Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market

This paper investigates the issue of temporal ordering of the range-based volatility and volume in the Indian stock market for the period 1995-2007. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish between volume traded before and after the introduction of futures and options trading. We find that in all three periods the impact of both the number of trades and the value of shares traded on volatility is negative. This result is in line with the theoretical argument that a marketplace with a larger population of liquidity providers will be less volatile than one with a smaller population. We also find that (i) the introduction of futures trading leads to a decrease in spot volatility, (ii) volume decreases after the introduction of option contracts and, (iii) there are signifcant expiration day effects on both the value of shares traded and volatility series.http://deepblue.lib.umich.edu/bitstream/2027.42/64397/1/wp935.pd

Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market

This paper investigates the issue of temporal ordering of the range-based volatility and volume in the Indian stock market for the period 1995-2007. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish between volume traded before and after the introduction of futures and options trading. We find that in all three periods the impact of both the number of trades and the value of shares traded on volatility is negative. This result is in line with the theoretical argument that a marketplace with a larger population of liquidity providers will be less volatile than one with a smaller population. We also find that (i) the introduction of futures trading leads to a decrease in spot volatility, (ii) volume decreases after the introduction of option contracts and, (iii) there are signifcant expiration day effects on both the value of shares traded and volatility series.derivatives trading; emerging markets; long-memory; range-based volatility; value of shares traded

Innovations in the clinical use of OCT

Optical coherence tomography (OCT) is an infrared light-based imaging modality with near-histological resolution (5-15μm) allowing the comprehensive evaluation of the vascular wall and intracoronary devices. This imaging modality has been widely used in clinical practice for the assessment of atherosclerosis and of the impact of interventions on the vascular wall, but also for the guidance of coronary intervention. The aim of this thesis is to outline the contemporary use of optical coherence tomography in clinical practice and summarize insights gained by this imaging modality into the acute and chronic vascular response after intravascular interventions. In specific, this thesis intends to: - Summarize the current status of OCT in clinical practice - Describe and validate new analysis tools for optical coherence tomography, that allow to overcome some of the current limitations - Examine the pathomechanisms of very late metallic stent failure with particular emphasis on the role of neoatherosclerosis - Assess the acute and chronic vascular healing response after bioresorbable vascular scaffold implantation and provide pilot observations regarding the pathomechanisms of early and late bioresorbable scaffold failure - Evaluate the acute effects of catheter-based renal denervation on the renal artery integrit

Medea: scheduling of long running applications in shared production clusters

The rise in popularity of machine learning, streaming, and latency-sensitive online applications in shared production clusters has raised new challenges for cluster schedulers. To optimize their performance and resilience, these applications require precise control of their placements, by means of complex constraints, e.g., to collocate or separate their long-running containers across groups of nodes. In the presence of these applications, the cluster scheduler must attain global optimization objectives, such as maximizing the number of deployed applications or minimizing the violated constraints and the resource fragmentation, but without affecting the scheduling latency of short-running containers. We present Medea, a new cluster scheduler designed for the placement of long- and short-running containers. Medea introduces powerful placement constraints with formal semantics to capture interactions among containers within and across applications. It follows a novel two-scheduler design: (i) for long-running containers, it applies an optimization-based approach that accounts for constraints and global objectives; (ii) for short-running containers, it uses a traditional task-based scheduler for low placement latency. Evaluated on a 400-node cluster, our implementation of Medea on Apache Hadoop YARN achieves placement of long-running applications with significant performance and resilience benefits compared to state-of-the-art schedulers

The informative role of trading volume in an expanding spot and futures market

This paper investigates the information content of trading volume and its relationship with range based volatility in the Indian stock market for the period 1995-2007. We examine the dynamics of the two variables and their respective uncertainties using a bivariate dual long-memory model. We distinguish between volume traded before and after the introduction of futures and options trading. We find that in all three periods the impact of both the number of trades and the value of shares traded on volatility is negative. This result is consistent with the argument that the activity of informed traders is inversely related to volatility when the marketplace has increased liquidity, an increasing number of active investors and high consensus among investors when new information is released. We also nd that (i) the introduction of futures trading leads to a decrease in spot volatility, (ii) volume decreases after the introduction of option contracts and, (iii) there are significant expiration day effects on both the value of shares traded and volatility series

Influence of randomly distributed magnetic nanoparticles on surface superconductivity in Nb films

We report on combined resistance and magnetic measurements in a hybrid structure (HS) of randomly distributed anisotropic CoPt magnetic nanoparticles (MN) embedded in a 160 nm Nb thick film. Our resistance measurements exhibited a sharp increase at the magnetically determined bulk upper-critical fields Hc2(T). Above these points the resistance curves are rounded, attaining the normal state value at much higher fields identified as the surface superconductivity fields Hc3(T). When plotted in reduced temperature units, the characteristic field lines Hc3(T) of the HS and of a pure Nb film, prepared at exactly the same conditions, coincide for H10 kOe they strongly segregate. Interestingly, the characteristic value H=10 kOe is equal to the saturation field of the MN. The behavior mentioned above is observed only for the case where the field is normal to the HS, while is absent when the field is parallel to the film. Our experimental results suggest that the observed enhancement of surface superconductivity field Hc3(T) is possibly due to the not uniform local reduction of the external magnetic field by the dipolar fields of the MN.Comment: to be published in Phys. Rev.

The usefulness of econometric models with stochastic volatility and long memory : applications for macroeconomic and financial time series

This study aims to examine the usefulness of econometric models with stochastic volatility and long memory in the application of macroeconomic and financial time series. An ARFIMA-FIAPARCH process is used to estimate the two main parameters driving the degree of persistence in the US real interest rate and its uncertainty. It provides evidence that the US real interest rates exhibit dual long memory and suggests that much more attention needs to be paid to the degree of persistence and its consequences for the economic theories which are still inconsistent with the finding of either near-unit-root or long memory mean-reverting behavior. A bivariate GARCH-type of model with/without long-memory is constructed to concern the issue of temporal ordering of inflation, output growth and their respective uncertainties as well as all the possible causal relationships among the four variables in the US/UK, allowing several lags of the conditional variances/levels used as regressors in the mean/variance equations. Notably, the findings are quite robust to changes in the specification of the model. The applicability and out-of-sample forecasting ability of a multivariate constant conditional correlation FIAPARCH model are analysed through a multi-country study of national stock market returns. This multivariate specification is generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, both the optimal fractional differencing parameter and power transformation are remarkably similar across countries.EThOS - Electronic Theses Online ServiceGBUnited Kingdo