The benefit of modeling jumps in realized volatility for risk prediction : evidence from Chinese mainland stocks

Recent literature has focused on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility models can largely improve VaR prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.

Effects of free flow state highway traffic parameters on the performance of amplify and forward cooperative multi-hop vehicular networks

In this paper we analyse the effects of highway traffic flow parameters like vehicle arrival rate and density on the performance of Amplify and Forward (AF) cooperative vehicular networks along a multi-lane highway under free flow state. We derive analytical expressions for connectivity performance and verify them with Monte-Carlo simulations. When AF cooperative relaying is employed together with Maximum Ratio Combining (MRC) at the receivers the average route error rate shows 10-20 fold improvement compared to direct communication. A 4-8 fold increase in maximum number of traversable hops can also be observed at different vehicle densities when AF cooperative communication is used to strengthen communication routes. However the theorical upper bound of maximum number of hops promises higher performance gains.

Pseudo-marginal algorithms with multiple CPUs

The emergence of pseudo-marginal algorithms has led to improved computational efficiency for dealing with complex Bayesian models with latent variables. Here an unbiased estimator of the likelihood replaces the true likelihood in order to produce a Bayesian algorithm that remains on the marginal space of the model parameter (with latent variables integrated out), with a target distribution that is still the correct posterior distribution. Very efficient proposal distributions can be developed on the marginal space relative to the joint space of model parameter and latent variables. Thus psuedo-marginal algorithms tend to have substantially better mixing properties. However, for pseudo-marginal approaches to perform well, the likelihood has to be estimated rather precisely. This can be difficult to achieve in complex applications. In this paper we propose to take advantage of multiple central processing units (CPUs), that are readily available on most standard desktop computers. Here the likelihood is estimated independently on the multiple CPUs, with the ultimate estimate of the likelihood being the average of the estimates obtained from the multiple CPUs. The estimate remains unbiased, but the variability is reduced. We compare and contrast two different technologies that allow the implementation of this idea, both of which require a negligible amount of extra programming effort. The superior performance of this idea over the standard approach is demonstrated on simulated data from a stochastic volatility model.

Spin-polarization and ferromagnetism of graphitic carbon nitride materials

Polymeric graphitic carbon nitride materials have attracted increasing attention in recent years owning to their potential applications in energy conversion, environment protection, and so on. Here, from first-principles calculations, we report the electronic structure modification of graphitic carbon nitride (g-C3N4) in response to carbon doping. We showed that each dopant atom can induce a local magnetic moment of 1.0 μB in non-magnetic g-C3N4. At the doping concentration of 1/14, the local magnetic moments of the most stable doping configuration which has the dopant atom at the center of heptazine unit prefer to align in a parallel way leading to long-range ferromagnetic (FM) ordering. When the joint N atom is replaced by C atom, the system favors an antiferromagnetic (AFM) ordering at unstrained state, but can be tuned to ferromagnetism (FM) by applying biaxial tensile strain. More interestingly, the FM state of the strained system is half-metallic with abundant states at the Fermi level in one spin channel and a band gap of 1.82 eV in another spin channel. The Curie temperature (Tc) was also evaluated using a mean-field theory and Monte Carlo simulations within the Ising model. Such tunable electron spin-polarization and ferromagnetism are quite promising for the applications of graphitic carbon nitride in spintronics.

In-cylinder pressure and inter-cycle variability analysis for a compression ignition engine : Bayesian approaches

This thesis introduced Bayesian statistics as an analysis technique to isolate resonant frequency information in in-cylinder pressure signals taken from internal combustion engines. Applications of these techniques are relevant to engine design (performance and noise), energy conservation (fuel consumption) and alternative fuel evaluation. The use of Bayesian statistics, over traditional techniques, allowed for a more in-depth investigation into previously difficult to isolate engine parameters on a cycle-by-cycle basis. Specifically, these techniques facilitated the determination of the start of pre-mixed and diffusion combustion and for the in-cylinder temperature profile to be resolved on individual consecutive engine cycles. Dr Bodisco further showed the utility of the Bayesian analysis techniques by applying them to in-cylinder pressure signals taken from a compression ignition engine run with fumigated ethanol.

Estimating the robustness of long-haul train plans

In Australia, and elsewhere, the movement of trains on long-haul rail networks is usually planned in advance. Typically, a train plan is developed to confirm that the required train movements and track maintenance activities can occur. The plan specifies when track segments will be occupied by particular trains and maintenance activities. On the day of operation, a train controller monitors and controls the movement of trains and maintenance crews, and updates the train plan in response to unplanned disruptions. It can be difficult to predict how good a plan will be in practice. The main performance indicator for a train service should be reliability - the proportion of trains running the service that complete at or before the scheduled time. We define the robustness of a planned train service to be the expected reliability. The robustness of individual train services and for a train plan as a whole can be estimated by simulating the train plan many times with random, but realistic, perturbations to train departure times and segment durations, and then analysing the distributions of arrival times. This process can also be used to set arrival times that will achieve a desired level of robustness for each train service.

Polymorphisms of the SIPA1 gene and sporadic breast cancer susceptibility

Background The novel breast cancer metastasis modulator gene signal-induced proliferation-associated 1 (Sipa1) underlies the breast cancer metastasis efficiency modifier locus Mtes 1 and has been shown to influence mammary tumour metastatic efficiency in the mouse, with an ectopically expressing Sipa1 cell line developing 1.5 to 2 fold more surface pulmonary metastases. Sipa1 encodes a mitogen-inducible GTPase activating (GAP) protein for members of the Ras-related proteins; participates in cell adhesion and modulates mitogen-induced cell cycle progression. Germline SIPA1 SNPs showed association with positive lymph node metastasis and hormonal receptor status in a Caucasian cohort. We hypothesized that SIPA1 may also be correlated to breast carcinoma incidence as well as prognosis. Therefore, this study investigated the potential relationship of SIPA1 and human breast cancer incidence by a germline SNP genotype frequency association study in a case-control Caucasian cohort in Queensland, Australia. Methods The SNPs genotyped in this study were identified in a previous study and the genotyping assays were carried out using TaqMan SNP Genotyping Assays. The data were analysed with chi-square method and the Monte Carlo style CLUMP analysis program. Results Results indicated significance with SIPA1 SNP rs3741378; the CC genotype was more frequently observed in the breast cancer group compared to the disease-free control group, indicating the variant C allele was associated with increased breast cancer incidence. Conclusion This observation indicates SNP rs3741378 as a novel potential sporadic breast cancer predisposition SNP. While it showed association with hormonal receptor status in breast cancer group in a previous pilot study, this exonic missense SNP (Ser (S) to Phe (F)) changes a hydrophilic residue (S) to a hydrophobic residue (F) and may significantly alter the protein functions of SIPA1 in breast tumourgenesis. SIPA1 SNPs rs931127 (5' near gene), and rs746429 (synonymous (Ala (A) to Ala (A)), did not show significant associations with breast cancer incidence, yet were associated with lymph node metastasis in the previous study. This suggests that SIPA1 may be involved in different stages of breast carcinogenesis and since this study replicates a previous study of the associated SNP, it implicates variants of the SIPA1 gene as playing a potential role in breast cancer.

A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Investigation of a neuronal nitric oxide synthase gene (NOS1) polymorphism in a multiple sclerosis population

Multiple Sclerosis (MS) is a chronic neurological disease characterized by demyelination associated with infiltrating white blood cells in the central nervous system (CNS). Nitric oxide synthases (NOS) are a family of enzymes that control the production of nitric oxide. It is possible that neuronal NOS could be involved in MS pathophysiology and hence the nNOS gene is a potential candidate for involvement in disease susceptibility. The aim of this study was to determine whether allelic variation at the nNOS gene locus is associated with MS in an Australian cohort. DNA samples obtained from a Caucasian Australian population affected with MS and an unaffected control population, matched for gender, age and ethnicity, were genotyped for a microsatellite polymorphism in the promoter region of the nNOS gene. Allele frequencies were compared using chi-squared based statistical analyses with significance tested by Monte Carlo simulation. Allelic analysis of MS cases and controls produced a chi-squared value of 5.63 with simulated P = 0.96 (OR(max) = 1.41, 95% CI: 0.926-2.15). Similarly, a Mann-Whitney U analysis gave a non-significant P-value of 0.377 for allele distribution. No differences in allele frequencies were observed for gender or clinical course subtype (P > 0.05). Statistical analysis indicated that there is no association of this nNOS variant and MS and hence the gene does not appear to play a genetically significant role in disease susceptibility.

A linkage and cross-sectional study of hypertension and obesity using a poly(A) Alu-repeat polymorphism at the glucagon receptor gene locus (17q25)

1. Previous glucagon receptor gene (GCGR) studies have shown a Gly40Ser mutation to be more prevalent in essential hypertension and to affect glucagon binding affinity to its receptor. An Alu-repeat poly(A) polymorphism colocalized to GCGR was used in the present study to test for association and linkage in hypertension as well as association in obesity development. 2. Using a cross-sectional approach, 85 hypertensives and 95 normotensives were genotyped using polymerase chain reaction primers flanking the Alu-repeat. Both hypertensive and normotensive populations were subdivided into lean and obese categories based on body mass index (BMI) to determine involvement of this variant in obesity. For the linkage study, 89 Australian Caucasian hypertension affected sibships (174 sibpairs) were genotyped and the results were analysed using GENE-HUNTER, Mapmaker Sibs, ERPA and SPLINK (all freely available from http://linlkage.rockefeller. edu/soft/list.html). 3. Cross-sectional results for both hypertension and obesity were analysed using Chi-squared and Monte Carlo analyses. Results did not show an association of this variant with either hypertension (χ2 = 6.9, P = 0.14; Monte Carlo χ2 = 7.0, P = 0.11; n = 5000) or obesity (χ2 = 3.3, P = 0.35; Monte Carlo χ2 = 3.26, P = 0.34; n = 5000). In addition, results from the linkage study using hypertensive sib-pairs did not indicate linkage of the poly(A) repent with hypertension. Hence, results did not indicate a role far the Alu-repeat in either hypertension or obesity. However, as the heterozygosity of this poly(A) repeat is low (35%), a larger number of hypertensive sib-pairs may be required to draw definitive conclusions.

Sibpair studies implicate chromosome 18 in essential hypertension

Interest in chromosome 18 in essential hypertension comes from comparative mapping of rat blood pressure quantitative trait loci (QTL), familial orthostatic hypotensive syndrome studies, and essential hypertension pedigree linkage analyses indicating that a locus or loci on human chromosome 18 may play a role in hypertension development. To further investigate involvement of chromosome 18 in human essential hypertension, the present study utilized a linkage scan approach to genotype twelve microsatellite markers spanning human chromosome 18 in 177 Australian Caucasian hypertensive (HT) sibling pairs. Linkage analysis showed significant excess allele sharing of the D18S61 marker when analyzed with SPLINK (P=0.00012), ANALYZE (Sibpair) (P=0.0081), and also with MAPMAKER SIBS (P=0.0001). Similarly, the D18S59 marker also showed evidence for excess allele sharing when analyzed with SPLINK (P=0.016), ANALYZE (Sibpair) (P=0.0095), and with MAPMAKER SIBS (P = 0.014). The adenylate cyclase activating polypeptide 1 gene (ADCYAP1) is involved in vasodilation and has been co-localized to the D18S59 marker. Results testing a microsatellite marker in the 3′ untranslated region of ADCYAP1 in age and gender matched HT and normotensive (NT) individuals showed possible association with hypertension (P = 0.038; Monte Carlo P = 0.02), but not with obesity. The present study shows a chromosome 18 role in essential hypertension and indicates that the genomic region near the ADCYAP1 gene or perhaps the gene itself may be implicated. Further investigation is required to conclusively determine the extent to which ADCYAP1 polymorphisms are involved in essential hypertension. © 2003 Wiley-Liss, Inc.

Diffusion tensor of water in partially aligned fibre networks

Monte Carlo simulations were used to investigate the relationship between the morphological characteristics and the diffusion tensor (DT) of partially aligned networks of cylindrical fibres. The orientation distributions of the fibres in each network were approximately uniform within a cone of a given semi-angle (θ0). This semi-angle was used to control the degree of alignment of the fibres. The networks studied ranged from perfectly aligned (θ0 = 0) to completely disordered (θ0 = 90°). Our results are qualitatively consistent with previous numerical models in the overall behaviour of the DT. However, we report a non-linear relationship between the fractional anisotropy (FA) of the DT and collagen volume fraction, which is different to the findings from previous work. We discuss our results in the context of diffusion tensor imaging of articular cartilage. We also demonstrate how appropriate diffusion models have the potential to enable quantitative interpretation of the experimentally measured diffusion-tensor FA in terms of collagen fibre alignment distributions.

Geographic remoteness, area-level socioeconomic disadvantage and inequalities in colorectal cancer survival in Queensland : a multilevel analysis

Background To explore the impact of geographical remoteness and area-level socioeconomic disadvantage on colorectal cancer (CRC) survival. Methods Multilevel logistic regression and Markov chain Monte Carlo simulations were used to analyze geographical variations in five-year all-cause and CRC-specific survival across 478 regions in Queensland Australia for 22,727 CRC cases aged 20–84 years diagnosed from 1997–2007. Results Area-level disadvantage and geographic remoteness were independently associated with CRC survival. After full multivariate adjustment (both levels), patients from remote (odds Ratio [OR]: 1.24, 95%CrI: 1.07-1.42) and more disadvantaged quintiles (OR = 1.12, 1.15, 1.20, 1.23 for Quintiles 4, 3, 2 and 1 respectively) had lower CRC-specific survival than major cities and least disadvantaged areas. Similar associations were found for all-cause survival. Area disadvantage accounted for a substantial amount of the all-cause variation between areas. Conclusions We have demonstrated that the area-level inequalities in survival of colorectal cancer patients cannot be explained by the measured individual-level characteristics of the patients or their cancer and remain after adjusting for cancer stage. Further research is urgently needed to clarify the factors that underlie the survival differences, including the importance of geographical differences in clinical management of CRC.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution