The present thesis tries to argue the importance of non monetary factors in explaining real exchange rate volatility. The main interest is on the effect of trade openness on real effec-tive exchange rate (REER) volatility. Based on theoretical studies I test the existence of a negative relationship between total trade share of an economy and the volatility of REER. Empirical evidence on a panel of 11 CEE and Baltic Countries for the 1995-2006 period confirms the relationship. The conclusion is that for these specific countries a large part of variation of the real exchange rate can be explained by openness of the respective economy to trade.

Estimating the integrated volatility of high frequency realized prices is an important issue in microstructure literature. Bandi and Russell (2006) derived the optimal-sampling frequency, and Zhang et al. (2005) proposed a "two-scales estimator" to solve the problem. In this study, we propose a new estimator based on a signal to noise ratio statistic with convergence rate of Op (n^(−1/ 4) ). The method is applicable to both constant and stochastic volatility models and modi¡Âes the Op (n^(−1/ 6) ) convergence rate of Zhang et al. (2005). The proposed estimator is shown to be asymptotic e¡Ócient as the maximum likelihood estimate for the constant volatility case. Furthermore, unbiased estimators of the two elements, the variance of the microstructure noise and the fourth moment of the realized log returns, are also proposed to facilitate the estimation of integrated volatility. The asymptotic prop- erties and e®ectiveness of the proposed estimators are investigated both theoretically and via simulation study.

This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the period from 1/16/2008 to 7/29/2011. The statistical approach employs dynamic log-returns quantile regression models to forecast price densities using implied volatilities (IVs) and factors estimated through principal component analysis (PCA) from the IVs, pooled IVs and lagged returns. Extensive in-sample and out-of-sample analyses are conducted, including assessment of excess trading returns, and evaluations of several combinations of quantiles, model specifications, and NCFC's. The results suggest that the IV-PCA-factors, particularly pooled return-IV-PCA-factors, improve quantile forecasting power relative to models using only individual IV information. The ratio of the put-IV to the call-IV is also found to improve quantile forecasting performance of log returns. Improvements in quantile forecasting performance are found to be better in the tails of the distribution than in the center. Trading performance based on quantile forecasts from the models above generated significant excess returns. Finally, the fact that the single IV forecasts were outperformed by their quantile regression (QR) counterparts suggests that the conditional distribution of the log-returns is not normal.

Allteftersom svenska företag utökar sina verksamheter på den globala marknaden uppstår nya möjligheter men även nya risker. De flesta länder har nu ett rörligt växelkurssystem vilket lett till att företagens valutarisker ökat. Volatiliteten på valutamarknaden har under de senaste åren ökat i omfattning vilket ökar betydelsen av fungerande valutariskhantering. Syftet med denna uppsats är att undersöka hur vissa svenska multinationella företag hanterar sina valutarisker och om det har förändrats i tider av hög volatilitet på valutamarknaden och förändrat konjunktursläge. Hur identifierar och hanterar företag sina valutaexponeringar? Vilka faktorer inverkar på ett företags valutariskhantering? Förändras valutariskhanteringen vid ökad volatilitet på valutamarknaden och förändrat konjunkturläge? I uppsatsen har vi använt oss utav en kvalitativ metod med ett hermeneutiskt synsätt för att uppnå en djupare förståelse av ämnet och på grund utav att uppsatsens syfte kräver en metod som möjliggör för oss författare att förstå tankesättet bakom handlingarna. Vi startade med att samla in sekundärdata för att skapa en teoretisk referensram att använda som bas att utgå våra intervjuer ifrån och analysera datan. Slutsatserna vi som uppsatsskrivare kommit fram till av studien är att den ökade volatiliteten påverkat valutariskhanteringen och intresset av valutariskhantering ute på operativ nivå i företagen ökat. Uppsatsen visar även på att det osäkra marknadsläget lett till att företagen minskat säkringsnivåerna på deras exponeringar för att undvika att bli översäkrad. Vi anser att det vore intressant att använda sig av ett större urval företag (alternativt att fokusera branschvis) ur en längre tidsperiod. Det har även uppkommit frågor under uppsatsen gång som vi inte kunnat gå in närmare på, exempelvis hur företagen arbetar med den ekonomiska exponeringen. Även en studie som närmare undersöker IAS 39s påverkan på företagens valutariskhantering vore intressant. Uppsatsen har bidragit till ökad kunskap om vilka faktorer som påverkar valutariskhanteringsarbetet och hur valutariskhanteringen förändras under volatila och mer osäkra förhållanden. / As multinational Swedish companies extend their international operations they encounter new opportunities as well as new risks. Since most economies today has floating currency systems the currency risk for the firms also increases. During the last years substantially increased volatility on the currency market has lead to an increased importance of a functioning currency risk management. The purpose of this bachelor thesis is to investigate how some Swedish multinational firms manage their currency risks and how they have become affected of the increased market volatility and an uncertain economic situation. How do firms measure and manage their currency exposure? Which variables affect firms' currency risk management? Do the firms' currencies risk management change during an increased volatility and uncertain economic situation?   In this thesis we have chosen to use a qualitative method with a hermeneutic approach to gain a deeper knowledge in the subject since our purpose demands a greater depth to understand the firms' way of thinking. We started off with gathering secondary data to create a theoretical frame of reference. The framework was later used a as support for the interviews and as well for the analysis. The conclusions we as writers made of this thesis was that the increased volatility affected the currency risk management and the interest to manage currency risks has risen on an operative levels. The thesis shows that the uncertain economic situation has lead to the firms have reduced their hedging levels to avoid become over hedged. We would find it interesting to gather data from a greater amount of firms (or focus on specific industries) during a longer period of time. Because of our limited time we couldn't explore all the questions that have come up. For an example: a study on how firms manage their economic exposure. We would also like to see a study on how IAS 39 affects firms' currency risk management. This thesis has contributed to increased knowledge about what variables influences firms' currency risk management and how the currency risk management changes in volatile and uncertain times.

Value-at-Risk (VaR) is among financial institutions a commonly used tool for measuring market risk. Several methods to calculate VaR exists and different implementations often results in different VaR forecasts. An interesting implementation is historical simulation, and the purpose of this thesis is to examine whether historical simulation with dynamic volatility updating is useful as a model to calculate VaR and how this differs in regard to type of asset or instrument. To carry out the investigation six different models are implemented, which then are tested for statistical accuracy through Christoffersens test. We find that incorporation of volatility updating into the historical simulation method in many cases improves the model. The model also generates good results compared to other commonly used models, especially if the volatility is predicted through a GARCH(1,1) updating scheme. / Value-at-Risk (VaR) är ett bland finansiella institutioner vanligt mått för att mäta marknadsrisk. Det finns ett flertal olika sätt att beräkna VaR, vilka ofta ger olika resultat beroende på förutsättningar. Ett av dessa är historisk simulering, och syftet med denna uppsats är att undersöka huruvida historisk simulering med dynamiskt uppdaterande volatilitet är en användbar modell för beräkning av VaR och hur dess lämplighet beror på valt tillgångsslag eller instrument. För att besvara detta implementeras sex olika modeller för beräkning av VaR, vilka sedan testas med hjälp av Christoffersens test. Vi finner att inkorporering av dynamisk volatilitet i historisk simulering i många fall medför en förbättring av modellen ifråga om statistisk riktighet. Vidare kan historisk simulering med dynamiskt uppdaterande volatilitet anses vara konkurrenskraftig i jämförelse med andra vanligt använda modeller, framförallt då volatiliteten skattas genom GARCH(1,1).

<p>Environmental policy is increasingly implemented via market mechanisms. While this is in many ways a great success for the economics profession, a number of questions remain. In this dissertation, I empirically explore the question of what will happen as environmental outcomes are coupled to potentially volatile market phenomena, whether policies can insulate environmental outcomes and market shocks, and policymakers should act to mitigate such volatility. I use a variety of empirical methods including reduced form and structural econometrics as well as theoretical models to consider a variety of policy, market, and institutional contexts. The effectiveness of market interventions depends on the context and on the policy mechanism. In particular, energy markets are characterized by low demand elasticities and kinked supply curves which are very flat below a capacity constraint (elastic) and very steep above it (inelastic). This means that a quantity-based policy that acts on demand, such as releasing additional pollution emission allowances from a reserved fund would be an effective way to constrain price shocks in a cap-and-trade system. However, a quantity-based policy that lowers the need for inframarginal supply, such as using ethanol as an oil product substitute to mitigate oil shocks, would be ineffective. Similarly, the benefits of such interventions depends on the macroeconomic impacts of price shocks from the sector. Relatedly, I show that a liability rule designed to reduce risk from low-probability, high-consequence oil spills have very low compliance costs.</p> / Dissertation

This paper provides an overview of the finite difference method and its application to approximating financial partial differential equations (PDEs) in incomplete markets. In particular, we study German’s [6] stochastic volatility PDE derived from indifference pricing. In [6], it is shown that the first order- correction to derivatives valued by indifference pricing can be computed as a function involving the stochastic volatility PDE itself. In this paper, we present three explicit finite difference models to approximate the stochastic volatility PDE and compare the resulting valuations to those generated by an Euler- Maruyama Monte Carlo pricing algorithm. We also discuss the significance of boundary condition choice for explicit finite difference models.

Forestry activities play a crucial role in climate change mitigation. To make carbon credits generated from such activities a tradable commodity, it is important to analyze the price dynamics of carbon markets. This dissertation contains three essays that examine various issues confronting world’s carbon markets. The first essay investigates cointegration of carbon markets using Johansen maximum likelihood procedure. All carbon markets of the world are not integrated. North American carbon markets show integration and so do the CDM markets. For future, the possibilities of arbitrage across world’s markets are expected to be limited, and carbon trading in these markets will be globally inefficient. There is a strong need of a global agreement that allows carbon trade to prevent climate change at the least cost options. The second essay evaluates various econometric models for predicting price volatility in the carbon markets. Voluntary carbon market of Chicago is relatively more volatile; and like other financial markets, its volatility is forecasted best by a complex non-linear GARCH model. The compliance market of Europe, on the other hand, is less volatile and its volatility is forecasted best by simple econometric models like Historical Averages and GARCH and hence is different from other markets. Findings could be useful for investment decision making, and for making choice between various policy instruments. The last essay focuses on agent based models that incorporate interactions of heterogeneous entities. Artificial carbon markets obtained from such models have statistical properties - lack of autocorrelations, volatility clustering, heavy tails, conditional heavy tails, and non-Gaussianity; which are similar to the actual carbon markets. These models possess considerably higher forecasting capabilities than the traditional econometric models. Forecast accuracy is further improved considerably through experimentation, when agent characteristics like wealth distribution, proportion of allowances and number of agents are set close to the real market situations.

Forestry activities play a crucial role in climate change mitigation. To make carbon credits generated from such activities a tradable commodity, it is important to analyze the price dynamics of carbon markets. This dissertation contains three essays that examine various issues confronting world’s carbon markets. The first essay investigates cointegration of carbon markets using Johansen maximum likelihood procedure. All carbon markets of the world are not integrated. North American carbon markets show integration and so do the CDM markets. For future, the possibilities of arbitrage across world’s markets are expected to be limited, and carbon trading in these markets will be globally inefficient. There is a strong need of a global agreement that allows carbon trade to prevent climate change at the least cost options. The second essay evaluates various econometric models for predicting price volatility in the carbon markets. Voluntary carbon market of Chicago is relatively more volatile; and like other financial markets, its volatility is forecasted best by a complex non-linear GARCH model. The compliance market of Europe, on the other hand, is less volatile and its volatility is forecasted best by simple econometric models like Historical Averages and GARCH and hence is different from other markets. Findings could be useful for investment decision making, and for making choice between various policy instruments. The last essay focuses on agent based models that incorporate interactions of heterogeneous entities. Artificial carbon markets obtained from such models have statistical properties - lack of autocorrelations, volatility clustering, heavy tails, conditional heavy tails, and non-Gaussianity; which are similar to the actual carbon markets. These models possess considerably higher forecasting capabilities than the traditional econometric models. Forecast accuracy is further improved considerably through experimentation, when agent characteristics like wealth distribution, proportion of allowances and number of agents are set close to the real market situations.

This thesis extends the application of the localized realized volatility model created by Ying Chen, Wolfgang Karl Härdle, and Uta Pigorsch to other futures markets, particularly the CAC 40 and the NI 225. The research attempted to replicate results though ultimately, those results were invalidated by procedural difficulties.