The thesis focuses on modelling and forecasting the exchange rate time series volatility. The basic approach used for the conditional variance modelling are class (G)ARCH models and their variations. Modelling of the conditional mean is based on the use of AR autoregressive models. Due to the breach of one of the basic assumption of the models (normality assumption), an important part of the work is a detailed analysis of unconditional distribution of returns enabling the selection of a suitable distributional assumption of error terms of (G)ARCH models. The use of leptokurtic distribution assumption leads to a major improvement of volatility forecasting compared to normal distribution. In regard to this fact, the often applied GED and the Student's t distributions represent the key-stones of this work. In addition, the less known distributions are applied in the work, e.g. the Johnson's SU and the normal Inverse Gaussian Distribution. To model volatility, a great number of linear and non-linear models have been tested. Linear models are represented by ARCH, GARCH, GARCH in mean, integrated GARCH, fractionally integrated GARCH and HYGARCH. In the event of the presence of the leverage effect, non-linear EGARCH, GJR-GARCH, APARCH and FIEGARCH models are applied. Using suitable models according to the selected criteria, volatility forecasts are made with different long-term and short-term forecasting horizons. Outcomes of traditional approaches using parametric models (G)ARCH are compared with semi-parametric neural networks based concepts that are widely applicable in clustering and also in time series prediction problems. In conclusion, a description is given of the coincident and different properties of the analyzed exchange rate time series. The author further summarized the models that provide the best forecasts of volatility behaviour of the selected time series, including recommendations for their modelling. Such models can be further used to measure market risk rate by the Value at Risk method or in future price estimating where future volatility is inevitable prerequisite for the interval forecasts.

The impact of the introduction of derivatives on the underlying stock is a debatable topic among the researchers. The issue is quite controversial as contradictory results have been obtained by researchers in various stock markets. The purpose of this study is to examine the volatility and the liquidity effect on the underlying stock after the introduction of index options. We have investigated volatility and liquidity effect by collecting sample data from the stock markets of India, Korea, Taiwan, Hong Kong, Japan, Thailand, Malaysia and Singapore, only markets which are offering index options in Asia.   Applying the generalized autoregressive conditional heteroscedasticity (GARCH) model, we have examined the conditional volatility of intraday (high frequency) returns for each stock market, before and after the introduction of index options. We have also examined the liquidity effect through t-test and Wilcoxon Signed Rank Test. We used t-test to determine the mean differences between the trading volume of pre-index and post-index options periods.    By comparing the estimated parameters and the coefficient of conditional volatility in pre and post period of index options introductions, we have examined that the derivatives trading dramatically increases the persistence of the conditional volatility for all the selected stock markets. We also observed mixed evidence in context to liquidity effect. In the stock exchanges of Hong Kong, Japan, Korea, Taiwan and Thailand, we found that the respective markets become more liquid in the post index options periods in contrast to pre index options period. In these markets trading volume increased significantly after the introduction of index options.  On the other hand, India, Malaysia and Singapore stock markets show no liquidity effect in the post-index option period.   Finally, the empirical results of our study conclude that the introduction of index options on the selected Asian stock markets have increased in stock return volatility and liquidity on the underlying stocks.

The diploma thesis deals with modelling of time series (stock and commodities) by using the models of volatility. The theoretical part focuses on the term of volatility and other terms connected to it. There is a theoretical description of the models as well. The practical part of the thesis focuses on the analysis of the time series and modelling of volatility using the program R.

In this thesis, we consider the pricing problem of an American put option. We introduce a new market model for the evolution of the underlying asset price. Our model adds a new parameter to the well known Heston model. Hence we name our model the extended Heston model. To solve the American put pricing problem we adapt the idea developed by Fouque et al. (2000) to derive the asymptotic formula. We then connect the idea developed by Medvedev and Scaillet (2010) to provide an asymptotic solution for the leading order term P0. We do numerical analysis to gain insight into the accuracy and validity of our asymptotic approximation formula.

An alternative way to invest on the stock market is to invest in IPO’s. An IPO (InitialPublic Offering) is the first time a company goes public on a stock market, giving outshares to private investors and financial institutions. However, there might be someuncertainties about the share price as it never has been traded on the stock exchangebefore and it could therefore be difficult to determine a reasonable value for the shareprice. Consequently, if the offering price for the investor is significantly lower thanthe “correct valued” price it will generate positive initial return during the first tradingday and this phenomenon is labelled as underpricing, generating more “money on thetable”. Still, previous researches display an underperformance among IPO’s during alonger period after the introduction compared to already established companies withinthe same sector, arguing that investors should sell their shares early after the firsttrading day.The objective of this study is therefore to determine if underpricing exists for IPO’son the Swedish stock exchange and if there are any differentiations amongst sectors,and also to investigate two different Buy-and-Hold strategies. A final objective for thestudy is to determine if the level of underpricing is affected by some explanatoryvariables.With a quantitative study and a longitudinal approach, the results confirm the effect ofunderpricing for IPO’s on the Swedish stock exchange, generating an averageunderpricing of 5.56%. Additionally, this study cannot display any different medianunderpricing between industry sectors. However, it contradicts with theunderperformance phenomenon, indicating an overperformance for longer Buy-and-Hold strategies. Lastly, a regression of explanatory variables trying to explain thelevel of underpricing demonstrates no statistically significant results.

<p>This dissertation presents the construction procedure of &ldquo;high-frequency&rdquo; daily measure of changes in housing valuations, and analyzes its return dynamics, as well as investigates its relationship to capital markets. The dissertation consists of three chapters. The first chapter introduces the house price index methodologies and housing transaction data, and reviews the related literature. The second chapter shows the construction and modeling of daily house price indexes and highlights the informational advantage of the daily indexes. The final chapter provides detailed empirical and theoretical investigations of housing index return volatilities. </p><p>Chapter 2 discusses the relationship of the housing market with the other markets, such as consumer good market and financial markets. Different housing price indexes and their construction methodologies are introduced, with emphases on the repeat sales model and S&P/Case Shiller Home Price Index. A detailed description of the housing transaction data I use in the dissertation is also provided in this chapter.</p><p>Chapter 3 is co-authored with Professor Tim Bollerslev and Professor Andrew Patton. We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity. The correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are commensurate with existing empirical evidence for existing monthly and quarterly house price series. Timely and accurate measures of house prices are important in a variety of applications, and are particularly valuable during times of turbulence, such as the recent housing crisis. To quantify the informational advantage of our daily index, we show that a relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data.</p><p>Chapter 4 investigates the properties of housing index return volatilities. Similar to stock market volatility, housing volatilities are found to respond asymmetrically to negative and positive returns. A direct test of volatility on changes in loan-to-value ratio suggests that the observed volatility asymmetry does not stem from changes in degree of housing financial leverage, but could result from the risk premium carried by housing volatility, which is supported by a consumption-based asset pricing model with housing. Moreover, housing and stock volatilities are found to be positively correlated from a set of predictive regressions based on realized variances of housing and stock markets, in which higher (lower) volatility in one market will be followed by higher (lower) volatility in the other. Finally, housing and stock cross-sectional return dispersions are shown to contain useful information in predicting both within-market and cross-market realized volatilities.</p> / Dissertation

This thesis,firstly, studies the impact of financial liberalization and political instability on economic growth and quantitatively examines the relative importance of the identified underling reasons of Argentine riddle by using an innovative econometric methodology and unique data set: it presents power ARCH estimates for Argentina from 1896 to 2000. The main results show that the long-run effect of financial liberalization on economic growth is positive while the short-run effect is negative, albeit substantially smaller. The political instability effects are substantially larger in the short-run than in the long-run. We also investigate potential mechanisms for the effects of financial liberalization and political instability on economic growth: direct impact or happening through the variation of growth volatility. Our results also suggest that financial development, trade openness and political instability are the main factors to explain the Argentine decline. Furthermore, real business cycle variability - growth relationship and the link between inflation and its uncertainty are investigated by using monthly data of four Asian countries/regions (Japan, South Korea, Singapore and Taiwan) and parametric power ARCH methodology to proxy uncertainty. We fnd that more uncertainty about output leads to a higher rate of growth in three of the four countries/regions and the form of the uncertainty matters. Output growth reduces its uncertainty in all countries/regions via inflation uncertainty except Singapore. For all countries/regions, inflation significantly raises inflation uncertainty as predicted by Friedman. On the other hand, increased uncertainty affects inflation positively in Japan and Singapore, which support the Cukierman-Meltzer hypothesis. We find a negative sign for Taiwan which is in accordance with the Holland hypothesis when error term was normally distributed, however, this result is not statistically significant when the student-t distribution is applied. Interestingly, South Korea’s data reveals a positive sign initially, however, it turns around when a structural dummy is incorporated. This dramatic outcome in favour of the Holland hypothesis, and chimes in with Dueker and Kim (1999), who claim that the inflation was strictly controlled by the South Korean monetary authority. In addition, this thesis investigates two-way causal relationships between spread, volatility and volume in the FTSE100 stock index over the period from 1992 to 2004 by using bivariate AR-FI-GARCH model and multiple measurements of risk and spread. The measurements of the spread include relative bid-ask spread, effective bid-ask spread, the inventory cost component of the bid-ask spread and the information cost component of the bid-ask spread. Risk is proxied by two measurements of price volatility: the close-to-close volatility and the range-based volatility. We also take the impact of electronic trading into account. Our results suggest that the spread and volume are positively impacted by volatility simultaneously. In addition, both volatility and volume are negatively affected by the spread. Furthermore, we find that the inventory cost component of the spread has a negative effect on volatility, in contrast, the information component of the spread positively impacts volatility. These results support the argument that speculation generates volatility in the market and higher transaction costs bene t stability of the market.

In this thesis, we propose two continuous time stochastic volatility models with long memory that generalize two existing models. More importantly, we provide analytical formulae that allow us to study option prices numerically, rather than by means of simulation. We are not aware about analytical results in continuous time long memory case. In both models, we allow for the non-zero correlation between the stochastic volatility and stock price processes. We numerically study the effects of long memory on the option prices. We show that the fractional integration parameter has the opposite effect to that of volatility of volatility parameter in short memory models. We also find that long memory models have the potential to accommodate the short term options and the decay of volatility skew better than the corresponding short memory stochastic volatility models.

Three main issues are explored in this thesis—volatility measurement, volatility spillover and large-dimension covariance matrices. For the first question of volatility measurement, this thesis compares two newly-proposed, high-frequency volatility measurement models, namely realized volatility and realized range-based volatility. It does so in the aim of trying to use empirical results to assess whether one volatility model is better than the other. The realized volatility model and realized range-based volatility model are compared based on three markets, five forecast models, two data frequencies and two volatility proxies, making sixty scenarios in total. Seven different loss functions are also used for the evaluation tests. This necessarily ensures that the empirical results are highly robust. After making some simple adjustments to the original realized range-based volatility, this thesis concludes that it is clear that the scaled realized range-based volatility model outperforms the realized volatility model. For the second research question on volatility spillover, realized range-based volatility and realized volatility models are employed to study the volatility spillover among the S&P 500 index markets, with the aim of finding out empirically whether volatility spillover exists between the markets. Volatility spillover is divided into the two categories of statistically significant volatility spillover and economically significant volatility spillover. Economically significant spillover is defined as spillover that can help forecast the volatility of another market, and is therefore a more powerful measurement than statistically significant spillover. The findings show that, in reality, the existence of volatility spillover depends on the choice of model, choice of volatility proxy and value of parameters used. The third and final research question in this thesis involves the comparison of various large-dimension multivariate models. The main contribution made by this specific study is threefold. First, a number of good performance multivariate volatility models are introduced by adjusting some commonly used models. Second, different models and various choices of parameters for these models are tested based on 26 currency pairs. Third, the evaluation criteria adopted possess much more practical implications than those used in most other papers on this subject area.

The paper analyzes effects of the financial crisis on stock market of the Czech Republic and Spain. We employ BEKK-GARCH model in order to study volatility spillovers and transmissions from the US stock market to stock markets of the Czech Republic and Spain. The multivariate GARCH models results show statistically significant, but relatively small, almost irrelevant volatility spillovers from the US stock market to stock markets of the Czech Republic and Spain. The Czech stock market exhibits higher conditional correlation coefficient than the Spanish stock market.