An initial public offering (IPO) represents a significant event in a firm’s lifecycle, marking the transition from being a privately held company to a publicly traded entity by offering its shares to the public for the first time. Several previous studies have shown that, from an investor point of view, IPOs posits the opportunity to earn substantial return, and that they also tend to underperform long-term. In recent years, stock market volatility has fluctuated considerably due to factors such as the global pandemic and geopolitical conflicts. These factors have led to varying stock market returns, affecting individuals' savings. Additionally, the number of investors in Sweden has grown substantially over the past decade. This, combined with the relatively unexplored nature of market volatility in IPO research, has laid the foundation for this study's focus. Therefore, the purpose of this study is to assess the impact market volatility has on the initial return and the long-term risk-adjusted return of IPOs in Sweden.  To fulfill this purpose, analyses have been undertaken to investigate the relationship between IPO short- and long-term returns and market volatility between 2019 and 2022. This timeframe encapsulates two years experiencing low market volatility (2019 and 2021), and two years experiencing higher market volatility (2020 and 2022). The data sample consists of 165 firms when measuring short-term returns, and 162 firms when measuring long-term returns, who have all had their IPO within this timeframe and are all listed on the Swedish stock market. To further contribute to the literature, the study incorporates the Efficient Market Hypothesis (EMH), Prospect Theory, and the Winner´s Curse Theory. These are three well-established and contrasting theories within IPO research which are introduced to see how well their perspectives align with the study's findings.  The empirical results from the statistical analyses showed varied outcomes. While a statistically significant difference could be identified between certain years, the majority did not. Since the majority of the tests conducted could not find a significant difference in return between high and low volatile years, market volatility at the time of an IPO does not significantly influence the return. Consequently, the findings suggest that employing an investment strategy that involves investing in IPOs based on market volatility levels is not superior to other strategies. These findings give investors deeper insights into how IPOs and their timing are influenced by market conditions and can therefore aid them in making more informed decisions.

Yes / This paper investigates the impact of investor sentiment on the mean-variance relationship in 14 European stock markets. Applying three approaches to define investors’ neutrality and determine high and low sentiment periods, we find that individual investors’ increased presence and trading over high-sentiment periods would undermine the risk-return tradeoff. More importantly, we report that investors’ optimism (pessimism) is more determined by their normal sentiment state, represented by the all-period average sentiment level, rather than the neutrality value set in sentiment surveys.

This empirical study uses stochastic coefficients econometric modeling to forecast real exchange rate volatility and examine how expected and unexpected volatility affect bilateral trade flows of certain steel products between Canada, Mexico and the United States using monthly data for the seven-year period 1996-2002. The results of the model indicate that the effects of exchange rate volatility on bilateral trade flows for this sector are relatively minor, where sustained changes in the spot exchange rate, sectoral economic growth, and the price of goods being traded all exert more significant influence on trade levels than exchange rate volatility. However, the model results also tend to indicate that as exchange rate volatility increases, the well-developed U.S.-Canadian forward currency exchange market may present economic agents with profit opportunities through risk-portfolio diversification, resulting in a positive correlation between volatility and trade. For the less-developed U.S.-Mexican forward currency market, the model results indicate that the relationship between trade and volatility, both expected and unexpected, is weak and predominantly negative. / Master of Arts

Yes / It is believed that financial markets are integrated and sensitive to news – including political conflicts in some regions of the world. Furthermore, financial markets seem to react differently to information flows from one region to another. The purpose of this research is to discern the effects of the recent Middle East and North Africa (MENA) conflicts – commonly referred to as the Arab Spring – on the volatility of risks and returns of global and regional stock markets as well as Gold and Oil markets. To be more specific, we consider the main uprisings in Tunisia, Egypt, Libya and Yemen and their impact on financial markets – as measured by the volatility of their risks and returns. In sum, we cluster 53 stock markets into 6 regions; namely, developed, developing, MENA, Asia, Europe, and Latin America countries, and use T-GARCH to assess the reaction of these regions to each uprising event independently. In addition, we use GARCH-M to assess the reaction of these regions stock markets as well as Gold and Oil markets to the uprisings of MENA as a whole. Our empirical findings suggest that the uprising events of MENA have more impact on the volatility of risks and returns of developed, developing, and Europe regions than MENA itself. In addition, although the results show that the volatility of both risks and returns of both developed and MENA regions are significantly affected by general conflicts in MENA, the volatility of MENA is affected during all intervals and with higher significance level. Furthermore, while MENA uprisings as a whole impact on the volatility of risk of oil (after 5 days) and gold (immediately after entering news) significantly, the returns of these markets are not affected by conflicts.

Traditional online measurements of the chemical composition and other physicochemical properties (such as volatility and oxygenation) of particulate matter have relied on expensive and complex research-grade instrumentation based on mass spectrometry and/or chromatography. However, routine monitoring requires lower-cost alternatives that can be operated autonomously, and such tools are lacking. Routine monitoring of particulate matter, especially organic aerosol, relies instead on offline techniques such as filter collection that require significant operator effort. To address this gap, first, we built a new online semi-continuous aerosol chemical composition monitor, the "ChemSpot", that provides information on volatility-resolved organic carbon and degree of oxygenation along with sulfur content at relatively moderate costs. Autonomous operation of the ChemSpot instrument was demonstrated for four weeks alongside a mass spectrometer (an Aerosol Chemical Speciation Monitor, or ACSM), and the results of the comparison were encouraging. Mean absolute percentage errors (MAPE) were estimated to be 21% and 27% for aerosol organic carbon and equivalent sulfate (equivalent amount of sulfate for ChemSpot measured sulfur content). Chemspot-measured oxygen-to-carbon ratio (O:C) compared well with ACSM-measured O:C for moderate aerosol loadings. Second, we extended the capability of the ChemSpot instrument to provide volatility distributions of organic aerosols. A thermogram-based method was developed for the ChemSpot for volatility calibration and the calculation of volatility distributions. This work also highlighted the need for better observational constraints on vapor pressure values from structure-activity relationship based models. Finally, the ChemSpot was deployed at a biomass-burning experiment (Georgia Wildfire Simulation Experiment, G-WISE) to show the utility of this instrument in studying changes in volatility distributions of Biomass Burning Organic Aerosols (BBOA) produced from different biomass fuel types (samples from Blue Ridge and Coastal Plains eco-regions of the state of Georgia), different burn conditions (prescribed burning vs. wild burning) and simulated atmospheric aging. Significant changes in the volatility distributions of organic carbon were observed for the two biomass fuel types studied. Prescribed burning led to the formation of some higher volatility organic compounds in the aerosols compared to the wild burning case. A similar but more pronounced observation of the formation of higher volatility organics was observed after the simulated atmospheric aging of the BBOA samples. The formation of these higher volatility organics could be because of the presence of higher moisture content during the prescribed burning conditions. The successful completion of these objectives provides confidence that the ChemSpot could be a viable tool for long-term data collection of aerosol composition and volatility and in turn advancing aerosol science and helping policymakers devise strategies to curb air pollution. / Doctor of Philosophy / Aerosols are fine particles suspended in the air, either emitted directly or formed through chemical reactions in the atmosphere. A significant fraction of the aerosols is made of thousands of organic compounds, making it difficult to study their composition and properties. Aerosols have been found to have significant impacts on human health, atmospheric visibility, radiative balance, cloud formation and, climate change. These effects vary depending upon the composition of aerosols and their ability to remain in the particle phase or get vaporized to the gas phase (also known as volatility). Traditional automated measurements of aerosol composition and volatility often rely on either the direct use of complex research-grade instrumentation or offline measurements collecting samples on a filter followed by analysis utilizing the same research-grade instruments. These approaches can be extremely expensive and/or labor-intensive, often making collection of long term data unfeasible. Some lower-cost alternatives exist but do not provide enough information on aerosol chemical composition. Essentially, there is a lack of an automated aerosol composition monitor which can run without significant operator effort and provide valuable data at moderate costs. To address this need, first, we designed a new instrument "ChemSpot" that runs autonomously for extended periods of time. We also validated its performance against a time-tested research-grade instrument. Comparisons with the research-grade instrument were found to be satisfactory. Second, we developed a method to estimate the amount of organic carbon based on its ability to evaporate at different temperatures (termed volatility distribution). This work also highlighted the need to have better observational constraints on the vapor pressure data from different models accounting for the structure of these organic compounds. Finally, we deployed the ChemSpot instrument at a simulated wildfire experiment (Georgia Wildfire Simulation Experiment or G-WISE) to study the effects of different fuel types (samples from Blue Ridge and Coastal Plains eco-regions of the state of Georgia), different burn conditions (prescribed burning vs. wild burning) and simulated atmospheric reaction. Different fuel types and atmospheric reactions were found to have more significant effects on the aerosol composition and volatility distribution of the organic carbon. The successful completion of these objectives provides confidence that the ChemSpot instrument could be a viable tool for long-term data collection of aerosol composition and volatility and in turn advancing aerosol science and helping policy-makers devise strategies to curb air pollution.

Informed traders trade options on underlying securities to lower transaction costs and increase financial leverage for price trend and variance strategies. Options markets play a significant role in price discovery by incorporating private information about future prices for an underlying security into option prices. I generate a new model-free volatility measure to calculate the "distance from arbitrage bounds" from minute-by-minute option series for the S&P 500 index and 30 individual underlying stocks. These iron butterfly arbitrage bounds (IBBs) use intraday call and put option prices from the Bloomberg database. Narrow and wide IBBs are expected to reveal the options market valuation of volatility by market participants. Data series is gathered by using successive one-minute intervals from the Bloomberg database. The data comprise the most recent bid and ask option prices and volumes. I collect S&P 500 index values and index options and use 30 underlying stock prices and option prices for the contracts that have the largest option trading volume during the sampling interval. These bid and ask prices reflect the information generated by intraday price pressures implied by S&P 500 index options or stock options. Consistent with the option micro-structure literature, I find that the IBB measure for actively traded stock options attains its highest level immediately after the open of the market, declines steadily throughout the first trading hour and remains relatively stable until market close. However, index IBBs behave differently. S&P 500 index option IBB attains its lowest level during the first hour of the trading day, then increases and remains relatively stable until market close. I present new evidence regarding the dynamic relation between stock returns and innovations in expected volatility by using the minute-by-minute change in implied volatility (IV) as a proxy. Unlike the relationship between individual stock returns and their respective changes in implied idiosyncratic volatility, I find that all the coefficients on the market volatility index (VIX) term are negative and significant. Therefore, the evidence supports the explanation that the negative relationship between stock returns and expected volatility innovations is primarily related to the systematic component of the expected volatility. I also test whether narrow and wide IBB values capture incremental information to explain the return-volatility relationship. Results indicate that neither narrow IBB nor wide IBB values provide additional information beyond that provided by VIX and IV. The results are robust to five-minute and ten-minute sampling frequencies.

<p>This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analysis.</p><p>The first paper of the thesis considers point estimation in a nonnegative, hence non-Gaussian, AR(1) model. The parameter estimation is carried out using a type of extreme value estimators (EVEs). A novel estimation method based on the EVEs is presented. The theoretical analysis is complemented with Monte Carlo simulation results and the paper is concluded by an empirical example.</p><p>The second paper extends the model of the first paper of the thesis and considers semiparametric, robust point estimation in a nonlinear nonnegative autoregression. The nonnegative AR(1) model of the first paper is extended in three important ways: First, we allow the errors to be serially correlated. Second, we allow for heteroskedasticity of unknown form. Third, we allow for a multi-variable mapping of previous observations. Once more, the EVEs used for parameter estimation are shown to be strongly consistent under very general conditions. The theoretical analysis is complemented with extensive Monte Carlo simulation studies that illustrate the asymptotic theory and indicate reasonable small sample properties of the proposed estimators.</p><p>In the third paper we construct a simple nonnegative time series model for realized volatility, use the results of the second paper to estimate the proposed model on S&P 500 monthly realized volatilities, and then use the estimated model to make one-month-ahead forecasts. The out-of-sample performance of the proposed model is evaluated against a number of standard models. Various tests and accuracy measures are utilized to evaluate the forecast performances. It is found that forecasts from the nonnegative model perform exceptionally well under the mean absolute error and the mean absolute percentage error forecast accuracy measures.</p><p>In the fourth and last paper of the thesis we construct a multivariate extension of the popular Diebold-Mariano test. Under the null hypothesis of equal predictive accuracy of three or more forecasting models, the proposed test statistic has an asymptotic Chi-squared distribution. To explore whether the behavior of the test in moderate-sized samples can be improved, we also provide a finite-sample correction. A small-scale Monte Carlo study indicates that the proposed test has reasonable size properties in large samples and that it benefits noticeably from the finite-sample correction, even in quite large samples. The paper is concluded by an empirical example that illustrates the practical use of the two tests.</p>

The present thesis focuses on exploration of the applicability of realized measures in volatility modeling and forecasting. We provide a first comprehensive study of jump variation impact on future volatility of Central and Eastern European stock markets. As a main workhorse, the recently proposed Realized Jump GARCH model, which enables a study of the impact of jump variation on future volatility forecasts, is used. In addition, we estimate Realized GARCH and heterogeneous autoregressive (HAR) models using one-minute and five-minute high frequency data. We find that jumps are important for future volatility, but only to a limited extent due to the high level of information aggregation within the stock market index. Moreover, Realized (Jump) GARCH models outperform the standard GARCH model in terms of data fit and forecasting performance. Comparison of forecasts with HAR models reveals that Realized (Jump) GARCH models capture higher portion of volatility variation. Eventually, Realized Jump GARCH compared to other Realized GARCH models provides comparable or even better forecasting performance.

Master of Science / Department of Horticulture, Forestry, and Recreation Resources / Steven Keeley / Common dandelion (Taraxacum officinale Weber) is an important perennial weed in turfgrass. Fall is considered the optimal time for postemergence herbicidal control of dandelions; however, applications in spring, when volatility damage to surrounding plants is an additional concern, are often needed. Therefore, we conducted research to determine the volatility of common broadleaf herbicides, and their efficacy when applied at spring and fall application timings. Volatility was determined by applying herbicides to turfgrass and using potted tomatoes as indicator plants. Tomatoes exposed to turfgrass treated with Trimec Classic, Confront, Surge, Escalade 2, and Imprelis exhibited little or no volatility damage, while exposure to Speedzone, 4 Speed XT, and Cool Power caused significant damage. In general, herbicides causing little or no damage were amine formulations. Two field studies determined the effect of spring and fall application timing on dandelion control with several herbicides. Herbicide applications in the spring coincided with dandelion anthesis stages: pre-bloom, peak bloom, and post-bloom. Results were dependent on dandelion pressure in the studies. In 2010, with lower pressure, there were no differences among herbicides at any spring timing when dandelion control was evaluated after one year; all herbicides gave ≥ 80% control. In 2011, with higher dandelion pressure, Imprelis SL and 4 Speed XT provided ≥ 96% dandelion control at the spring pre- and post-bloom timings, which was better than Surge, Escalade 2, Cool Power, and Confront. The best choices for spring efficacy combined with minimal to no volatility were Escalade 2 and Trimec Classic. Finally, because interest in organic dandelion control is increasing, we compared several organic weed control tactics with a conventional herbicide. In a two-year field study, the conventional herbicide gave much better control (> 96%) than any organic method. Horticultural vinegar corn gluten meal, and fertilizer-only gave < 25% control, while hand-weeding gave 58 to 71% control. While hand-weeding was the best of the organic tactics, the time required was considered prohibitive for turfgrass managers, unless initial weed levels were very low.

Cílem této doktorské práce je analýza chování vybraných českých otevřených podílových fondů a akcií. Podílové fondy si od druhé poloviny 90. let získávají v České republice stále větší oblibu. Do konce roku 2006 dosáhl objem investic do podílových fondů 150 miliard korun. Empirická studie se věnuje třem typům podílových fondů: akciovým, dluhopisovým a peněžním a akcie. Denní hodnoty cen byly získány z internetových stránek správců fondů a RM-systému. Sledované období začíná 1.1.2001 a končí 31.12.2005. Akcie a podílové listy mají odlišné principy formování ceny. Zatímco ceny akcií se vytváří interakcí nabídky a poptávky na akciovém trhu, u podílových listů je cena odvozena z celkové hodnoty aktiv fondu. Vliv trhu není u podílových fondů významný, protože nabídka podílo-vých listů je téměř neomezená. Navíc jsou aktiva podílového fondu tvořena řadou rozdílných investičních nástrojů jako jsou české a zahraniční akcie, dluhopisy, pokladniční poukázky, instrumenty peněžních trhů atd. Zjištění, zda časové řady fondů mají i za těchto předpokladů stejné vlastnosti jako řady akcií a zda je pro jejich modelování vhodné použít modely vytvo-řené pro akcie, burzovní indexy nebo směnné kurzy, je hlavním tématem této práce. Pozornost je věnována nepodmíněnému rozdělení výnosů logaritmů cen podílových listů. Metodou maximální věrohodnosti jsou odhadnuty parametry teoretických rozdělení a poté je testována jejich shoda s rozdělením výnosů. Další rozdělení zmiňovaná v souvislosti s nepodmíněným rozdělením finančních časových řad jsou zmíněna v teoretické části. K mo-delování podmíněné střední hodnoty je využito modelů typu AR, k modelování podmíněného rozptylu pak lineárních modelů ARCH, GARCH a GARCH-M a nelineárních modelů typu GRJ-GARCH a EGARCH. Další modely volatility jsou popsány v jedné z úvodních kapitol. Skupina nelineárních modelů je do analýzy zahrnuta za účelem hledání ?pákového efektu?. Lineární model GARCH-M popisuje přímé působení podmíněného rozptylu časové řady na její podmíněnou střední hodnotu. Vzhledem k prokázané nenormalitě rozdělení reziduí, ne-jsou splněny počáteční podmínky modelů časových řad. Vhodnější modely lze získat změnou předpokladu o rozdělení nesystematické složky na GED nebo Studentovo t rozdělení. Na zá-kladě porovnání prostřednictvím informačních kritérií a u příbuzných modelů testem věrohodnostním poměrem je pro každou časovou řadu nalezen nejvhodnější model, který slouží k popisu jejích vlastností a v praxi může být využit i k předpovědi dalšího vývoje, v analýze Value at Risk nebo k popisu vývoje rizikovosti fondu. V závěru jsou popsány zjiš-těné společné a rozdílné vlastnosti podílových fondů a akcií a doporučení pro modelování těchto časových řad.