Thesis advisor: Zhijie Xiao / This dissertation consists of three independent studies in Financial Economics. The first chapter focuses on the predictive power of the implied correlation index on the future S&P 500 Index returns. The second chapter investigates a nonlinear contemporary relationship between stock returns and oil price changes. The last chapter discusses the relationship between impact trading costs and a number of market factors that affect the costs. In the first chapter, I investigate the predictive power of the implied correlation index on the future S&P 500 Index returns. This new index was launched by Chicago Board Option Exchange following 2007-2008 financial crisis. As it is derived from the S&P 500 Index option price and the option prices of the largest 50 S&P Index stocks, it is widely regarded by market participants as a gauge of average expected future stock return correlation. Because of its role in measuring systematic risks, any changes in this index may provide useful information about the future market movements. Motivated by this index's forward-looking characteristic, I propose a linear regression where the future S&P 500 Index multi-period return is regressed on a number of controls such as the current period changes of S&P index and the implied correlation index etc. I use weekly data and three different sample splits for in-sample estimation and out-of-sample performance evaluation. I find that the implied correlation index is informative for the period 2007-2009 in predicting the S&P 500 Index returns of 28 to 39 weeks. My model consistently outperforms the random walk model using the Superior Predictive Ability test. This implied correlation index is also useful in predicting the S&P index future multi-week returns for the period 2009-2011 and a longer time span from 2007-2011. I also do a test for the Efficient Market Hypothesis by incorporating the implied volatility index in the regression. There is no evidence supporting the view that the market is efficient for those time periods. In the second chapter, I estimate a nonlinear contemporary relationship between stock returns and oil price shocks. Previous studies on this issue suffer a number of limitations. For example, they do not control the factors potentially driving the economy and the oil market simultaneously. Although, Kilian and Park (2009) does a good job in identifying the relationship by distinguishing different oil market shocks, they use a linear regression framework and do not address the contemporary relationship. Considering the different impacts of different-size oil shocks on stock returns, I propose a two-step estimation procedure for identifying their relationship. In the first step, I follow Kilian and Park's methodology, i.e. a structural vector autoregression, to estimate the demand-specific oil shocks. During the second step, I use a nonparametric quantile regression to estimate the relationship between stock returns and the estimated exogenous oil price shocks. This way, I can control for the factors that simultaneously drive the economy and the oil market and am able to identify a nonlinear relationship of stock returns with oil shocks at the same time. The result shows that different-size oil price changes do have quite different impacts on stock returns. I also find an asymmetric effect of large oil shocks on large stock returns. Specifically, the positive impact of the large negative oil shocks on stock returns is much bigger than the negative impact of the large positive oil shocks on stock returns. I carry out a robust check by running regressions for a number of different model setups and the result persists. I also compare my model with Kilian and Park' SVAR model and it turns out that my model is a big improvement on their model in explaining the stock return variations. The third and last chapter focuses on impact trading cost and its relationship with several market factors. In this chapter, I focus on one of financial market microstructure issues, the immediate impact trading cost for major NASDAQ stocks. The immediate impact cost is the extra cost that market traders pay when they execute a large volume transaction without delay during the time when the market is less liquid. Because the market depth is defined to be the market's ability to sustain relatively large market orders without impacting the price of the security, this cost is closely linked to the trading volume. When trading volume becomes large, market liquidity gets worse and therefore the relationship between immediate impact cost and trading volume is virtually nonlinear. People trading in the market are interested in this relationship because they hope to figure out the best strategies in the situation where they want to execute a large volume order when the market is not deep. Another measure of market depth or liquidity people often use is market spread. Because it is the compensation for market makers' willingness to hold an imbalanced portfolio when the market is not liquid, it is regarded as another important factor linked to the impact cost. In this chapter, I use a nonparametric model to estimate the unknown relationship between immediate impact cost and market factors such as trading volume, market spread etc. for the major NASDAQ stocks. The result shows that, for many stock transactions, there is a certain volume threshold of trading volume beyond which impact costs increase dramatically. I find that for 99% of trading, immediate execution is optimal. I also identify a negative relationship between the occurrence likelihood of a large trading cost and the stock market cap. / Thesis (PhD) — Boston College, 2012. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

<p>This thesis investigates the Day-of-the-week, Month-of-the-year and Quarter-of-the-year effects. Historical data from the S&P 500 index between 1970- 2005 is analyzed. The purpose is to investigate if there is any evidence of increased returns (ROR) pattern related to seasonality during this period. The conclusion is that Wednesdays, December and Quarter 4 have had the highest ROR while Mondays, September and Quarter 3 have had the lowest ROR.</p><p>The empirical analysis found support for the Monday effect that Mondays are the days with the lowest stock returns. An investor would have earned on approximately four times more if you invested on Wednesdays instead of Mondays. Mondays was the only days with a negative ROR. I also found support for the weekend effect that return on Fridays are higher than returns on Mondays. Based on the empirical analysis a mid-of-the-week effect or Wednesday effect is also present.</p><p>No support was found for the January effect that stock prices should be higher in January than in December. What I however clearly could see was a September effect. September is the only month with negative returns. You would have on earned approximately three times as much if you invest in the beginning of December instead of the beginning of September. This leads to that the quarter 3 should be avoided due to a negative historical ROR.</p>

This thesis investigates the Day-of-the-week, Month-of-the-year and Quarter-of-the-year effects. Historical data from the S&amp;P 500 index between 1970- 2005 is analyzed. The purpose is to investigate if there is any evidence of increased returns (ROR) pattern related to seasonality during this period. The conclusion is that Wednesdays, December and Quarter 4 have had the highest ROR while Mondays, September and Quarter 3 have had the lowest ROR. The empirical analysis found support for the Monday effect that Mondays are the days with the lowest stock returns. An investor would have earned on approximately four times more if you invested on Wednesdays instead of Mondays. Mondays was the only days with a negative ROR. I also found support for the weekend effect that return on Fridays are higher than returns on Mondays. Based on the empirical analysis a mid-of-the-week effect or Wednesday effect is also present. No support was found for the January effect that stock prices should be higher in January than in December. What I however clearly could see was a September effect. September is the only month with negative returns. You would have on earned approximately three times as much if you invest in the beginning of December instead of the beginning of September. This leads to that the quarter 3 should be avoided due to a negative historical ROR.

This thesis aims to identify the links between economic developments and move-ments in US stock markets. The period from Q1 2001 to Q3 2016 inclusive was chosen for an analysis of the relationship between trends in GDP time series and the S&P 500 index. A correlation analysis was carried out on the time series ex-amined, while the Granger causality test was used to determine the causal rela-tionship between them.

AN ABSTRACT OF THE THESIS OFSamridha Jung Rana, for the Master of Science degree in Economics, presented on November 10, 2022, at Southern Illinois University Carbondale.TITLE: MACROECONOMICS AND ANAMOLIES AS DETERMINANTS OF STOCK RETURNSMAJOR PROFESSOR: Dr. Scott GilbertAbstract: There is no general support to explain the strong correlation between the macroeconomic variables and the Standard & Poor 500 index fund returns. This thesis sheds some light on how the macroeconomic variables have impacted the monthly returns on the Standard & Poor 500 over the last decade. Firstly, we introduce the Standard & Poor 500 index and various macroeconomic factors influencing the U.S. economy over the years. Subsequently, investigating the casualty relationship between the monthly rate of returns, the consumer-producer index, the industrial producer index, Money Supply, Unemployment, inflation rate, and the exchange rate. The methodology used in this study includes a stepwise multiple regression model, Johansen cointegration test, Dickey-fuller augmented test, Phillip perron test, and the Granger Causality test. Furthermore, investigating stock market anomalies that have been verified immensely, such as the day-of-the-week Effect and month-of-the-year Effect, has also been explored to see whether those anomalies still exist in recent times.

This research paper revolves around the world’s oldest financial asset, gold, and whatdrives its price, which is of importance for all investors looking to be exposed to gold.The aim of this paper is to identify the main drivers behind the gold price, whichis done by performing a multiple linear regression analysis on the gold price and aset of explanatory variables. The results show that the real yield, measured as theTIPS-rate, has the largest impact on the gold price, followed by the inflation rate.The conclusion that is drawn in the paper is that it is reasonable that the real yield isthe main driver of the gold price, because the higher the real yield, the less attractiveit becomes for investors to own gold, as it is not an interest-bearing asset. / Den här uppsatsen handlar om väarldens äldsta finansiella tillgåang, guld, och vad som driver dess pris, vilket är till nytta för alla investerare som ämnar vara exponerade mot guld. Syftet med uppsatsen är att identifiera de huvudsakliga drivkrafterna bakom guldpriset, vilket görs genom att utföra en multipel linjär regressionsanalys på guldpriset och ett antal förklaringsvariabler. Resultatet visar att realräntan, mätt i form av TIPS-räntan, har störst påverkan på guldpriset, följt av inflationstakten. Slutsatsen som dras i uppsatsen är att det är rimligt att realräntan har störst påverkan på guldpriset, i och med att ju högre realränta, desto mindre attraktivtblir det för investerare att äga guld, då guld inte är en räntebärande tillgång.

<p>The purpose of this study is to examine what affects the changes in credit spreads. A</p><p>regression model was performed where the explanatory variables were; volatility,</p><p>SP&500 index, interest-rate level the slope of yield curve and the dependent</p><p>variable was credit spread for each of CSUSDA, CSUSDBBB, and CSUSDB. We</p><p>found a positive correlation between these independent variables (Volatility, S&P</p><p>500index) and a negative correlation between interest-rate level and credit spreads.</p><p>These results were consistent with our hypothesis. However, the link between the</p><p>slope of yield curve and credit spreads was positive and that was inconsistent with</p><p>our hypothesis and some previous studies. The conclusion of this paper was a</p><p>change in credit spread is related to the variables that we used in our model. And</p><p>these variables explained about 50 per cent of this change.</p>

Thesis advisor: Harold A. Petersen / This paper attempts to find the extent of the predictive power of the stock market in relation to consumption, non-residential investment, and corporate profits. Initially, a naïve model is formulated to assess the impact of the stock market on GDP, and then the model is used to find the predictive power of the stock market on the components. This component analysis compares the impact of the market on each of the components and attempts to find reasons for the variations in impact. Finally, the long term predictive power of the various models is assessed. / Thesis (BS) — Boston College, 2005. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Economics Honors Program.

This volume contains four essays on various topics in the field of financial econometrics. All four discuss the properties of high frequency financial data and its implications on the model choice when an estimate of the capital asset return volatility is in focus. The interest lies both in characterizing "stylized facts" in such series with time series models and in predicting volatility. The first essay, entitled A Survey of Recent Papers Considering the Standard &amp; Poor 500 Composite Stock Index, presents recent empirical findings and stylized facts in the financial market from 1987 to 1996 and gives a brief introduction to the research field of capital asset return volatitlity models and properties of high frequency financial data. As the title indicates, the survey is restricted to research on the well known Standard &amp; Poor 500 index. The second essay, with the title, Stylized Facts of Daily Return Series and the Hidden Markov Model, investigates the properties of the hidden Markov Model, HMM, and its capability of reproducing stylized facts of financial high frequency data. The third essay, Modelling the Conditional Mean and Conditional Variance: A combined Smooth Transition and Hidden Markov Approach with an Application to High Frequency Series, investigates the consequences of combining a nonlinear parameterized conditional mean with an HMM for the conditional variance when characterization of stylized facts is considered. Finally, the fourth essay entitled, Volatility Forecasting for Option Pricing on Exchange Rates and Stock Prices, investigates the volatility forecasting performance of some of the most frequently used capital asset return volatility models such as the GARCH with normal and t-distributed errors, the EGARCH and the HMM. The prediction error minimization approach is also investigated. Each essay is self-contained and could, in principle, be read in any order chosen by the reader. This, however, requires a working knowledge of the properties of the HMM. For readers less familiar with the research field the first essay may serve as an helpful introduction to the following three essays. / <p>Diss. Stockholm : Handelshögsk.</p>

The purpose of this study is to examine what affects the changes in credit spreads. A regression model was performed where the explanatory variables were; volatility, SP&amp;500 index, interest-rate level the slope of yield curve and the dependent variable was credit spread for each of CSUSDA, CSUSDBBB, and CSUSDB. We found a positive correlation between these independent variables (Volatility, S&amp;P 500index) and a negative correlation between interest-rate level and credit spreads. These results were consistent with our hypothesis. However, the link between the slope of yield curve and credit spreads was positive and that was inconsistent with our hypothesis and some previous studies. The conclusion of this paper was a change in credit spread is related to the variables that we used in our model. And these variables explained about 50 per cent of this change.