Volatility changes caused by the trading system:a Markov switching application

An expanding literature exists to suggest that the trading mechanism can influence the volatility of security returns. This study adds to this literature by examining the impact that the introduction of SETS, on the London Stock Exchange, had on the volatility of security returns. Using a Markov switching regime change model security volatility is categorized as being in a regime of either high or low volatility. It is shown that prior to the introduction of SETS securities tended to be in a low volatility regime. At the time SETS was introduced securities moved to a high volatility regime. This suggests that volatility increased when SETS was introduced.

A Markov switching unobserved component analysis of the CDX index term premium

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX index into a permanent and a stationary component. We establish that the inversion of the CDX term premium is induced by sudden changes in the unobserved stationary component, which represents the evolution of the fundamentals underpinning the probability of default in the economy. We find evidence that the monetary policy response from the Fed during the crisis period was effective in reducing the volatility of the term premium. We also show that equity returns make a substantial contribution to the term premium over the entire sample period.

Modelling financial time series with switching state space models

The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods.

Analysing time series structure with hidden Markov models

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.

Time delay estimation with hidden Markov models

Most traditional methods for extracting the relationships between two time series are based on cross-correlation. In a non-linear non-stationary environment, these techniques are not sufficient. We show in this paper how to use hidden Markov models to identify the lag (or delay) between different variables for such data. Adopting an information-theoretic approach, we develop a procedure for training HMMs to maximise the mutual information (MMI) between delayed time series. The method is used to model the oil drilling process. We show that cross-correlation gives no information and that the MMI approach outperforms maximum likelihood.

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Switching the mode of metabolism in the yeast Saccharomyces cerevisiae

The biochemistry of most metabolic pathways is conserved from bacteria to humans, although the control mechanisms are adapted to the needs of each cell type. Oxygen depletion commonly controls the switch from respiration to fermentation. However, Saccharomyces cerevisiae also controls that switch in response to the external glucose level. We have generated an S. cerevisiae strain in which glucose uptake is dependent on a chimeric hexose transporter mediating reduced sugar uptake. This strain shows a fully respiratory metabolism also at high glucose levels as seen for aerobic organisms, and switches to fermentation only when oxygen is lacking. These observations illustrate that manipulating a single step can alter the mode of metabolism. The novel yeast strain is an excellent tool to study the mechanisms underlying glucose-induced signal transduction. © 2004 European Molecular Biology Organization.

Enterprise networking:multilayer switching and applications

Primarily targeted toward the network or MIS manager who wants to stay abreast of the latest networking technology, Enterprise Networking: Multilayer Switching and Applications offers up to date information relevant for the design of modern corporate networks and for the evaluation of new networking equipment. The book describes the architectures and standards of switching across the various protocol layers and will also address issues such as multicast quality of service, high-availability and network policies that are requirements of modern switched networks.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Bandpass switching in a nonlinear optical loop mirror

We demonstrate bandpass nonlinear switching, using a novel device configuration based on a nonlinear-optical loop mirror and an in-fiber Bragg grating. Self-switching is demonstrated in the soliton regime by use of an asymmetrically arranged in-fiber Bragg grating as a wavelength-selective element. In addition, we adapt the configuration to perform efficient two-wavelength switching.

Soliton switching using cascaded nonlinear-optical loop mirrors

We demonstrate multiple-peaked switching in a nonlinear-optical loop mirror and present an experimental investigation of device cascading in the soliton regime based on a sequence of two independent nonlinear-optical loop mirrors. Cascading leads to an enhanced switching response with sharper switching edges, flattened peaks, and increased interpeak extinction ratios. We observe that pulses emerging from the cascade retain the sech2 temporal profile of a soliton with minimal degradation in the spectral characteristics.

Packet switching in wide area broadband private networks

B-ISDN is a universal network which supports diverse mixes of service, applications and traffic. ATM has been accepted world-wide as the transport technique for future use in B-ISDN. ATM, being a simple packet oriented transfer technique, provides a flexible means for supporting a continuum of transport rates and is efficient due to possible statistical sharing of network resources by multiple users. In order to fully exploit the potential statistical gain, while at the same time provide diverse service and traffic mixes, an efficient traffic control must be designed. Traffic controls which include congestion and flow control are a fundamental necessity to the success and viability of future B-ISDN. Congestion and flow control is difficult in the broadband environment due to the high speed link, the wide area distance, diverse service requirements and diverse traffic characteristics. Most congestion and flow control approaches in conventional packet switched networks are reactive in nature and are not applicable in the B-ISDN environment. In this research, traffic control procedures mainly based on preventive measures for a private ATM-based network are proposed and their performance evaluated. The various traffic controls include CAC, traffic flow enforcement, priority control and an explicit feedback mechanism. These functions operate at call level and cell level. They are carried out distributively by the end terminals, the network access points and the internal elements of the network. During the connection set-up phase, the CAC decides the acceptance or denial of a connection request and allocates bandwidth to the new connection according to three schemes; peak bit rate, statistical rate and average bit rate. The statistical multiplexing rate is based on a `bufferless fluid flow model' which is simple and robust. The allocation of an average bit rate to data traffic at the expense of delay obviously improves the network bandwidth utilisation.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.