy companies are positioned at the center of the network, and 2) northern and western Europe are highly interconnected, while eastern and southern Europe present lower spillovers. 3) For the British energy firms British Petroleum (BP) and Royal Dutch Shell, we evidence greater spillovers from France than from Great Britain itself. 4) We identify which non-Russian firms are most influenced by Russia, simulating a risk management analysis in the event of of turmoil distresses such as the recent Ukrainian conflict. 5) We show the improvement on spillover information when using individual firm connectedness and aggregating spillovers afterwards against calculating spillovers directly from indexes. 6) We finally show that eastern Europe has increased interconnectedness with the rest of the continent after the Covid-19 pandemic. Regarding the asset pricing literature, we aim to understand the key elements that predict extreme quantile levels of a stock return. We study which factors for a 7-factor asset pricing specification are more relevant for each part of the distributions’ tail. The 7-factor specification is constituted by the factors size, book-to-market, operating profitability, investment, momentum, market beta and liquidity. We present a method to add more factors depending on the investors’ interests. We use quantile regression models for predicting quantile levels 0.05, 0.25, 0.5, 0.75 and 0.95 of the stock returns using cross-sectional characteristics as covariates from the Open Source Cross-Sectional Asset Pricing Dataset (Chen and Zimmermann, 2021). We observe that the factor size changes from positive to negative sign when predicting lower quantiles to higher quantiles. We show that extreme quantile level estimations perform better than the median in terms of pseudo-R2. Regarding factor significance, the variable investment has lower predictive power than other factors in terms of t-statistics for all tested quantile levels. Liquidity gains significance if quantile levels increase. For book-to-market, profitability, momentum and market beta, median predictions of returns are more significant than extreme quantile level estimations. The opposite happens for size, which presents higher relevance for predicting extreme quantile levels of the returns’ distribution. We observe that during crisis periods, some factors lose significance. This is the case of profitability and momentum for quantile levels 0.05 and 0.5, and size, book-to-market and market beta for quantile level 0.5. We add additional factors individually and compare the weighted average pseudo-R2 obtained across all 5 quantile levels. The weighting depends on the strategy that the investor follows. For all strategies tested, the most relevant factors to add to the 7-factor specification are momentum seasonality and net operating assets. Following, for strategies more interested in predicting losers’ tails (left part of the distribution), adding asset growth is recommended, but if the investor is interested in the winners’ tail (right part of the distribution), the recommended factor to add is enterprise multiple. Within the asset pricing literature, we encourage the use of cross-sectional information against time-series factors to predict extreme quantile levels of the right-hand side of the response distribution during periods of high volatility. By using this methodology, we do not restrict the information on panel-like datasets, which allows us to study more companies, and provide estimates for newly added firms. We use quantile regression specification with cross-sectional characteristics obtained from the Open Source Cross-Sectional Asset Pricing Dataset (Chen and Zimmermann, 2021) and compare results against a CAViaR (Engle and Manganelli, 2004) specification. Fama and French (2020) evidence that the average returns are better explained by using cross-sectional factors than by using time-series factors. We show that this only applies on extreme quantile levels during high volatility periods. We show that individual firm Hits (exceedances above VaR) calculated using time-series models tend to accumulate, while using cross-sectional data we avoid concentrations. We show that cross-sectional information improves the prediction of Value-at-Risk (VaR) and Conditional Tail Expectations (CTE). We finally discuss changes on capital requirements for a firm. In general, by using cross-sectional information, capital requirements should be increased from when time-series information is used. During turmoil periods the opposite happens: capital requirements should decrease compared to when using the CAViaR specification. Inside the area of non-crossing quantiles, we define the non-crossing property for VaR and CTE for several quantile levels. We define a regression framework based on neural networks that creates an environment for predicting VaR and CTE for several quantile levels while asserting non-crossing conditions. The proposed neural network predicts VaR and CTE as positive excesses of the previous VaR and CTE. We prove that this definition satisfies the non-crossing property and show its improvement against the Monotone Composite Quantile Regression Neural Network (Cannon, 2018) and a quantile regression and CTE linear approach on an energy consumption and telematic datasets. We show the estimation improvements on extreme quantile levels of the right part of the distribution against the other tested models by using Murphy diagrams (Ehm et al., 2016). We present examples with crossing predictions to demonstrate the infeasibility of such results in a business context, which we overcome using the proposed model