Estimating the parameters of stochastic volatility models using option price data

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.

Efficient Output-Sensitive Construction of Reeb Graphs

The Reeb graph tracks topology changes in level sets of a scalar function and finds applications in scientific visualization and geometric modeling. This paper describes a near-optimal two-step algorithm that constructs the Reeb graph of a Morse function defined over manifolds in any dimension. The algorithm first identifies the critical points of the input manifold, and then connects these critical points in the second step to obtain the Reeb graph. A simplification mechanism based on topological persistence aids in the removal of noise and unimportant features. A radial layout scheme results in a feature-directed drawing of the Reeb graph. Experimental results demonstrate the efficiency of the Reeb graph construction in practice and its applications.

Using Alkanes to Predict Voluntary Intake, Faecal Output and Digestibility of Buffel Grass Hay Fed to Steers

The cuticular waxes of forage plants contain long chain n-alkanes with odd carbon chain lengths in the range C25-C37 which are quantitatively recovered in faeces. When these concentrations are used with the concentrations of administered synthetic even chain length alkanes, the voluntary intake (VI), faecal output (FO) and digestibility (DMD) of forages can be estimated (Dove and Mayes 1991, 1996).

A non-linear analogue-to-digital converter for linearization of transducer input-output relationships

Analogue and digital techniques for linearization of non-linear input-output relationship of transducers are briefly reviewed. The condition required for linearizing a non-linear function y = f(x) using a non-linear analogue-to-digital converter, is explained. A simple technique to construct a non-linear digital-to-analogue converter, based on ' segments of equal digital interval ' is described. The technique was used to build an N-DAC which can be employed in a successive approximation or counter-ramp type ADC to linearize the non-linear transfer function of a thermistor-resistor combination. The possibility of achieving an order of magnitude higher accuracy in the measurement of temperature is shown.

A fast acting detector for abnormal output voltages of an UPS system

A new method of sensing the abnormal output voltage conditions of a single phase UPS system is presented, which provides the information almost instantaneously, so that a fast load transfer can be initiated. A continuous monitoring of the UPS output instantaneous voltage is used so that any under/over voltage, transients, or waveform distortion present can be detected.

Information flow, trading activity and commodity futures volatility

Based on unique news data relating to gold and crude oil, we investigate how news volume and sentiment, shocks in trading activity, market depth and trader positions unrelated to information flow covary with realized volatility. Positive shocks to the rate of news arrival, and negative shocks to news sentiment exhibit the largest effects. After controlling for the level of news flow and cross-correlations, net trader positions play only a minor role. These findings are at odds with those of [Wang (2002a). The Journal of Futures Markets, 22, 427–450; Wang (2002b). The Financial Review, 37, 295–316], but are consistent with the previous literature which doesn't find a strong link between volatility and trader positions.

Cointegration and causal relationships between energy consumption and output: Assessing the evidence from Australia

In this paper, we describe our investigation of the cointegration and causal relationships between energy consumption and economic output in Australia over a period of five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach used in an Australian study of this type to include energy, capital and labour as separate inputs of production. The empirical evidence points to a cointegration relationship between energy and output and implies that energy is an important variable in the cointegration space, as are conventional inputs capital and labour. We also find some evidence of bidirectional causality between GDP and energy use. Although the evidence of causality from energy use to GDP was relatively weak when using the thermal aggregate of energy use, once energy consumption was adjusted for energy quality, we found strong evidence of Granger causality from energy use to GDP in Australia over the investigated period. The results are robust, irrespective of the assumptions of linear trends in the cointegration models, and are applicable for different econometric approaches.

The long-run impact of Information and Communication Technology on economic output: The case of Australia

This paper investigates the cointegration and causal relationships between Information and Communication Technology (ICT) and economic output in Australia using data for about five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach of this kind to include ICT and non-ICT capital and other factors to examine long-run Granger causality. The empirical evidence points to a cointegration relationship between ICT capital and output, and implies that ICT capital Granger causes economic output and multifactor productivity, as does non-ICT capital.

Energy-output linkages in Australia: Implications for emissions reduction policies

The study investigates the long-run and dynamic relationships between energy consumption and output in Australia using a multivariate cointegration and causality framework. Using both Engle-Granger and Johansen cointegration approaches, the study finds that energy consumption and real Gross Domestic Product are cointegrated. The Granger causality tests suggest bidirectional Granger causality between energy consumption and real GDP, and Granger endogeineity in the system. Since the energy sector largely contributes to carbon emissions in Australia, we suggest that direct measures to reduce carbon by putting constraints on the energy consumption would pose significant economic costs for the Australian economy.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

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.

Do Measurement Errors in GDP Announcements Cause Output Fluctuations?

This thesis studies empirically whether measurement errors in aggregate production statistics affect sentiment and future output. Initial announcements of aggregate production are subject to measurement error, because many of the data required to compile the statistics are produced with a lag. This measurement error can be gauged as the difference between the latest revised statistic and its initial announcement. Assuming aggregate production statistics help forecast future aggregate production, these measurement errors are expected to affect macroeconomic forecasts. Assuming agents’ macroeconomic forecasts affect their production choices, these measurement errors should affect future output through sentiment. This thesis is primarily empirical, so the theoretical basis, strategic complementarity, is discussed quite briefly. However, it is a model in which higher aggregate production increases each agent’s incentive to produce. In this circumstance a statistical announcement which suggests aggregate production is high would increase each agent’s incentive to produce, thus resulting in higher aggregate production. In this way the existence of strategic complementarity provides the theoretical basis for output fluctuations caused by measurement mistakes in aggregate production statistics. Previous empirical studies suggest that measurement errors in gross national product affect future aggregate production in the United States. Additionally it has been demonstrated that measurement errors in the Index of Leading Indicators affect forecasts by professional economists as well as future industrial production in the United States. This thesis aims to verify the applicability of these findings to other countries, as well as study the link between measurement errors in gross domestic product and sentiment. This thesis explores the relationship between measurement errors in gross domestic production and sentiment and future output. Professional forecasts and consumer sentiment in the United States and Finland, as well as producer sentiment in Finland, are used as the measures of sentiment. Using statistical techniques it is found that measurement errors in gross domestic product affect forecasts and producer sentiment. The effect on consumer sentiment is ambiguous. The relationship between measurement errors and future output is explored using data from Finland, United States, United Kingdom, New Zealand and Sweden. It is found that measurement errors have affected aggregate production or investment in Finland, United States, United Kingdom and Sweden. Specifically, it was found that overly optimistic statistics announcements are associated with higher output and vice versa.

The Structure and Short-Term Development of Finnish Industries in the 1920s and 1930s : An Input-output Approach

Tutkimuksen tavoitteena on tuottaa uutta tietoa Suomen kansantalouden rakenteesta ja lyhyen aikavälin kehityksestä 1920- ja 1930-luvulla. Tutkimus toteutettiin laatimalla kansantaloutta kuvaava panos-tuotostaulu vuodelle 1928 sekä sen laajennus, panos-tuotosmalli. Aineiston avulla kuvataan kansantalouden rakenteellisia riippuvuuksia, tuotannon avaintoimialoja sekä näiden vaikutusta kansantalouteen. Lisäksi tutkimuksessa tarkastellaan kansantalouden tuontiriippuvuutta sekä tuontitullien vaikutusta hintoihin 1930-luvun laman aikana. Tutkimuksen perusteella voitiin identifioida Suomen kansantalouden avaintoimialat vuonna 1928: maatalous, metsätalous, elintarviketeollisuus, puuteollisuus, paperiteollisuus ja rakennustoiminta. Erityisesti elintarviketeollisuuden vahva rooli kansantaloudessa oli kenties yllättävää, erityisesti kun huomioidaan kuinka vähän toimiala on saanut huomiota osakseen taloushistorian tutkimuksessa. Tutkimus osoitti, että Suomen vienti oli pääomavaltaisempaa kuin tuonti. Vaikka tämän tuloksen tulkinta on varauksellinen, tutkimus pystyi osoittamaan ja kvantifioimaan toimialojen työ- ja pääomapanoksen osuuden tuotoksesta yksityiskohtaisesti. Panos-tuotosmallilla arvioitiin puuteollisuuden, paperiteollisuuden ja rakennustoiminnan ajanjaksona 1928-32 tapahtuneen loppukäytön muutoksen vaikutusta kansantalouteen. Merkittävä havainto on, että rakennustoiminnan loppukäytön muutoksella oli erittäin suuri kasvua vähentävä vaikutus koko kansantaloudessa. Talonrakennusinvestointien romahtaminen aiheutti lähes 13 prosentin tuotannon laskun kansantaloudessa. Vaikutus oli jopa suurempi kuin puuteollisuuden viennin romahtamisen. Tulokset osoittavat toisaalta, että yksityisen kulutuksen merkitys kansantaloudelle oli erittäin vahva. Esimerkiksi puuteollisuuden viennin romahtaminen aiheutti yli 4 % tuotannon vähenemisen mutta huomioitaessa mallissa myös yksityisen kulutuksen väheneminen, oli kokonaisvaikutus yli 10 %. Yksityisen kulutuksen huomioiminen mallissa siis yli kaksinkertaisti toimialojen vaikutukset kansantalouteen. Tulokset vahvistivat aiemmissa tutkimuksissa esitettyjä johtopäätöksiä tullipolitiikasta ja osoittivat maatalouteen läheisesti liittyvän elintarviketeollisuuden olleen eniten suojeltu toimiala kansantaloudessa. Muut kotimarkkinoiden toimialat eivät kuitenkaan hyötyneet tullipolitiikasta lamakauden aikana. Panos-tuotoshintamallilla osoitettiin, ettei tullipolitiikka ollut niin onnistunutta kuin aikalaistutkimuksissa väitettiin, vaan tullit korkeintaan pystyivät hidastamaan hintojen alenemista. Tutkimuksen liitteenä esitetään kaikki keskeiset Suomen kansantaloutta vuonna 1928 kuvaavat tilastolliset taulukot, mukaan lukien käyttö- ja tarjontataulukot, panos-tuotostaulukot, panoskertoimet, Leontiefin käänteismatriisi sekä työ- ja pääomapanoskertoimet.

Output feedback control of networked systems with a stochastic communication protocol

This paper addresses an output feedback control problem for a class of networked control systems (NCSs) with a stochastic communication protocol. Under the scenario that only one sensor is allowed to obtain the communication access at each transmission instant, a stochastic communication protocol is first defined, where the communication access is modelled by a discrete-time Markov chain with partly unknown transition probabilities. Secondly, by use of a network-based output feedback control strategy and a time-delay division method, the closed-loop system is modeled as a stochastic system with multi time-varying delays, where the inherent characteristic of the network delay is well considered to improve the control performance. Then, based on the above constructed stochastic model, two sufficient conditions are derived for ensuring the mean-square stability and stabilization of the system under consideration. Finally, two examples are given to show the effectiveness of the proposed method.

Early and late electrophysiological effects of distractor frequency in picture naming: Reconciling input and output accounts

The “distractor-frequency effect” refers to the finding that high-frequency (HF) distractor words slow picture naming less than low-frequency distractors in the picture–word interference paradigm. Rival input and output accounts of this effect have been proposed. The former attributes the effect to attentional selection mechanisms operating during distractor recognition, whereas the latter attributes it to monitoring/decision mechanisms operating on distractor and target responses in an articulatory buffer. Using high-density (128-channel) EEG, we tested hypotheses from these rival accounts. In addition to conducting stimulus- and response-locked whole-brain corrected analyses, we investigated the correct-related negativity, an ERP observed on correct trials at fronto-central electrodes proposed to reflect the involvement of domain general monitoring. The wholebrain ERP analysis revealed a significant effect of distractor frequency at inferior right frontal and temporal sites between 100 and 300-msec post-stimulus onset, during which lexical access is thought to occur. Response-locked, region of interest (ROI) analyses of fronto-central electrodes revealed a correct-related negativity starting 121 msec before and peaking 125 msec after vocal onset on the grand averages. Slope analysis of this component revealed a significant difference between HF and lowfrequency distractor words, with the former associated with a steeper slope on the time windowspanning from100 msec before to 100 msec after vocal onset. The finding of ERP effects in time windows and components corresponding to both lexical processing and monitoring suggests the distractor frequency effect is most likely associated with more than one physiological mechanism.