This paper identifies America's first Great Moderation, a period from 1841-1856 of unbroken economic expansion and low volatility comparable to the Great Moderation of the 1980s-2000s. This moderation occurred despite a lack of central banks, low governmental spending, and barriers to interstate commerce during the antebellum period. I demonstrate this moderation in industrial production and stock market indexes and compare the first Great Moderation with the second in these economic factors. These results also call into question the conventional wisdom of the National Bureau of Economic Research business cycle chronology that the antebellum period was volatile and fraught with recessions. I then identify several possible causes of this stable growth in the effects of cotton prices, technological revolutions such as railroads, and wage and interest rate integration during the period, among other factors. Understanding these factors helps develop our understanding of the American antebellum economy and the causes of economic growth and stability, especially during these Great Moderations.

In this paper, we explore the relationship between a Global Mood Time Series, provided by Wall Street Birds, and the Dow Jones Industrial Average (DJIA) from April 2011 to December 2011. My econometric results show that there is no long run equilibrium relationship between the level of global mood and the level of the DJIA. These results apply to the whole period, as well as in the six-month subperiods. Furthermore, daily changes in global mood do not Granger cause DJIA returns. However, changes in global mood do appear to be useful in forecasting the volatility of the DJIA, and my results suggest that GARCH models of volatility of large-cap indexes, and potentially the market as a whole, could be strengthened by including online sentiment measures of Big Data. Measuring global mood, and quantifying its impacts, can potentially lead to superior portfolio construction as forecasting volatility is an important input in portfolio optimization. The results, as a whole, suggest that Big Data can have important implications for investment decision-making.

This paper will assess the relationship between tenant characteristics and public REIT volatility. Specifically, we focus on the retail REIT subset of the industry. Given that retail REITs are one the most transparent asset classes, they provide an interesting landscape for evaluating the relationship between the firm and the customers, or in this case, the tenants. Specifically, we assess how major tenant ownership, public or private equity owned, impacts the volatility of the REIT’s stock price using 2010 data on 30 retail REITs. Controlling for tenant credit quality, leverage, ROE, book-to-market, size, age, region and property focus, we find that a higher percentage of rental revenue from private equity owned tenants is associated with lower REIT stock volatility, and a higher percentage of rental revenue from publicly owned tenants is associated with higher REIT stock volatility.

The thesis studies the martingale properties, probabilistic methods and efficient unbiased Monte Carlo simulation methods for various time-homogeneous diffusion models commonly used in mathematical finance. Some of the popular stochastic volatility models such as the Heston model, the Hull-White model and the 3/2 model are special cases. The thesis consists of the following three parts: Part I: Martingale properties in time-homogeneous diffusion models: Part I of the thesis studies martingale properties of stock prices in stochastic volatility models driven by time-homogeneous diffusions. We find necessary and sufficient conditions for the martingale properties. The conditions are based on the local integrability of certain deterministic test functions. Part II: Analytical pricing methods in time-homogeneous diffusion models: Part II of the thesis studies probabilistic methods for determining the Laplace transform of the first hitting time of an integral functional of a time-homogeneous diffusion, and pricing an arithmetic Asian option when the stock price is modeled by a time-homogeneous diffusion. We also consider the pricing of discrete variance swaps and discrete gamma swaps in stochastic volatility models based on time-homogeneous diffusions. Part III: Nearly Unbiased Monte Carlo Simulation: Part III of the thesis studies the unbiased Monte Carlo simulation of option prices when the characteristic function of the stock price is known but its density function is unknown or complicated.

This paper examines the impact of USD and EUR exchange rates on the Swedish stock market performance for different economic sectors over a time period of ten years (2003-2013). The growing integration between foreign exchange markets and stock markets with the wide spread use of hedging and diversification policies made it necessary to test the degree of impact these two distinct markets share between each other. Number of studies, were done studying the relationship between the exchange rates and stock performance combining and comparing different economies and currencies. Nevertheless, research gap prevailed when it came at the point of the studying the relationship on Swedish stock and foreign exchange market. The research was conducted with the quantitative method. Initially we have tested how the performance of Swedish stock market is correlated with the return of the USD and EUR in different economic sectors over different time periods. Later, we try to investigate if there is any spillover effect flows from the exchange market to the Swedish stock market. The Pearson’s correlation coefficient and GARCH (1,1) model were applied to study the correlation and spillover effect between the exchange and stock return respectively. Our empirical study showed that there is very low correlation which is statistically insignificant between the two different markets. Correlations were found to be significantly varied across the different economic sectors in different time periods. Moreover empirical study supported that the spillover effect exists and showed that movement of exchange rates will affect the future performance of stock market. The significant conclusions were that USD and EUR can be used as portfolio diversification and during the volatile exchange market, investors should diversify or hedge their risk domestically and vice versa. The implications of this finding is particularly very important for the portfolio managers when devising their hedging policies and diversifying their portfolios in order to minimize their unsystematic risk.

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable candidates is then selected accordingly, which may explain price behavior in this market. In the process of selecting the explanatory variable candidates, some important issues, such as direct or indirect effects of the variables on price behavior, availability of the variables before real-time, choice of appropriate forecasting horizon and market time-line, are taken into account. Price and demand in three neighboring electricity markets, namely, the New York, New England, and PJM electricity markets, are also considered among the explanatory variable candidates. Electricity market clearing prices in Ontario are calculated every five minutes. However, the hourly average of these 5-minute prices, referred to as the Hourly Ontario Energy Price (HOEP), applies to most Ontario market participants for financial settlements. Therefore, this thesis concentrates on forecasting the HOEP by employing various linear and non-linear modeling approaches. The multivariate Transfer Function (TF), the multivariate Dynamic Regression (DR), and the univariate Auto Regressive Integrated Moving Average (ARIMA) are the linear time series models examined. The non-linear approaches comprise the Multivariate Adaptive Regression Splines (MARS), and the Multi-Layer Perceptron (MLP) neural networks. Multivariate HOEP models are developed considering two forecasting horizons, i.e. 3 hours and 24 hours, taking into account the case market time-line and the ability of market participants to react to the generated forecasts. Univariate ARIMA models are also developed for day-ahead market prices in the three neighboring electricity markets. The developed models are used to generate price forecasts for low-demand, summer peak-demand, and winter peak-demand periods. The HOEP forecasts generated in this work are significantly more accurate than any other available forecast. However, the accuracy of the generated HOEP forecasts is relatively lower than those of the price forecasts for Ontario's neighboring electricity markets. The low accuracy of the HOEP forecasts is explained by conducting a price volatility analysis across the studied electricity markets. This volatility analysis reveals that the Ontario electricity market has the most volatile prices compared to the neighboring electricity markets. The high price volatility of the Ontario electricity market is argued to be the direct result of the real-time nature of this market. It is further observed that the inclusion of the just-in-time publicly available data in multivariate HOEP models does not improve the HOEP forecast accuracy significantly. This lack of significant improvement is attributed to the information content of the market data which are available just-in-time. The generated HOEP forecasts are used to plan the short-term operation of two typical demand-side case-study BEMCs. The first case-study BEMC is a process industry load with access to on-site generation facilities, and the second one is a municipal water plant with controllable electric demand. Optimization models are developed for the next-day operation of these BEMCs in order to minimize their total energy costs. The optimization problems are solved when considering market price forecasts as the expected future prices for electricity. The economic impact of price forecast inaccuracy on both the case study is analyzed by introducing the novel Forecast Inaccuracy Economic Impact (FIEI) index. The findings of this analysis show that electricity market price forecasts can effectively be used for short-term scheduling of demand-side BEMCs. However, sensitivity to price forecast inaccuracy significantly varies across market participants. In other words, a set of price forecasts may be considered ``accurate enough'' for a customer, while leading to significant economic losses for another.

In this thesis I discuss the method of time-change and its applications in quantitative finance. I mainly consider the time change by writing a continuous diffusion process as a Brownian motion subordinated by a subordinator process. I divide the time change method into two cases: deterministic time change and stochastic time change. The difference lies in whether the subordinator process is a deterministic function of time or a stochastic process of time. Time-changed Brownian motion with deterministic time change provides a new viewpoint to deal with option pricing under stochastic interest rates and I utilize this idea in pricing various exotic options under stochastic interest rates. Time-changed Brownian motion with stochastic time change is more complicated and I give the equivalence in law relation governing the ``original time" and the ``new stochastic time" under different clocks. This is readily applicable in pricing a new product called ``timer option". It can also be used in pricing barrier options under the Heston stochastic volatility model. Conclusion and further research directions in exploring the ideas of time change method in other areas of quantitative finance are in the last chapter.

Due to the sharpen regulation of the Swedish plain vanilla warrant in 2006 and the recent increase in trade among private investors, this thesis examined the informa-tion efficiency of Swedish plain vanilla warrants. This was done in three different ways. First the theoretical Black & Scholes (B&S) price was tested against the ac-tual market price. Secondly likelihood ratio test statistics was used to see whether information regarding past returns added any information to that already captured by the implied volatility (IV) generated from observed warrant market prices via the B&S model. The third method used was a comparison of the IV´s among com-parable warrants. As the regulation of the Swedish plain vanilla warrant market states that only certified issuer are allowed short calls and puts, the self adjusting price mechanism found in the option market doesn’t exist on this market. As a con-sequence of this, investors on this market is reliant of accurate ask and bid prices from the issuers. Further, the information efficiency of a capital market is of es-sence for capital allocation, price discovery and risk management. The results from all three tests rejected the information efficiency hypothesis of the sample. Thus concluding that the included warrants in this thesis are none ideally for activities such as capital allocation, price discovery and risk management.

The purpose of this thesis is to evaluate five different volatility forecasting models that are used to calculate financial market risk. The models are used on both daily exchange rates and high-frequency intraday data from four different series. The results show that time series models fitted to high-frequency intraday data together with a critical value taken from the empirical distribution displayed the best forecasts overall.

Bakgrund/Motiv: Historiskt sett tillämpades vanligen fast prissättning vid börsintroduktioner vilket innebar att aktierna ofta blev underprissatta och det var lätt för investerare att generera hög avkastning första handelsdagen. Numera används i större utsträckning anbudsförfarande och intervallprissättning där investerare lämnar anbud om pris och antal vilket har minskat underprissättningen. Studien utgår från att en del av marknadens förväntningar inkluderas i priset vid intervallprissättning vilket i sin tur skulle minska aktiens volatilitet efter introduktion. Syfte: Syftet med denna studie är att undersöka om aktiens volatilitet skiljer sig efter att den introducerats beroende på vilken av de två metoderna som använts för att prissätta aktien samt hur val av prissättningsmetod påverkar en aktiens underprissättning och avkastning efter introduktionen. Genomförande: Studien består av kvantitativa historiska data i form av aktiekurser och övrig information från de prospekt som upprättats i samband med bolagens introduktion på börsen. Utöver bearbetning av data och analyser i Excel har ett flertal ekonometriska tester genomförts med hjälp av ickelinjära regressionsanalyser där prissättningsmetod, betavärde, underprissättning och varians testats som beroende variabel mot ett flertal kombinationer av förklarande variabler. Slutsats: Studien visar att bolag som tillämpat fast prissättning uppvisar högre volatilitet efter börsintroduktion och att valet av prissättningsmetod därmed har en viss påverkan på volatiliteten. Vidare kan det konstateras att dessa bolag generellt varit mer underprissatta och genererat högre avkastning det första handelsåret. / Background: In the past, most companies performing an Initial Public Offering, IPO, applied the fixed pricing method, which often lead to an extensive underpricing of the shares. By doing so, it was easy for investors to gain high return on the first trading day. Nowadays, companies use auction pricing to a greater extent where investors bid for a certain amount of shares to a certain price. This procedure has resulted in a decrease of the underpricing. With the assumption that some of the market’s expectations are included in the price, whilst using an auction pricing method, these stocks would possibly appear less volatile after the IPO. Purpose: The aim of this study is to investigate whether the volatility of the shares is different after the introduction on the market, based on which method that has been applied when pricing the shares. The thesis also investigates to what extent the choice of pricing method influences the underpricing and returns of a share after its introduction. Method: The study comprises quantitative historical data, such as share prices as well as additional information gathered from the prospectus of each IPO. In addition to arranging the data and the analyses, made in Excel, numerous econometric analyses have been made by using non-linear regressions, where variables such as pricing method, beta, underpricing on the first trading day, and variance have been examined as a dependent variable in relation to several different combinations of explanatory variables. Findings: The study finds that companies that have practiced a fixed pricing method show a higher volatility after the introduction on the market. Thus, the choice of either pricing method has some influence on the volatility. Furthermore, it was proved that companies using a fixed pricing method were more underpriced and gained higher returns during the first year of trading compared to companies using an auction pricing method.