Elements of Volatility at High Frequency

Volatility is central in options pricing and risk management. It reflects the uncertainty of investors and the inherent instability of the economy. Time series methods are among the most widely applied scientific methods to analyze and predict volatility. Very frequently sampled data contain much valuable information about the different elements of volatility and may ultimately reveal the reasons for time varying volatility. The use of such ultra-high-frequency data is common to all three essays of the dissertation. The dissertation belongs to the field of financial econometrics. The first essay uses wavelet methods to study the time-varying behavior of scaling laws and long-memory in the five-minute volatility series of Nokia on the Helsinki Stock Exchange around the burst of the IT-bubble. The essay is motivated by earlier findings which suggest that different scaling laws may apply to intraday time-scales and to larger time-scales, implying that the so-called annualized volatility depends on the data sampling frequency. The empirical results confirm the appearance of time varying long-memory and different scaling laws that, for a significant part, can be attributed to investor irrationality and to an intraday volatility periodicity called the New York effect. The findings have potentially important consequences for options pricing and risk management that commonly assume constant memory and scaling. The second essay investigates modelling the duration between trades in stock markets. Durations convoy information about investor intentions and provide an alternative view at volatility. Generalizations of standard autoregressive conditional duration (ACD) models are developed to meet needs observed in previous applications of the standard models. According to the empirical results based on data of actively traded stocks on the New York Stock Exchange and the Helsinki Stock Exchange the proposed generalization clearly outperforms the standard models and also performs well in comparison to another recently proposed alternative to the standard models. The distribution used to derive the generalization may also prove valuable in other areas of risk management. The third essay studies empirically the effect of decimalization on volatility and market microstructure noise. Decimalization refers to the change from fractional pricing to decimal pricing and it was carried out on the New York Stock Exchange in January, 2001. The methods used here are more accurate than in the earlier studies and put more weight on market microstructure. The main result is that decimalization decreased observed volatility by reducing noise variance especially for the highly active stocks. The results help risk management and market mechanism designing.

Monetary Policy, Stock Price Misalignments and Macroeconomic Instability

Perhaps the most fundamental prediction of financial theory is that the expected returns on financial assets are determined by the amount of risk contained in their payoffs. Assets with a riskier payoff pattern should provide higher expected returns than assets that are otherwise similar but provide payoffs that contain less risk. Financial theory also predicts that not all types of risks should be compensated with higher expected returns. It is well-known that the asset-specific risk can be diversified away, whereas the systematic component of risk that affects all assets remains even in large portfolios. Thus, the asset-specific risk that the investor can easily get rid of by diversification should not lead to higher expected returns, and only the shared movement of individual asset returns – the sensitivity of these assets to a set of systematic risk factors – should matter for asset pricing. It is within this framework that this thesis is situated. The first essay proposes a new systematic risk factor, hypothesized to be correlated with changes in investor risk aversion, which manages to explain a large fraction of the return variation in the cross-section of stock returns. The second and third essays investigate the pricing of asset-specific risk, uncorrelated with commonly used risk factors, in the cross-section of stock returns. The three essays mentioned above use stock market data from the U.S. The fourth essay presents a new total return stock market index for the Finnish stock market beginning from the opening of the Helsinki Stock Exchange in 1912 and ending in 1969 when other total return indices become available. Because a total return stock market index for the period prior to 1970 has not been available before, academics and stock market participants have not known the historical return that stock market investors in Finland could have achieved on their investments. The new stock market index presented in essay 4 makes it possible, for the first time, to calculate the historical average return on the Finnish stock market and to conduct further studies that require long time-series of data.

Constructing Stability in Software Product Development during Organizational Restructurings

We all have fresh in our memory what happened to the IT sector only a few years ago when the IT-bubble burst. The upswing of productivity in this sector slowed down, investors lost large investments, many found themselves looking for a new job, and countless dreams fell apart. Product developers in the IT sector have experienced a large number of organizational restructurings since the IT boom, including rapid growth, downsizing processes, and structural reforms. Organizational restructurings seem to be a complex and continuous phenomenon people in this sector have to deal with. How do software product developers retrospectively construct their work in relation to organizational restructurings? How do organizational restructurings bring about specific social processes in product development? This working paper focuses on these questions. The overall aim is to develop an understanding of how software product developers construct their work during organizational restructurings. The theoretical frame of reference is based on a social constructionist approach and discourse analysis. This approach offers more or less radical and critical alternatives to mainstream organizational theory. Writings from this perspective attempt to investigate and understand sociocultural processes by which various realities are created. Therefore these studies aim at showing how people participate in constituting the social world (Gergen & Thatchenkery, 1996); knowledge of the world is seen to be constructed between people in daily interaction, in which language plays a central role. This means that interaction, especially the ways of talking and writing about product development during organizational restructurings, become the target of concern. This study consists of 25 in-depth interviews following a pilot study based on 57 semi-structured interviews. In this working paper I analyze 9 in-depth interviews. The interviews were conducted in eight IT firms. The analysis explores how discourses are constructed and function, as well as the consequences that follow from different discourses. The analysis shows that even though the product developers have experienced many organizational restructurings, some of which have been far-reaching, their accounts build strongly on a stability discourse. According to this discourse product development is, perhaps surprisingly, not influenced to a great extent by organizational restructurings. This does not mean that product development is static. According to the social constructionist approach, product development is constantly being reproduced and maintained in ongoing processes. In other words stable effects are also ongoing achievements and these are of particular interest in this study. The product developers maintain rather than change the product development through ongoing processes of construction, even when they experience continuous extensive organizational restructurings. The discourse of stability exists alongside other discourses, some which contradict each other. Together they direct product development and generate meanings. The product developers consequently take an active role in the construction of their work during organizational restructurings. When doing this they also negotiate credible positions for themselves

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.