The U.S. National Market System (NMS), the largest marketplace in the world for securities and exchange traded funds, suffers from geographic market fragmentation which leads to reduced market efficiency. Communication lines transmit price updates and other information between geographically isolated exchanges at varying speeds, bounded above by the speed of light. Market participants have access to federally mandated information provided by the Securities Information Processor (SIP) and privately offered information provided by the exchanges, often called direct feeds. These feeds are quantitatively and qualitatively distinct, with the direct feeds tending to provide more information at a faster rate than the SIP feed. Differences between the SIP and direct feeds can lead to information asymmetries between market participants, which in turn create arbitrage opportunities. Under the market conditions of the NMS in 2016, these arbitrage opportunities occur regularly and many can be captured by market participants with fast connectivity. Several methods exist which allow market participants to reduce their communication latency with trading centers, including the practice of co-location where market participants pay to have their trading infrastructure located in the same building as the matching engines of an exchange. Such regularly occurring and executable arbitrage opportunities run counter to the Efficient-Market Hypothesis (EMH) in all forms, where even the weak form of the EMH claims that market participants should not be able to systematically profit from market inefficiencies. This thesis investigates the market inefficiencies and related effects introduced by geographic market fragmentation in two baskets of stocks: the Dow Jones Industrial Average (Dow), and the 30 largest stocks by market capitalization in the Standard \& Poor's 500 index (S&P 30).

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.

Recently, there are more and more literatures discuss on the issues of investment strategies of leveraged ETFs. In our works, we concentrate our issues on optimal leverage of ETF of S&P 500 index. Based on ARMA-GARCH model¡¦s assumption, we find out that the forecasting optimal leverage can be shown in a formula which contains return and characteristic function. In this paper, we use MA(1)-GARCH(1,1) to forecast volatility based on 1008 rolling window to forecast one day ahead¡¦s volatility; and our estimation time is start from 1954 to March 2011. In this paper, we present four dynamic leverage models (Normal, Student T, VG, and Best model¡¦s leverage) to find out the payoffs under these models. In our model, the forecasting accuracy is just about 55% which is slightly higher than SPX raise probability. But during long-term compound effect, the dynamic leverage models can out-perform than constant leverage. There may exist some important factors in these results, one of them is the crash forecasting ability. During 1980 to 2011 SPX has 14 big crashes and these models can effectively avoid 10 big crashes. In short-term investment horizon none of these five models are always outperform than others but in long-term investment horizon the strategy of best model¡¦s leverage can always earn money when investment horizon is 2400 days.

While the much more use of leverage could be effective in generating alpha o investment, the Kelly strategy is an attractive approach to capital creation and growth. It is originated from the Kelly criterion dubbed ¡§ fortunes formula ¡§ which maximizes the long run growth rate of wealth. There is a tradeoff of rate of return versus risk/volatility as a asymptotic function solution of leverage or position size determined by the application of EGARCH model in the different residual assumptions given by the Normal, Generalized Hyperbolic, and the Generalized Error distributions. No matter there is any timing ability in any strategy, risk management is much more important especially with many repeated trading. We present the performance and risk control of the leveraged ETFs tracked the S&P 500 index in the past ten years using optimal leverage strategy derived by the full Kelly and fraction Kelly criterion.

Magister Commercii - MCom / The notion of market efficiency posits that stock prices fully reflect all available information in a timely manner. The efficient market hypothesis (EMH) proposed by Fama (1970) systematically rules out the profitability of information driven investing, and implicitly promulgates a passive market capitalisation weighted investment strategy such as indexing. The appeal of passive strategies has largely been driven by the growth of passive tracking instruments, which allow investors to earn underlying index performance by purchasing a single security such as an exchange traded fund (ETF). On the contrary, proponents of behavioural finance suggest that investors are irrational and subject to psychological biases. Furthermore, the noisy market hypothesis of Siegel (2006) asserts that the deviations from the economic ideal of rationality proposed by the EMH, introduces noise in the market which could lead prices to deviate from their intrinsic values. The resultant drag in performance of market capitalisation weighted indices suggests that the optimal cap-weighted market portfolio promulgated by the modern portfolio theory (MPT) of Markowitz (1952), ceases to be the most mean-variance approach to asset allocation. With the goal of testing the applications of ETF’s, this study first evaluates the performance of passive sector ETF’s in the global equity market. In addition, motivated by the potential inefficiencies of capweighted portfolios, the study tests optimisation based asset allocation techniques, and technical analysis based market timing strategies. The study employs the S&P Global 1200 sector indices and their respective sector ETF’s to test their performances and applications in passive and active investment strategies, over the period from July 5th, 2002 to February 6th, 2015. The ETF’s are evaluated based on their tracking ability and price efficiency. All 10 sector ETF’s possess insignificant tracking errors and successfully replicate the performance of their underlying indices. In addition, the globalsector ETF’s are not price efficient over the study period, as they possess persistent price deviations from their net asset values (NAV’s). Furthermore, the ETF trading strategy based on the relationship between ETF returns and price deviations, proves to be effective in outperforming the passive buy and hold strategy in the majority of the sectors. The sector decomposition of the cap-weighted S&P Global 1200 index which is employed as the market proxy, reveals that its sector allocation remains fairly stable throughout the study period. In contrast, the optimal historical sector composition incurs large changes in sector exposure from year to year and provides substantially superior performance relative to the cap-weighted market portfolio. The cap-weighted portfolio tends to overweight cyclical sectors and underweight resilient sectors during major economic downturns. The long-only, long-short and market neutral strategies developed from the S&P Global 1200 index and its constituent sector indices provide exceptional risk-adjusted performance, and more meanvariance efficient portfolios than the cap-weighted market proxy. The relaxation of the longonly constraint also improves the optimised portfolios risk-adjusted performance, mainly through risk reduction benefits. The performance of the optimised global sector based portfolios also resembles the performances of the global style based optimised portfolios developed by Hsieh (2010), thereby suggesting that the two approaches are analogous. The 3 technical market timing strategies tested in this research provide varying results. The sector momentum portfolios experience significant positive returns during bull markets, however the portfolios incur significant drawdowns during periods of economic turmoil such as the 2008 global financial crisis. As a result, all sector momentum portfolios provide inferior risk-adjusted returns relative to the passive cap-weighted buy and hold strategy. The exponential moving average (EMA) trend timing strategy promulgated by Hsieh (2010) provides impressive risk-management attributes and superior risk-adjusted performance relative to passive buy and hold benchmarks. Similarly, the alternative technical charting heuristics trend timing strategy helps reduce drawdowns during market crashes, however the charting strategy provides inferior cost and risk-adjusted performance relative to the capweighted buy and hold approach due to larger timing errors and longer hedging periods in comparison to the EMA strategy. In addition, the global tactical sector allocation (GTSA) model tests the EMA and technical charting trend timing tools in the context of a global sector portfolio, and the model provides outstanding cost and risk-adjusted performances relative to the passive investing alternatives. The portfolio based GTSA model highlights the benefits of portfolio diversification and successfully hedges market exposure during economic downturns.

This study explores Bitcoin’s volatility characteristics using different extensions of the GARCH model. The volatility characteristics of bitcoin are compared with to a gold commodity and the S&amp;P 500 index. The purpose is to identify which model fits best for the data and to see how the volatility changes during the time period of 1st February 2017 to 1stFebruary 2022. The dataset is divided into two time periods, one prior to the pandemic which is the low uncertainty period and the other after the pandemic being the high uncertainty period. The attention for cryptocurrencies and especially bitcoin, has risen expeditiously the last couple of years, this makes the analysis appropriate and current for the market. The result showed that bitcoin’s volatility is more effected by the volatility of gold than for S&amp;P 500. The volatility shows that bitcoin was more similar to the behavior of the gold than the S&amp;P 500 prior to the pandemic. Further is there still no clearer explanation and bitcoins behavior cannot be stated as a commodities or financial asset. The GARCH model results showed that bitcoin’s volatility is persistent over time and can therefore be an explanation that will apply well as for the next years. The high volatility time periods of bitcoin can be explained by optimism and overestimate bias. The bias connected the overly confident investment decisions to less accurate rents. Bitcoin is still new on the financial market which makes new knowledge extremely important in order to create safer investment portfolios.

This study examines the Chicago Board Option Exchange (CBOE) Volatility Index (VIX) which is the implied volatility calculated from short-term option prices on the Standards & Poor’s 500 stock index (S&P 500). Findings suggest VIX overestimates average volatility by approximately 3% but explains 55% of S&P 500’s proceeding month’s volatility. The implied volatility (IV) from options on the VIX add additional explanatory power for the S&P’s 500 proceeding kurtosis values (a measure of tail risk). The VIX option’s volatility smirks did not add additional explanatory power for explaining the S&P 500 volatility or kurtosis. A simple trading rule based on buying the S&P 500 whether the VIX, IV from the options on the VIX, and the VIX option’s volatility smirk decline over the preceding month results in an additional 0.96% return in the following month. However, this only occurs approximately 10% of the time and does not outperform a simple buy-and-hold strategy as the strategy has the investor out of the market the majority of the time.

Can the law of the natural distribution of random numbers expose malice in financial markets? This thesis aims to analyze the indices S&amp;P 500 and STOXX 600, in an effort to identify days in which behavior in the market was the result of financial manipulation or non normal market movements. What was discovered by extending a previous study [10], was that we could accurately identify many days in which the market crashed or was affected by malpractice similar to the events in the 2007-2008 financial crisis.

This diploma thesis examines the possibilities of using the PE ratio in the creation of a stock portfolio on the Chinese and American stock market. The result of this work is the finding that low PE shares achieve higher risk-weighted returns over short and long investment horizons than shares with high PE values in both ex-amined markets. However, based on the detected volatility of the shares with the extreme values of PE, it is possible to recommend the use of this indicator for creation of the portfolio only to the most speculative investors.