Bayesian Assessment of Dynamic Quantile Forecasts

Methods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant marginal likelihoods involved, analytic integration methods are utilised when possible, otherwise multivariate adaptive quadrature methods are employed to estimate the required quantities. The usual Bayesian interval estimate for a proportion is also examined in this context. The size and power properties of the proposed methods are examined via a simulation study, illustrating favourable comparisons both overall and with their frequentist counterparts. An empirical study employs the proposed methods, in comparison with standard tests, to assess the adequacy of a range of forecasting models for Value at Risk (VaR) in several financial market data series

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisi

Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four error probability distributions: Gaussian, Student-t, skewed-t and generalized error distribution. Adaptive Markov chain Monte Carlo methods are employed in estimation and forecasting. A portfolio of four Asia-Pacific stock markets is considered. Two forecasting periods are evaluated in light of the recent global financial crisis. Results reveal that: (i) GARCH models out-performed stochastic volatility models in almost all cases; (ii) asymmetric volatility models were clearly favoured pre-crisis; while at the 1% level during and post-crisis, for a 1 day horizon, models with skewed-t errors ranked best, while IGARCH models were favoured at the 5% level; (iii) all models forecasted VaR less accurately and anti-conservatively post-crisi

Epitope recognition of peptide-imprinted polymers for Regenerating protein 1 (REG1)

Molecularly imprinted polymers (MIPs) were developed to replace natural antibodies with a cost-effective and durable synthetic material. Molecular imprinting of proteins conventionally utilizes the whole protein as the template, which is complex (as many different epitopes may be imprinted) and expensive. In this work, seven peptides (13–18 amino acids) were synthesized and used as templates for the imprinting and recognition of Regenerating Protein 1 (REG1). REG1 is involved in the proliferation and differentiation of diverse cell types, and was recently described as a urinary biomarker for pancreatic ductal adenocarcinoma (PDAC). Peptide-imprinted poly(ethylene-co-vinyl alcohol)s (PIPs), containing four different mole fractions of ethylene were cast on screen-printed electrodes to find the optimum composition for both the sensing and the extraction of REG1 in an E. coli culture medium. Peptides with fewer than 16 amino acids and two or three aromatic and hydrophobic groups have a higher affinity for MIPs of poly(ethylene-co-vinyl alcohol) (EVAL) with 27 mol% of ethylene, while those with four aromatic and hydrophobic groups have a higher affinity for MIPs with EVALs that contain 32 mol% of ethylene. The peptide / EVAL combination that maximized both imprinting effectiveness and response to REG1B was the sequence NEDRETWVDADLY imprinted into 32 mol% EVAL. This EVAL composition and template peptide were then modified by incorporation of magnetic nanoparticles, thus extending applications for PIPs to include extraction of REG1 protein from E. coli culture medium

Complexity dichotomy on partial grid recognition

Deciding whether a graph can be embedded in a grid using only unit-length edges is NP-complete, even when restricted to binary trees. However, it is not difficult to devise a number of graph classes for which the problem is polynomial, even trivial. A natural step, outstanding thus far, was to provide a broad classification of graphs that make for polynomial or NP-complete instances. We provide such a classification based on the set of allowed vertex degrees in the input graphs, yielding a full dichotomy on the complexity of the problem. As byproducts, the previous NP-completeness result for binary trees was strengthened to strictly binary trees, and the three-dimensional version of the problem was for the first time proven to be NP-complete. Our results were made possible by introducing the concepts of consistent orientations and robust gadgets, and by showing how the former allows NP-completeness proofs by local replacement even in the absence of the latter

Coping with COVID-19: Exposure to COVID-19 and Negative Impact on Livelihood Predict Elevated Mental Health Problems in Chinese Adults

The COVID-19 pandemic might lead to more mental health problems. However, few studies have examined sleep problems, depression, and posttraumatic symptoms among the general adult population during the COVID-19 outbreak, and little is known about coping behaviors. This survey was conducted online in China from February 1st to February 10th, 2020. Quota sampling was used to recruit 2993 Chinese citizens aged ≥18 years old. Mental health problems were assessed with the Post-Traumatic Stress Disorders (PTSD) Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the Center for Epidemiological Studies Depression inventory, and the Pittsburgh Sleep Quality Index. Exposure to COVID-19 was measured with questions about residence at outbreak, personal exposure, media exposure, and impact on livelihood. General coping style was measured by the brief Coping Style Questionnaire (SCSQ). Respondents were also asked 12 additional questions about COVID-19 specific coping behaviors. Direct exposure to COVID-19 instead of the specific location of (temporary) residence within or outside the epicenter (Wuhan) of the pandemic seems important (standardized beta: 0.05, 95% confidence interval (CI): 0.02-0.09). Less mental health problems were also associated with less intense exposure through the media (standardized beta: -0.07, 95% CI: -0.10--0.03). Perceived negative impact of the pandemic on livelihood showed a large effect size in predicting mental health problems (standardized beta: 0.15, 95% CI: 0.10-0.19). More use of cognitive and prosocial coping behaviors were associated with less mental health problems (standardized beta: -0.30, 95% CI: -0.34--0.27). Our study suggests that the mental health consequences of the lockdown impact on livelihood should not be underestimated. Building on cognitive coping behaviors reappraisal or cognitive behavioral treatments may be most promising

Submergence of the Sidebands in the Photon-assisted Tunneling through a Quantum Dot Weakly Coupled to Luttinger Liquid Leads

We study theoretically the photon-assisted tunneling through a quantum dot weakly coupled to Luttinger liquids (LL) leads, and find that the zero bias dc conductance is strongly affected by the interactions in the LL leads. In comparison with the system with Fermi liquid (FL) leads, the sideband peaks of the dc conductance become blurring for 1/2<g<1, and finally merge into the central peak for g<1/2, (g is the interaction parameter in the LL leads). The sidebands are suppressed for LL leads with Coulomb interactions strong enough, and the conductance always appears as a single peak for any strength and frequency of the external time-dependent field. Furthermore, the quenching effect of the central peak for the FL case does not exist for g<1/2.Comment: 9 pages, 4 figure

Searching a bitstream in linear time for the longest substring of any given density

Given an arbitrary bitstream, we consider the problem of finding the longest substring whose ratio of ones to zeroes equals a given value. The central result of this paper is an algorithm that solves this problem in linear time. The method involves (i) reformulating the problem as a constrained walk through a sparse matrix, and then (ii) developing a data structure for this sparse matrix that allows us to perform each step of the walk in amortised constant time. We also give a linear time algorithm to find the longest substring whose ratio of ones to zeroes is bounded below by a given value. Both problems have practical relevance to cryptography and bioinformatics.Comment: 22 pages, 19 figures; v2: minor edits and enhancement

YamSat: the First Picosatellite being Developed in Taiwan

This paper describes the current planning and design of the YamSat, the first picosatellite being developed in Taiwan. The design, analysis, manufacture, integration, test and operation of the YamSat will be performed by the National Space Program Office (NSPO), Taiwan, R.O.C, in cooperation with other domestic organizations and companies. It is a member of the CubeSat [1], 10cm x 10cm x 10cm size and within 1kg mass. The major objective of the YamSat is to qualify in space the components and technology developed in Taiwan, including a micro-spectrometer payload using Micro Electro Mechanical Systems (MEMS) technology. The YamSat will be ready for flight in the middle of 2002

Dissipative Dynamics of a Josephson Junction In the Bose-Gases

The dissipative dynamics of a Josephson junction in the Bose-gases is considered within the framework of the model of a tunneling Hamiltonian. The effective action which describes the dynamics of the phase difference across the junction is derived using functional integration method. The dynamic equation obtained for the phase difference across the junction is analyzed for the finite temperatures in the low frequency limit involving the radiation terms. The asymmetric case of the Bose-gases with the different order parameters is calculated as well