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.

Essays on the Economics of Intellectual Property Rights

Väitöskirjassani tarkastelen informaatiohyödykkeiden ja tekijänoikeuksien taloustiedettä kahdesta eri perspektiivistä. Niistä ensimmäinen kuuluu endogeenisen kasvuteorian alaan. Väitöskirjassani yleistän ”pool of knowledge” -tyyppisen endogeenisen kasvumallin tilanteeseen, jossa patentoitavissa olevalla innovaatiolla on minimikoko, ja jossa uudenlaisen tuotteen patentoinut yritys voi menettää monopolinsa tuotteeseen jäljittelyn johdosta. Mallin kontekstissa voidaan analysoida jäljittelyn ja innovaatioilta vaaditun ”minimikoon” vaikutuksia hyvinvointiin ja talouskasvuun. Kasvun maksimoiva imitaation määrä on mallissa aina nolla, mutta hyvinvoinnin maksimoiva imitaation määrä voi olla positiivinen. Talouskasvun ja hyvinvoinnin maksimoivalla patentoitavissa olevan innovaation ”minimikoolla” voi olla mikä tahansa teoreettista maksimia pienempi arvo. Väitöskirjani kahdessa jälkimmäisessä pääluvussa tarkastelen informaatiohyödykkeiden kaupallista piratismia mikrotaloustieteellisen mallin avulla. Informaatiohyödykkeistä laittomasti tehtyjen kopioiden tuotantokustannukset ovat pienet, ja miltei olemattomat silloin kun niitä levitetään esimerkiksi Internetissä. Koska piraattikopioilla on monta eri tuottajaa, niiden hinnan voitaisiin mikrotaloustieteen teorian perusteella olettaa laskevan melkein nollaan, ja jos näin kävisi, kaupallinen piratismi olisi mahdotonta. Mallissani selitän kaupallisen piratismin olemassaolon olettamalla, että piratismista saatavan rangaistuksen uhka riippuu siitä, kuinka monille kuluttajille piraatti tarjoaa laittomia hyödykkeitä, ja että se siksi vaikuttaa piraattikopioiden markkinoihin mainonnan kustannuksen tavoin. Kaupallisten piraattien kiinteiden kustannusten lisääminen on mallissani aina tekijänoikeuksien haltijan etujen mukaista, mutta ”mainonnan kustannuksen” lisääminen ei välttämättä ole, vaan se saattaa myös alentaa laillisten kopioiden myynnistä saatavia voittoja. Tämä tulos poikkeaa vastaavista aiemmista tuloksista sikäli, että se pätee vaikka tarkasteltuihin informaatiohyödykkeisiin ei liittyisi verkkovaikutuksia. Aiemmin ei-kaupallisen piratismin malleista on usein johdettu tulos, jonka mukaan informaatiohyödykkeen laittomat kopiot voivat kasvattaa laillisten kopioiden myynnistä saatavia voittoja jos laillisten kopioiden arvo niiden käyttäjille riippuu siitä, kuinka monet muut kuluttajat käyttävät samanlaista hyödykettä ja jos piraattikopioiden saatavuus lisää riittävästi laillisten kopioiden arvoa. Väitöskirjan viimeisessä pääluvussa yleistän mallini verkkotoimialoille, ja tutkin yleistämäni mallin avulla sitä, missä tapauksissa vastaava tulos pätee myös kaupalliseen piratismiin.

Context-dependent mate choice in the sand goby, Pomatoschistus minutus

The results presented in this thesis show that all females of a given population do not necessarily choose similar mating partners. Specifically, partner preferences of a fish, the sand goby (Pomatoschistus minutus), varied among individual females and depended on the social context at the time of choice. I also show that females assess multiple mate choice cues simultaneously; partner preferences were based more strongly on an interaction effect between different choice cues than on any individual cue. Furthermore, I found that preferred matings involved fitness benefits in the form of increased offspring success, but these benefits were not significantly affected by mate compatibility. Hence, mate choice for partner compatibility does not appear to be an important determinant of the observed variation in female mate preferences in this species. The context-dependency of female mating preferences revealed is relevant to how genetic variation in sexually selected traits might be maintained: as the mating success of a certain male type varies according to the choice context, directional sexual selection on male traits is shown to be less intense than generally thought making for a slower loss of genetic variation in these traits. Mating preferences of sand gobies were assessed by giving females a binary choice between males that differed in body size and/or other focus traits. These association preferences were found to be sexually motivated, repeatable and to correspond to actual mating decisions.

A curve shortening flow rule for closed embedded plane curves with a prescribed rate of change in enclosed area

Motivated by a problem from fluid mechanics, we consider a generalization of the standard curve shortening flow problem for a closed embedded plane curve such that the area enclosed by the curve is forced to decrease at a prescribed rate. Using formal asymptotic and numerical techniques, we derive possible extinction shapes as the curve contracts to a point, dependent on the rate of decreasing area; we find there is a wider class of extinction shapes than for standard curve shortening, for which initially simple closed curves are always asymptotically circular. We also provide numerical evidence that self-intersection is possible for non-convex initial conditions, distinguishing between pinch-off and coalescence of the curve interior.

Criminal intent

The High Court recently heard submissions of counsel in Zaburoni v The Queen. This case concerns an appeal against conviction for transmitting a serious disease with intent under section 317(b) and (e) of the Queensland Criminal Code. It raises important issues about the meaning of intent and how intent can be proven in Queensland criminal offences. Since intent is an element of so many of the more serious crimes, it is surprising to see that the courts, both in England and Australia, continue to grapple with how best to define it. In murder, for example, the accused is potentially going to be locked up for a very long time, so it is essential that the courts and juries are very clear on what intent actually means, so that they can be confident in correctly finding that it was present on the facts of the case.

Posters versus lurkers. Improving participation in enterprise social networks through management interventions

This thesis investigated the phenomenon of underutilised Enterprise social networks (ESNs). Guided by established theories, we identified key reasons that drive ESN members to either post (i.e., create content) or lurk (i.e., read others' content) and examined the influence of three management interventions - aim to boost participation - on lurkers' and posters' beliefs and participation. We test our model with data collected from 366 members in Google⁺ communities in a large Australian retail organization. We find that posters and lurkers are motivated and hindered by different factors. Moreover, management interventions do not – always – yield the hoped-for results among lurkers.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

A complete characterization of pre-Mueller and Mueller matrices in polarization optics

The Mueller-Stokes formalism that governs conventional polarization optics is formulated for plane waves, and thus the only qualification one could require of a 4 x 4 real matrix M in order that it qualify to be the Mueller matrix of some physical system would be that M map Omega((pol)), the positive solid light cone of Stokes vectors, into itself. In view of growing current interest in the characterization of partially coherent partially polarized electromagnetic beams, there is a need to extend this formalism to such beams wherein the polarization and spatial dependence are generically inseparably intertwined. This inseparability brings in additional constraints that a pre-Mueller matrix M mapping Omega((pol)) into itself needs to meet in order to be an acceptable physical Mueller matrix. These additional constraints are motivated and fully characterized. (C) 2010 Optical Society of America

Modelling, calibrating, and validating car following and lane changing behaviour

Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behaviour under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety; this realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modelling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few. Thus, this special issue will focus on latest developments in modelling, calibrating, and validating two primary vehicular interactions observed in traffic flow: CF and LC.

A solution framework for scheduling a BPM with non-identical job dimensions

A Batch Processing Machine (BPM) is one which processes a number of jobs simultaneously as a batch with common beginning and ending times. Also, a BPM, once started cannot be interrupted in between (Pre-emption not allowed). This research is motivated by a BPM in steel casting industry. There are three main stages in any steel casting industry viz., pre-casting stage, casting stage and post-casting stage. A quick overview of the entire process, is shown in Figure 1. There are two BPMs : (1) Melting furnace in the pre-casting stage and (2) Heat Treatment Furnace (HTF) in the post casting stage of steel casting manufacturing process. This study focuses on scheduling the latter, namely HTF. Heat-treatment operation is one of the most important stages of steel casting industries. It determines the final properties that enable components to perform under demanding service conditions such as large mechanical load, high temperature and anti-corrosive processing. In general, different types of castings have to undergo more than one type of heat-treatment operations, where the total heat-treatment processing times change. To have a better control, castings are primarily classified into a number of job-families based on the alloy type such as low-alloy castings and high alloy castings. For technical reasons such as type of alloy, temperature level and the expected combination of heat-treatment operations, the castings from different families can not be processed together in the same batch.

Deterministic schemes for key distribution in wireless sensor networks

In this paper, we propose a new security metric for measuring resilience of a symmetric key distribution scheme in wireless sensor network. A polynomial-based and a novel complete connectivity schemes are proposed and an analytical comparison, in terms of security and connectivity, between the schemes is shown. Motivated by the schemes, we derive general expressions for security and connectivity. A number of conclusions are made using these general expressions.

Computationally efficient optimum weighting function for vector quantization of LSF parameters

We propose a new weighting function which is computationally simple and an approximation to the theoretically derived optimum weighting function shown in the literature. The proposed weighting function is perceptually motivated and provides improved vector quantization performance compared to several weighting functions proposed so far, for line spectrum frequency (LSF) parameter quantization of both clean and noisy speech data.