<p>The purpose of this study is to examine the expiration day effect on a stocks volatility due to stock option expiration, which is every third Friday in the month on Stockholm stock exchange. Volatility is the standard deviation of a stock. It measures the uncertainty about a stocks future movement. When volatility increases, the chance or probability of a stock going up or down increases. It’s a common rumor among stock traders that stock volatility tends to increase nearby expiration day. Trader calls it expiration day effect. Some previous studies which the authors of this thesis have studied confirms that there is a expiration day effect, some don’t.</p><p>The approach to see if there is an increase in stock volatility is by setting up hypotheses where the null hypothesis is that there is no effect on stock volatility due to option expiration and the alternative hypothesis that there is. The hypotheses were tested by a t-test for four companies listed on the Stockholm stock exchange between 1st January 2002 and 31st March 2005. The companies which were chosen for the studies were ABB, Ericsson, Skandia and Telia Sonera. Reason of choosing these particular companies were the high turnover in their options which was an requirement set up by the authors.</p><p>The results and conclusion of this thesis was that there is no expiration day effect nearby expiration day. The authors couldn’t find any increase in volatility for the chosen companies due to option expiration and therefore didn’t rejected the null hypothesis.</p>

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>

Background: An evaluation of analysts´ forecasting ability is interesting since their estimates constitute an important part in stock valuation and investment decisions. The recent years´ development in the stock market has lead to criticism of analysts’ deficient forecasts. Purpose: The purpose of this thesis is to evaluate analysts´ forecasting ability concerning companies quoted at Stockholmsbörsen between 1987 and 2002. We also intend to discuss possible explanations for analysts’ behavior in case of deficient accuracy. Method: Regression analysis is used to compare consensus estimates of earnings per share to actual earnings per share. We attempt to investigate the existence of a relation between forecasting ability and forecast horizon, the volatility at Stockholmsbörsen and the industry in which the firm operates. Behavioral finance and economic incentives is used to discuss the most convincing explanations to analysts´ behavior in cases of deficient accuracy. Result: The study indicates over optimistic forecasts and overreaction to earnings information. Analysts tend to give more accurate forecasts closer to earnings announcement. We believe that herding, economic incentives and the fact that analysts get information from the company explains a significant part of analysts’ behavior. In addition, the study shows a possible relation between more accurate forecasts and lower volatility. Concerning industries we find stronger overreaction in healthcare and heavy industry. The study shows the most exceptional optimism in consumer goods/services and IT/telecom.

The purpose of this study is to examine the expiration day effect on a stocks volatility due to stock option expiration, which is every third Friday in the month on Stockholm stock exchange. Volatility is the standard deviation of a stock. It measures the uncertainty about a stocks future movement. When volatility increases, the chance or probability of a stock going up or down increases. It’s a common rumor among stock traders that stock volatility tends to increase nearby expiration day. Trader calls it expiration day effect. Some previous studies which the authors of this thesis have studied confirms that there is a expiration day effect, some don’t. The approach to see if there is an increase in stock volatility is by setting up hypotheses where the null hypothesis is that there is no effect on stock volatility due to option expiration and the alternative hypothesis that there is. The hypotheses were tested by a t-test for four companies listed on the Stockholm stock exchange between 1st January 2002 and 31st March 2005. The companies which were chosen for the studies were ABB, Ericsson, Skandia and Telia Sonera. Reason of choosing these particular companies were the high turnover in their options which was an requirement set up by the authors. The results and conclusion of this thesis was that there is no expiration day effect nearby expiration day. The authors couldn’t find any increase in volatility for the chosen companies due to option expiration and therefore didn’t rejected the null hypothesis.

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Trying to find explanations to movements in the exchange rate is something that econo-mists have been dealing with to a great extend lately. Especially since the break down of the Bretton Wood system in the early 1970’s, when many countries introduced a floating sys-tem instead. One of the most famous and often tested models is Jeffery A. Frankel’s Real Interest Differential (RID) model from 1979. This paper investigates which of the variables included in the model are affecting move-ments in the exchange rate for Sweden, the UK and Japan against the US dollar between January 1995 and December 2004. The variables in question are money supply, industrial production, interest rate and inflation differential. The model has purchasing power parity and uncovered interest parity as underlying theoretical assumptions, two main building blocks of open macro economics, and when combined, they can offer a relationship be-tween changes in the exchange rate and the interest rate differential. The results show that the variable interest rate differential constitutes a significant explana-tory variable for exchange rate movements regarding all three countries included in the model. Both Sweden and the UK have also, in accordance with the RID model, the ex-pected negative sign on the coefficient. The results regarding the other variables are mixed between the countries, but it can in general be said that the model seems to be able to ex-plain movements in the exchange rate to a certain degree.

The quality of software systems depends on early activities in the software development process, of which the management of requirements is one. When requirements are not managed well, a project can fail or become more costly than intended, and the quality of the software developed can decrease. Among the requirements management practices, it is particularly important to quantify and predict requirements volatility, i.e., how much the requirements are likely to change over time. Software measures can help in quantifying and predicting requirements attributes like volatility. However, few measures have yet been defined, due to the fact that the early phases are hard to formalise. Furthermore, very few requirements measures have been validated, which would be needed in order to demonstrate that they are useful. The approach to requirements management in this thesis is quantitative, i.e. to monitor the requirements management activities and requirements volatility through software measurement. In this thesis, a set of 45 requirements management measures is presented. The measures were defined using the goal question metrics framework for the two predefined goals of the requirements management key process area of the capability maturity model for software. A subset of these measures was validated theoretically and empirically in four case studies. Furthermore, an analysis of validated measures in the literature was performed, showing that there is a lack of validated process, project, and requirements measures in software engineering. The studies presented in this thesis show that size measures are good estimators of requirements volatility. The important result is that size is relevant: increasing the size of a requirements document implies that the number of changes to requirements increases as well. Furthermore, subjective estimations of volatility were found to be inaccurate assessors of requirements volatility. These results suggest that practitioners should complement the subjective estimations for assessing volatility with the objective ones. Requirements engineers and project managers will benefit from the research presented in this thesis because the measures defined, proved to be predictors of volatility, can help in understanding how much requirements will change. By deploying the measures, the practitioners would be prepared for possible changes in the schedule and cost of a project, giving them the possibility of creating alternative plans, new cost estimates, and new software development schedules.

There are some nonlinear models for pricing financial derivatives which can improve the linear Black-Scholes model introduced by Black, Scholes and Merton. In these models volatility is not constant anymore, but depends on some extra variables. It can be, for example, transaction costs, a risk from a portfolio, preferences of a large trader, etc. In this thesis we focus on these models. In the first chapter we introduce some important theory of financial derivatives. The second chapter is devoted to the volatility models. We derive three models concerning transaction costs (RAPM, Leland's  and Barles-Soner's model) and Frey's model which assumes a large (dominant) trader on the market. In the third and in the forth chapter we derive portfolio and make numerical experiments with a free boundary. We use the first order additive and the second order Strang splitting methods. We also use approximations of Barles-Soner's model using the identity function and introduce an approximation with the logarithm function of Barles-Soner's model. These models we finally compare with models where the volatility includes constant transaction costs.

To understand and model the dynamics of the implied volatility smile is essential for trading, pricing and risk management portfolio. We suggest a  linear Kalman filter for updating of the Stochastic Volatility Inspired (SVI) model of the volatility. From a risk management perspective we generate the 1-day ahead forecast of profit and loss (P\&amp;L) of option portfolios. We compare the estimation of the implied volatility using the SVI model with the cubic polynomial model. We find that the SVI Kalman filter has outperformed the  others.

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.