This paper provides an empirical assessment of whether the world economy has become smaller in terms of economic distance over the last decades. We adopt a cross-sectional spatial econometric approach, relating domestic output volatility to (distance-weighted averages of) other countries' output volatility, using a sample of 135 countries and rolling 10-year time windows over the period 1955 to 2006. Using descriptive measures, test statistics, and spatial econometric estimates, we find that cross-country interdependence was virtually insignificant in the early post-war period but has increased strongly from the mid-1960s to the mid-1980s and remained at a high level since then. Results for the most recent period suggest that common shocks to output volatility have a magnified impact and roughly quadruplicate through international spillover effects, which are transmitted through both trade and financial openness.

The airline industry is a major user of jet fuel and this constitutes a large component of the operating costs and is a risk coefficient for airlines. Several studies have been conducted on how oil price volatility affect stock prices and cash flows as well as how, in general, firms that uses derivatives experience lower stock returns volatility and stock s .The impact of oil price volatility on airline stock s and the impact of hedging on airline stock s have not been adequately examined, this paper fills this gap. By gathering daily frequency of oil spot prices to access the quarterly oil price volatility and stock s from 16 European airlines, we correlate quarterly oil price volatility to quarterly airline stock s as well as stock s and hedging percentages between 2010-2015, we reject the hypothesis that oil price volatility has an impact on airline stock s and that hedging reduces stock s. These findings therefore suggest that oil price volatility do not have a large impact on systematic risks or that hedging offset systematic risks. The findings are of interest to investors who want to make well informed investment decisions based on non-diversifiable equity risk since it has become popular for management recently to implement hedging policies to signal competency in risk management in order to attract investments.

La présente thèse s'est inscrite dans une perspective d'améliorer les outils de mesure du risque de marché en proposant des solutions capables de reproduire certaines caractéristiques empiriques d'évolution des marchés financiers. A travers une étude empirique sur des données réelles, nous montrons que la réalité des marchés financiers possède certaines caractéristiques empiriques connues et résumées sous le nom "faits stylisés", qui rendent les mesures usuelles du risque de marché incapables de reproduire ces caractéristiques. Nous proposons des nouvelles méthodes de mesure de la Value-at-Risk (VaR), en fonction de la volatilité passée et des corrélations existant entre les actifs composant un portefeuille, dans le cadre de deux grandes approches de mesure du risque : une approche de mesure du risque global (ou risque univarié) et une approche de mesure du risque multiple (ou risque multivarié), tout en testant leur qualité prédictive au moyen des procédures de backtesting. Les résultats obtenus montrent une grande capacité des différentes mesures utilisées à capturer les faits stylisés caractérisant l'évolution des marchés financiers étudiés avec une nette surperformance des méthodes de mesure de la VaR estimées dans le cadre du risque multivarié par rapport à celles du risque univarié. / This thesis has object to improve the methods for estimating market risk by offering solutions capable to replicate some empirical properties of asset returns. Through an empirical study on real data, we show that the reality of financial markets has some empirical characteristics known and summarized as "stylized facts" that render the conventional market risk measurement unable to reproduce. We propose a Value-at-Risk (VaR) measures, based on modeling portfolio volatility and correlations between assets classes, using two risk measurement approaches: an univariate risk measurement approach and multivariate risk measurement approach, and testing their quality predictive using backtesting procedures. The results obtained show a great ability of different used risk measurement to capture the stylized facts characterizing financial markets, with a clear outperformance of the multivariate VaR measures than the univariate VaR measures.

We analyze the interaction between monetary policy in the US and the global economy proposing a new class of Bayesian global vector autoregressive models that accounts for time-varying parameters and stochastic volatility (TVP-SV-GVAR). Our results suggest that US monetary policy responds to shocks to the global economy, in particular to global aggregate demand and monetary policy shocks. On the other hand, US-based contractionary monetary policy shocks lead to persistent international output contractions and a drop in global inflation rates, coupled with rising interest rates in advanced economies and a real depreciation of currencies with respect to the US dollar. We find considerable evidence for heterogeneity in the spillovers across countries, as well for changes in the transmission of monetary policy shocks over time. (authors' abstract) / Series: Department of Economics Working Paper Series

This dissertation is centered around two comprehensive themes: the extraction of information embedded in equity index option prices, and how to use this information in order to be able to make optimal decisions in the equity index option markets. These problems are important for decision makers in the equity index options markets, since they are continuously faced with making decisions under uncertainty given observed market prices. The methods developed in this dissertation provide robust tools that can be used by practitioners in order to improve the quality of the decisions that they make. In order to be able to extract information embedded in option prices, the dissertation develops two different methods for estimation of stable option implied surfaces which are consistent with observed market prices. This is a difficult and ill-posed inverse problem which is complicated by the fact that observed option prices contain a large amount of noise stemming from market micro structure effects. Producing estimated surfaces that are stable over time is important since otherwise risk measurement of derivatives portfolios, pricing of exotic options and calculation of hedge parameters will be prone to include significant errors. The first method that we develop leads to an optimization problem which is formulated as a convex quadratic program with linear constraints which can be solved very efficiently. The second estimation method that we develop in the dissertation makes it possible to produce local volatility surfaces of high quality, which are consistent with market prices and stable over time. The high quality of the surfaces estimated with the second method is the crucial input to the research which has resulted in the last three papers of the dissertation. The stability of the estimated local volatility surfaces makes it possible to build a realistic dynamic model for the equity index derivatives market. This model forms the basis for the stochastic programming (SP) model for option hedging that we develop in the dissertation. We show that the SP model, which uses generated scenarios for the squared local volatility surface as input,  outperforms the traditional hedging methods that are described in the literature. Apart from having an accurate view of the variance of relevant risk factors, it is when building a dynamic model also important to have a good estimate of the expected values, and thereby risk premia, of those factors. We use a result from recently published research which lets us recover the real-world density from only a cross-section of observed option prices via a local volatility model. The recovered real-world densities are then used in order to identify and estimate liquidity premia that are embedded in option prices. We also use the recovered real-world densities in order to test how well the option market predicts the realized statistical characteristics of the underlying index. We compare the results with the performance of commonly used models for the underlying index. The results show that option prices contain a premium in the tails of the distribution. By removing the estimated premia from the tails, the resulting density predicts future realizations of the underlying index very well.

This paper provides a comprehensive empirical assessment of the relation between the cyclicality of fiscal expenditure policy, output volatility, and economic growth, using a large cross-section of 88 countries over the period 1960 to 2004. Identification of the effects of (endogenous) cyclical expenditure policy is achieved by exploiting the exogeneity of countries political and institutional characteristics, which we find to be relevant determinants of the cyclicality of expenditures. There are three main results: First, both pro- and countercyclical expenditure policy amplify output volatility, much in a way like pure fiscal shocks that are unrelated to the cycle. Second, output volatility, due to variations in cyclical and discretionary fiscal policy, is negatively associated with economic growth. Third, there is no direct effect of cyclicality on economic growth other than through output volatility. These findings advocate the introduction of fiscal rules that limit the use of (discretionary and) cyclical fiscal (expenditure) policy to improve growth performance by reducing volatility. (author's abstract)

The exchange rates of currencies of different countries show higher volatility than it could be explained by the volatility of the fundamental variables. There are introduced different models which try to describe the behavior of these exchange rates in this Diploma Thesis. Their comparison is made with respect to the ability to capture the volatility of the empirically observed data. The behavior of exchange rates may also be influenced by interventions of the state institutions and therefore we introduced models which allow the effect of such regulatory interventions. These models were applied on real data. The properties of the model predictions of exchange rates were compared and evaluated with respect to their ability to explain the volatility of the empirical data. At the summary of my work one of the models has been used to simulate the behavior of the exchange rate during the application of different intervention strategies of the Central Bank. Powered by TCPDF (www.tcpdf.org)

In this thesis, we aim to find the effect of economic crisis announcements arising from the US subprime mortgage crisis and European sovereign debt crisis on the market volatility in theBRIC countries. We implement a GARCH model in order to compare the effect of individual news announcements and find that the US crisis had a bigger impact on BRIC market volatility than the European crisis. Of particular note, we find the US bailout had a higher impact than the failure of Lehman Brothers or any European crisis dates that were considered. We then examine the volatility transmission mechanism by implementing a VAR model to create a spillover index. Following, we apply a rolling window approach, creating spillover plots which show that both return and volatility spillovers are affected by crisis announcements. The importance of our results are related to investor decision making, particularly the relationship between market return and risk in developing country markets. Far to our knowledge, no recent literature has compared the two crises in the way we have nor with the datasets we have used.

This work concentrates on exploring the influence of social networks to financial markets. We have introduced a novel approach to Twitter sentiment analysis, in which we collect continuous stream of data and analyze it. Our original data set contains over 200 million English written Tweets from the period between July 1, 2014 and October 9, 2014. Twitter sentiment is used as a good representative of investors' mood. On hourly data we investigate how investors are influenced by basic emotions, moods and sentiment in their decision making processes as well as the influence of keywords related to specific securities and FOREX symbols. Particularly, we examine the relationships between Twitter-based variables and returns as well as volatility of several financial instruments on a wide range of data including commodities, currencies and S&P 500 Cash Index. We show that Twitter sentiment influences volatility of securities' returns, tested and shown on both conditional and realized volatility models. We also describe the effect of Twitter sentiment on securities' returns. Moreover, we reveal the influence of basic emotions on investors' decision making processes. Our results suggest that investors are influenced by emotions and moods, especially at longer investment horizons. The impact of emotions at shorter...

The thesis deals with electoral and partisan cycles in stock returns of nine CEE countries and checks consistency of observed cycles with efficient market hypothesis. The evidence mostly supports possibility of political influence on stock markets, but the effects often have opposite sign than hypothesized. Electoral cycle has been found in Estonia and Hungary, while returns in four other countries are significantly lower before elections. Markets more often exhibit left-wing premium, it is significant in the Czech Republic, Lithuania and Romania. The results are similar between nominal and real returns. Both cycles are also considered significant for the panel of countries. Moreover, cycles are hardly explainable by macroeconomic conditions, which indicates market inefficiency. This is confirmed by analysis of volatility, which reveals that risk does not correspond to changes in returns induced by the cycles.