The purpose of this thesis is to study if and how well implied volatility can predict realised volatility and returns on the OMXS30 index one month in the future. The findings are put in relation to how historical volatility can predict realised volatility and how changes in implied volatility can predict returns. The study covers the time period from 10th of May 2012 to 9th of February 2020 and the implied volatility used in the study is derived from an unweighted average of OMXS30 call and put option implied volatility. Six different OLS-regressions are performed to study the prediction capability of implied volatility. This study finds support of implied volatility to be a statistically significant estimate for future realised returns in a univariate regression. However, our results show that historical volatility performs slightly better predictions of realised volatility than implied volatility. These are contradictory results to the majority of the papers studied in this thesis. These papers share the common notion that implied volatility is superior to historical volatility in predicting realised volatility. Further our results show that implied volatility nor change in implied volatility are significant estimates to future realised returns and perform poorly as predictors. This result is supported by the larger part of previous research, which found implied volatility to be a weak predictor of returns.

This dissertation includes two different groups of objects in macroeconomics and financial economics. In macroeconomics, the aggregate investment fluctuation and its relation to an individual firm's behavior have been extensively studied for the past three decades. Most studies on the interdependence behavior of firms' investment focus on the key issue of separating a firm's reaction to others' behavior from reaction to common shocks. However, few researchers have addressed the issue of isolating this endogenous effect from a statistical and econometrical approach. The first essay starts with a comprehensive review of the investment fluctuation and firms' interdependence behavior, followed by an econometric model of lumpy investments and an analysis of the binary choice behavior of firms'investments. The last part of the first essay investigates the unique characteristics of the Italian economy and discusses the economic policy implications of our research findings. We ask a similar question in the field of financial economics: Where does stock market volatility come from? The literature on the sources of such volatility is abundant. As a result of the availability of high-frequency financial data, attention has been increasingly directed at the modeling of intraday volatility of asset prices and returns. However, no empirical research of intraday volatility analysis has been applied at both a single stock level and industry level in the food industry. The second essay is aimed at filling this gap by modeling and testing intraday volatility of asset prices and returns. It starts with a modified High Frequency Multiplicative Components GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, which breaks daily volatility into three parts: daily volatility, deterministic intraday volatility, and stochastic intraday volatility. Then we apply this econometric model to a single firm as well as the whole food industry using the Trade and Quote Data and Center for Research in Security Prices data. This study finds that there is little connection between the intraday return and overnight return. There exists, however, strong evidence that the food recall announcements have negative impacts on asset returns of the associated publicly traded firms.

This dissertation consists of three chapters covering the following topics in firm value and volatility: valuation of agency cost, valuation of the underpricing in IPOs and the idiosyncratic volatility of public firms. In Chapter 1, I briefly introduce three topics studied in my dissertation. In addition, I summarized the stochastic frontier model which is employed in the study of valuation of agency cost and the underpricing in IPOs. In Chapter 2, I extend the agency cost literature multifold. First, by using the data of the 1,500 S&P Super Composite Index Constituents for 1994-2011, I estimate firm-level agency cost and the uncertainty in firm's maximum benchmark value, respectively, as the mean and variance of the inefficiency term conditional on the composed error. The estimation results reveal that, on average, a sample firm is around 18% and 15% below its optimal value for the period 1994-2006 and 2007-2011 respectively. The variances of the inefficiency term are 0.01 and 0.001 for two periods respectively. Second, using this measure of uncertainty, I construct the confidence interval for the agency cost of each firm. Third, a new concept called Coefficient of Uncertainty of Market Value due to the principal-agent problem (CUMV) is defined and calculated, which measures uncertainty in the benchmark value per unit of agency cost. Finally, I decompose the change in market value of a firm into three components, i.e. change due to agency cost; change due to operational efficiency, and change due to the evaluation of the whole market (called the market effect). I find that the reduction of agency cost and the expansion of the whole market do contribute to firm growth, but the majority of the growth for the sample firms is explained by the improvement of firms' operational efficiency. In chapter 3, I estimate the magnitude of the underpricing of the initial public offering (IPO) for 338 firms during 2001-2010 under the framework of Stochastic Frontier Approach. The magnitude of the underpricing in IPOs and the uncertainty in IPOs' maximum benchmark value are estimated as the mean and variance of the inefficiency term conditional on the composed error respectively. I note that the new issues of a firm with initial offering in US between 2001 and 2010, on average, fall short of 22.9% of their optimal value with a variance of 0.63. As an extension of existing literature, I do not only estimate the frontier model, but investigate the determinants of the underpricing of the IPOs. The estimation results support the fact that the underpricing would be lower if the firms have more reputable underwriters, more insider ownership and higher age at the time of offering new issues. Finally, I introduce a new concept, the Coefficient of Uncertainty of Firm Value due to the underpricing in the IPOs (CUV), which reports the firm value uncertainty for each unit of the underpricing in IPOs. I observe that, on average, the CUV is 4.21 for a sample firm, which implies that firm's uncertainty is indeed sensitive to the underpricing in IPOs. In chapter 4, I investigate the idiosyncratic volatility and its relation to executive ownership during 1992 to 2011. The ownership of executives is employed as the proxy for the behavior of executives to study how executives influence firms' idiosyncratic volatility. Inconsistent with the previous literature, I don't find upward trend of the aggregated idiosyncratic volatility during 1992 to 2011. Instead, I observe that the aggregated idiosyncratic volatility exhibits indeterministic pattern during this period. Moreover, I also note that the reverts of aggregated idiosyncratic volatility occur at a time of the US stock market crash in 2000 and the period of most recent recession (2008-2009). The most interesting finding of this study is that although the idiosyncratic volatility increases in executives' ownership, the idiosyncratic volatility's growth rate is not always positive related with executives' ownership. In Chapter 5, I conclude this dissertation.

This study compares different methods of forecasting price volatility in the crude oil futures market using daily data for the period November 1986 through March 1997. It compares the forward-looking implied volatility measure with two backward-looking time-series measures based on past returns - a simple historical volatility estimator and a set of estimators based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) class of models. Tests for the relative information content of implied volatilities vis-à-vis GARCH time series models are conducted within-sample by estimating nested conditional variance equations with returns information and implied volatilities as explanatory variables. Likelihood ratio tests indicate that both implied volatilities and past returns contribute volatility information. The study also checks for and confirms that the conditional Generalized Error Distribution (GED) better describes fat-tailed returns in the crude oil market as compared to the conditional normal distribution. Out-of-sample forecasts of volatility using the GARCH GED model, implied volatility, and historical volatility are compared with realized volatility over two-week and four-week horizons to determine forecast accuracy. Forecasts are also evaluated for predictive power by regressing realized volatility on the forecasts. GARCH forecasts, though superior to historical volatility, do not perform as well as implied volatility over the two-week horizon. In the four-week case, historical volatility outperforms both of the other measures. Tests of relative information content show that for both forecast horizons, a combination of implied volatility and historical volatility leaves little information to be added by the GARCH model. / Master of Arts

In the dynamic landscape of investment, the comparison between conventional ETFs andESG-screened ETFs presents an interesting narrative of risk and return. With the ETF market soaringto $7.2 trillion in total net assets by 2021 and a surge in global enthusiasm for ESG investing, theintegration of financial and ethical considerations has become increasingly significant.This study delves into this comparison, dissecting the risk-return dynamics between conventional andESG-screened ETFs in developed markets. Against a backdrop of escalating interest in ESG criteria,two questions drive the study: How do the risk-return dynamics differ between conventional ETFs andESG-screened ETFs? Do ESG-screened ETFs outperform conventional ETFs relative to the S&P 500index? Leveraging Sharpe ratio calculations and GARCH modelling, the analysis extends into thepost-pandemic era, capturing recent market performances.Findings reveal mixed performance outcomes. While ESG-screened ETFs demonstrate stability,conventional ETFs achieve slightly higher risk-adjusted returns. Nonetheless, ESG-screened ETFsoffer stability without significant performance trade-offs, making them attractive for investors seekingalignment with ethical considerations. This research not only sheds light on ETF performancedynamics but also guides investors in integrating ESG principles into their investment strategies forbalanced financial and ethical outcomes.

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Many past literatures have examined the predictive power of implied volatility versus that of historical volatility, but they have showed divergent conclusions. One of the major differences among these studies is the methods that they used to obtain implied volatility. The VIX index, introduced in 1993, provides a model-free and directly observable source of implied volatility data. The VIX futures is an actively traded VIX derivative product, and its prices are believed to contain market’s expectation about future volatility. By analyzing the relationship between the VIX futures prices and the realized volatilities of the 30-day period that these VIX futures contracts cover, this paper finds that the VIX futures contracts with shorter maturities have predictive power on future realized volatility, but they are upwardly biased estimates. The predictive power, however, decreases as the time to maturity increases. The outstanding VIX futures contracts with the nearest expiration dates outperform GARCH estimates based on historical return data at predicting future realized volatility.

Due to recent research disproving old claims in financial mathematics such as constant volatility in option prices, new approaches have been incurred to analyze the implied volatility, namely stochastic volatility models. The use of stochastic volatility in option pricing is a relatively new and unexplored field of research with a lot of unknowns, where new answers are of great interest to anyone practicing valuation of derivative instruments such as options. With both single and two-factor stochastic volatility models containing various correlation structures with respect to the asset price and differing mean-reversions of variance the question arises as to how these values change their more observable counterpart: the implied volatility. Using the semi-analytical formula derived by Chiarella and Ziveyi, we compute European call option prices. Then, through the Black–Scholes formula, we solve for the implied volatility by applying the bisection method. The implied volatilities obtained are then approximated using various models of regression where the models’ coefficients are determined through the Moore–Penrose pseudo-inverse to produce implied volatility surfaces for each selected pair of correlations and mean-reversion rates. Through these methods we discover that for different mean-reversions and correlations the overall implied volatility varies significantly and the relationship between the strike price, time to maturity, implied volatility are transformed.

Can we capture the explosive nature of volatility skew observed in the market, without resorting to non-Markovian models? We show that, in terms of skew, the Heston model cannot match the market at both long and short maturities simultaneously. We introduce Abi Jaber (2019)'s Lifted Heston model and explain how to price options with it using both the cosine method and standard Monte-Carlo techniques. This allows us to back out implied volatilities and compute skew for both models, confirming that the Lifted Heston nests the standard Heston model. We then produce and analyze the skew for Lifted Heston models with a varying number N of mean reverting terms, and give an empirical study into the time complexity of increasing N. We observe a weak increase in convergence speed in the cosine method for increased N, and comment on the number of factors to implement for practical use.

Yes / This paper studies the predictive ability of corridor implied volatility (CIV) measure. It is motivated by the fact that CIV is measured with better precision and reliability than the model-free implied volatility due to the lack of liquid options in the tails of the risk-neutral distribution. By adding CIV measures to the modified GARCH specifications, the out-of-sample predictive ability of CIV is measured by the forecast accuracy of conditional volatility. It finds that the narrowest CIV measure, covering about 10% of the RND, dominate the 1-day ahead conditional volatility forecasts regardless of the choice of GARCH models in high volatile period; as market moves to non volatile periods, the optimal width broadens. For multi-day ahead forecasts narrow and mid-range CIV measures are favoured in the full sample and high volatile period for all forecast horizons, depending on which loss functions are used; whereas in non turbulent markets, certain mid-range CIV measures are favoured, for rare instances, wide CIV measures dominate the performance. Regarding the comparisons between best performed CIV measures and two benchmark measures (market volatility index and at-the-money Black–Scholes implied volatility), it shows that under the EGARCH framework, none of the benchmark measures are found to outperform best performed CIV measures, whereas under the GARCH and NAGARCH models, best performed CIV measures are outperformed by benchmark measures for certain instances.