Illiquidity shocks and the comovement between stocks:new evidence using smooth transition

This paper extends the smooth transition conditional correlation model by studying for the first time the impact that illiquidity shocks have on stock market return comovement. We show that firms that experience shocks that increase illiquidity are less liquid than firms that experience shocks that decrease illiquidity. Shocks that increase illiquidity have no statistical impact on comovement. However, shocks that reduce illiquidity lead to a fall in comovement, a pattern that becomes stronger as the illiquidity of the firm increases. This discovery is consistent with increased transparency and an improvement in price efficiency. We find that a small number of firms experience a double illiquidity shock. For these firms, at the first shock, a rise in illiquidity reduces comovement while a fall in illiquidity raises comovement. The second shock partly reverses these changes as a rise in illiquidity is associated with a rise in comovement and a fall in illiquidity is associated with a fall in comovement. These results have important implications for portfolio construction and also for the measurement and evolution of market beta and the cost of capital as it suggests that investors can achieve higher returns for the same amount of market risk because of the greater diversification benefits that exist. We also find that illiquidity, friction, firm size and the pre-shock correlation are all associated with the magnitude of the correlation change. © 2013 Elsevier B.V.

The relationship between CA/C ratio and individual differences in dynamic accommodative responses while viewing stereoscopic images

The oculomotor synergy as expressed by the CA/C and AC/A ratios was investigated to examine its influence on our previous observation that whereas convergence responses to stereoscopic images are generally stable, some individuals exhibit significant accommodative overshoot. Using a modified video refraction unit while viewing a stereoscopic LCD, accommodative and convergence responses to balanced and unbalanced vergence and focal stimuli (BVFS and UBVFS) were measured. Accommodative overshoot of at least 0.3 D was found in 3 out of 8 subjects for UBVFS. The accommodative response differential (RD) was taken to be the difference between the initial response and the subsequent mean static steady-state response. Without overshoot, RD was quantified by finding the initial response component. A mean RD of 0.11 +/- 0.27 D was found for the 1.0 D step UBVFS condition. The mean RD for the BVFS was 0.00 +/- 0.17 D. There was a significant positive correlation between CA/C ratio and RD (r = +0.75, n = 8, p <0.05) for only UBVFS. We propose that inter-subject variation in RD is influenced by the CA/C ratio as follows: an initial convergence response, induced by disparity of the image, generates convergence-driven accommodation commensurate with the CA/C ratio; the associated transient defocus subsequently decays to a balanced position between defocus-induced and convergence-induced accommodations.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

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.

Conditional volatility and firm size:an empirical analysis of UK equity portfolios

The techniques and insights from two distinct areas of financial economic modelling are combined to provide evidence of the influence of firm size on the volatility of stock portfolio returns. Portfolio returns are characterized by positive serial correlation induced by the varying levels of non-synchronous trading among the component stocks. This serial correlation is greatest for portfolios of small firms. The conditional volatility of stock returns has been shown to be well represented by the GARCH family of statistical processes. Using a GARCH model of the variance of capitalization-based portfolio returns, conditioned on the autocorrelation structure in the conditional mean, striking differences related to firm size are uncovered.

Anatomical correlates of dynamic auditory processing:relationship to literacy during early adolescence

Adults show great variation in their auditory skills, such as being able to discriminate between foreign speech-sounds. Previous research has demonstrated that structural features of auditory cortex can predict auditory abilities; here we are interested in the maturation of 2-Hz frequency-modulation (FM) detection, a task thought to tap into mechanisms underlying language abilities. We hypothesized that an individual's FM threshold will correlate with gray-matter density in left Heschl's gyrus, and that this function-structure relationship will change through adolescence. To test this hypothesis, we collected anatomical magnetic resonance imaging data from participants who were tested and scanned at three time points: at 10, 11.5 and 13 years of age. Participants judged which of two tones contained FM; the modulation depth was adjusted using an adaptive staircase procedure and their threshold was calculated based on the geometric mean of the last eight reversals. Using voxel-based morphometry, we found that FM threshold was significantly correlated with gray-matter density in left Heschl's gyrus at the age of 10 years, but that this correlation weakened with age. While there were no differences between girls and boys at Times 1 and 2, at Time 3 there was a relationship between gray-matter density in left Heschl's gyrus in boys but not in girls. Taken together, our results confirm that the structure of the auditory cortex can predict temporal processing abilities, namely that gray-matter density in left Heschl's gyrus can predict 2-Hz FM detection threshold. This ability is dependent on the processing of sounds changing over time, a skill believed necessary for speech processing. We tested this assumption and found that FM threshold significantly correlated with spelling abilities at Time 1, but that this correlation was found only in boys. This correlation decreased at Time 2, and at Time 3 we found a significant correlation between reading and FM threshold, but again, only in boys. We examined the sex differences in both the imaging and behavioral data taking into account pubertal stages, and found that the correlation between FM threshold and spelling was strongest pre-pubertally, and the correlation between FM threshold and gray-matter density in left Heschl's gyrus was strongest mid-pubertally.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

Dynamic models of exchange rate dependence using option prices and historical returns

Models for the conditional joint distribution of the U.S. Dollar/Japanese Yen and Euro/Japanese Yen exchange rates, from November 2001 until June 2007, are evaluated and compared. The conditional dependency is allowed to vary across time, as a function of either historical returns or a combination of past return data and option-implied dependence estimates. Using prices of currency options that are available in the public domain, risk-neutral dependency expectations are extracted through a copula repre- sentation of the bivariate risk-neutral density. For this purpose, we employ either the one-parameter \Normal" or a two-parameter \Gumbel Mixture" specification. The latter provides forward-looking information regarding the overall degree of covariation, as well as, the level and direction of asymmetric dependence. Specifications that include option-based measures in their information set are found to outperform, in-sample and out-of-sample, models that rely solely on historical returns.

Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.