The thesis focuses at the evolution of cryptocurrencies or more precisely at the competition process between them in expanding to broader usage. The first main goal of the work is to find out, whether Bitcoin, as the first and still most capitalized cryptocurrency, has an advantage of higher maturity than alternative cryptocurrencies. The second goal is to analyze whether the individual cryptocurrencies are perceived individually by market participants, which could grant the alternative cryptocurrencies an option to compete with Bitcoin by offering better features as safer technology or faster transaction. The analysis of volatility patterns in their exchange rates via various GARCH models suggests that Bitcoin still has advantage in higher maturity. The analysis of the correlation between various alternative cryptocurrencies and Bitcoin finds positive correlation and thus suggests that the cryptocurrencies are rather perceived together. JEL Classification G17, G19, E40, E41 Keywords cryptocurrencies, volatility, GARCH, money, correlation Author's e-mail 79828843@fsv.cuni.cz Supervisor's e-mail frantisek.cech@fsv.cuni.cz

This study investigates how increased wind power production (in MWh) in Sweden has affected electricity price volatility in the Nordic wholesale electricity exchange Nord Pool. The importance and growth of wind power have emerged in light of its low marginal costs of production and it being a renewable, zero-carbon electricity generation source. Previous studies have found that while increased wind power production generally lowers the average wholesale price of electricity, volatility tends to increase due to the intermittent character of wind power production. By using daily price and wind power data from the Nordic exchange market Nord Pool during the period 2015-2017, a GARCH model was used to investigate how wind power has affected price volatility. The results indicate that electricity price volatility increases in the long run when wind power production increases. The reasons behind this could be found in the inflexibility of baseload power production. However, the Swedish electric power system also benefits from a high degree of flexibility due to the presence of hydropower reservoirs.

Japanese monetary authorities used to employ various intervention techniques to adjust the level of the dollar/yen exchange rate and reduce its volatility. Application of the GARCH-in- mean model for estimation of the effect of these operations demonstrates that depreciating interventions reduced volatility effectively from 1995 until 2002. Frequent interventions of the small scale had a tendency to increase volatility during period 1991-1995. Foreign exchange interventions conducted by US Fed have increasing, means negative, effect, on the conditional variance. Frequent interventions of the great scale do not affect the volatility; it is determined mostly by the persistent level of the conditional variance from the latter periods. Recent interventions conducted by the Bank of Japan after the financial crisis do not show any considerable effect on both the volatility and the level of the exchange rate.

Volatility is a fascinating and important topic for financial markets in general, and probably the single most important issue in financial risk management. Although volatility itself is not synonymous with risk, it is closely associated with it in the realm of risk management. In this study, I focus on the volatility in the foreign exchange markets and investigate the spillover of volatility from this market to equity correlations and its impact on global equity markets’ bid-ask spreads as a proxy for market quality. I also explore the role that accounting earnings quality play in subsequent volatility in U.S. equity markets. I provide a theoretical base and its associated empirics for the link between exchange rate volatilities and global equity correlations. I test this theory using multiple techniques that ends with the application of autoregressive error correction analysis, wherein, I demonstrate the predictive power of options implied exchange rate volatilities against ex-ante global equity correlations. My findings indicate that exchange rate implied volatilities, coupled with one-period ex-post correlations, are more predictive of subsequent equity market correlations than other models. I then examine the impact of currency volatilities on the average monthly spreads in ADRs and their underlying local shares. I employ dynamic panel data estimation and principal component analysis to show that currency volatility explains a significant portion (16.6%) of the variation in spreads across markets, heretofore largely unexplored by extant finance literature. Finally, I employ well established accrual measures to calculate aggregate accruals for the S&P 500 on a quarterly basis and examine the ability of this aggregate measure to forecast future trends in the volatility of the index. I find a statistically significant relation between subsequent twelve-month volatility in the S&P 500 index and aggregate accruals. This relation holds whether total or abnormal accruals measures are employed. My findings document a rare long-term indicator of volatility in the widely followed index. I also show that my aggregate accrual measure yields additional information about S&P 500 volatility when compared with simple historical volatility measures or option implied volatility.

The statements that include sufficient detail to identify the probability distributions of future prices are asset price dynamics. In this research, using the empirical methods that could explain the historical prices and discuss about how prices change we investigate various important characteristics of the dynamics of asset pricing. The volatility changes can explain very important facts about the asset returns. Volatility could gauge the variability of prices over time. In order to do the volatility modelling we use the conditional heteroskedasticitc models. One of the most powerful tools to do so is using the idea of autoregressive conditional heteroskedastic process or ARCH models, which fill the gap in both academic and practical literature. In this work we detect the asymmetric volatility effect and investigate long memory properties in volatility in Canadian stock market index, using daily data from 1979 through 2015. On one hand, we show that there is an asymmetry in the equity market index. This is an important indication of how information impacts the market. On the other hand, we investigate for the long-range dependency in volatility and discuss how the shocks are persistence. By using the long memory-GARCH models, we not only take care of both short and long memory, but also we compute the d parameter that stands for the fractional decay of the series. By considering the breaks in our dataset, we compare our findings on different conditions to find the most suitable fit. We present the best fit for GARCH, EGARCH, APARCH, GJR-GARCH, FIGARCH, FIAPARCH, and FIEGARCH models.

The thesis is dealing with option pricing. The basic Black-Scholes model is described, along with the reasons that led to the development of stochastic volatility models. SABR model and Heston model are described in detail. These models are then applied to equity options in the times of high volatility. The models and their application are then evaluated.

The main goal of this diploma thesis is to explore the area of virtual currency Bitcoin and assess the use of this currency in today's economy. Thesis first mentions the cryptocurrency market, the technology and other altcoins. It further analyzes the cryptocurrency Bitcoin in detail, its foundation, history and mining. The text also explores the volatility of this currency in the recent years, the question of regulation by states and technological threats to the network. In the final chapter diploma thesis examines the possibilities for individuals to obtain this currency and the use of Bitcoin by enterprises.

This thesis makes a number of contributions in the derivative pricing and risk management literature and to the growing literature that exploits information embedded in option prices. First, it develops an effective numerical scheme for importance sampling scheme of Fouque and Tullie (2002) based on a 2-dimensional lookup table of stock price and time to maturity that dramatically improves the speed of this importance sampling scheme. Second, the thesis presents an application of this importance sampling scheme in a Multi-Level Monte Carlo simulation. Such combination yields greater variance reduction compared to Multi-Level Monte Carlo or importance sampling alone. Third, it demonstrates how the Greeks can be computed using the Likelihood Ratio Method based on characteristic function, and how combining it with importance sampling leads to a significant variance reduction for the Greeks. Finally, it documents the positive relationship between the risk-neutral skewness (RNS) and future realized stock returns that is driven by the underperformance of highly negative RNS portfolio. The results provide strong evidence that the underperformance of stocks with the lowest RNS is driven by those stocks that are associated with a higher hedging demand, relative overvaluation and are also too costly or too risky to sell short. Moreover, by decomposing RNS into its systematic and idiosyncratic components, this thesis shows that the latter drives the positive relationship with future realized stock returns.

Current Requirements Engineering (RE) mechanisms used to measure Requirements Volatility (RV) employ textual-based artifacts for tracking changes to software requirements that primarily consist of detailed requirements documents that are difficult to understand by most software system stakeholders making it almost impossible for these stakeholders to gain a clear picture of how changes to a requirement will impact the total system overall. Research in the area of RE visualizations have proven that graphically representing software information in the form of visualizations can communicate complex information regarding requirements to system stakeholders in a manner that does not require an in-depth knowledge of RE technical documentation. This research used the concepts of Footprint Visualizations (FVs) to graphically represent software requirements as they evolved over time and analyzed these FV image artifacts to determine RV ratings. This work successfully demonstrated the use of FV analysis to measure RV. This work performed a qualitative study that compared the relationship between the RV ratings that were determined using the FV-based analysis methods proposed in this work to the RV ratings determined using traditional non-visual RV methods that relied on subject matter expert evaluation of a common requirements use case data set. The results of this study expanded the body of knowledge in the field of Requirements Engineering Visualization by demonstrating new analysis methods for measuring volatility in requirements use cases as they evolve over the software development life cycle process in order to aid system stakeholders in understanding the effects of changes made to requirements regardless of the individual stakeholders level of technical requirements documentation training.

Prior research has established that idiosyncratic volatility of the securities prices exhibits a positive trend. This trend and other factors have made the merits of investment diversification and portfolio construction more compelling. A new optimization technique, a greedy algorithm, is proposed to optimize the weights of assets in a portfolio. The main benefits of using this algorithm are to: a) increase the efficiency of the portfolio optimization process, b) implement large-scale optimizations, and c) improve the resulting optimal weights. In addition, the technique utilizes a novel approach in the construction of a time-varying covariance matrix. This involves the application of a modified integrated dynamic conditional correlation GARCH (IDCC - GARCH) model to account for the dynamics of the conditional covariance matrices that are employed. The stochastic aspects of the expected return of the securities are integrated into the technique through Monte Carlo simulations. Instead of representing the expected returns as deterministic values, they are assigned simulated values based on their historical measures. The time-series of the securities are fitted into a probability distribution that matches the time-series characteristics using the Anderson-Darling goodness-of-fit criterion. Simulated and actual data sets are used to further generalize the results. Employing the S&P500 securities as the base, 2000 simulated data sets are created using Monte Carlo simulation. In addition, the Russell 1000 securities are used to generate 50 sample data sets. The results indicate an increase in risk-return performance. Choosing the Value-at-Risk (VaR) as the criterion and the Crystal Ball portfolio optimizer, a commercial product currently available on the market, as the comparison for benchmarking, the new greedy technique clearly outperforms others using a sample of the S&P500 and the Russell 1000 securities. The resulting improvements in performance are consistent among five securities selection methods (maximum, minimum, random, absolute minimum, and absolute maximum) and three covariance structures (unconditional, orthogonal GARCH, and integrated dynamic conditional GARCH).