Estimating stochastic volatility diffusion using conditional moments of integrated volatility

We exploit the distributional information contained in high-frequency intraday data in constructing a simple conditional moment estimator for stochastic volatility diffusions. The estimator is based on the analytical solutions of the first two conditional moments for the latent integrated volatility, the realization of which is effectively approximated by the sum of the squared high-frequency increments of the process. Our simulation evidence indicates that the resulting GMM estimator is highly reliable and accurate. Our empirical implementation based on high-frequency five-minute foreign exchange returns suggests the presence of multiple latent stochastic volatility factors and possible jumps. © 2002 Elsevier Science B.V. All rights reserved.

Prediction in dynamic models with time-dependent conditional variances

This paper considers forecasting the conditional mean and variance from a single-equation dynamic model with autocorrelated disturbances following an ARMA process, and innovations with time-dependent conditional heteroskedasticity as represented by a linear GARCH process. Expressions for the minimum MSE predictor and the conditional MSE are presented. We also derive the formula for all the theoretical moments of the prediction error distribution from a general dynamic model with GARCH(1, 1) innovations. These results are then used in the construction of ex ante prediction confidence intervals by means of the Cornish-Fisher asymptotic expansion. An empirical example relating to the uncertainty of the expected depreciation of foreign exchange rates illustrates the usefulness of the results. © 1992.

Volume, volatility, and leverage: A dynamic analysis

This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multi-step-ahead characteristics of a nonparametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.

Nonparametric estimation of structural models for high-frequency currency market data

Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975-90. © 1995.

A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets

Assuming that daily spot exchange rates follow a martingale process, we derive the implied time series process for the vector of 30-day forward rate forecast errors from using weekly data. The conditional second moment matrix of this vector is modelled as a multivariate generalized ARCH process. The estimated model is used to test the hypothesis that the risk premium is a linear function of the conditional variances and covariances as suggested by the standard asset pricing theory literature. Little supportt is found for this theory; instead lagged changes in the forward rate appear to be correlated with the 'risk premium.'. © 1990.

BMP signaling in the development of the mouse esophagus and forestomach.

The stratification and differentiation of the epidermis are known to involve the precise control of multiple signaling pathways. By contrast, little is known about the development of the mouse esophagus and forestomach, which are composed of a stratified squamous epithelium. Based on prior work in the skin, we hypothesized that bone morphogenetic protein (BMP) signaling is a central player. To test this hypothesis, we first used a BMP reporter mouse line harboring a BRE-lacZ allele, along with in situ hybridization to localize transcripts for BMP signaling components, including various antagonists. We then exploited a Shh-Cre allele that drives recombination in the embryonic foregut epithelium to generate gain- or loss-of-function models for the Bmpr1a (Alk3) receptor. In gain-of-function (Shh-Cre;Rosa26(CAG-loxpstoploxp-caBmprIa)) embryos, high levels of ectopic BMP signaling stall the transition from simple columnar to multilayered undifferentiated epithelium in the esophagus and forestomach. In loss-of-function experiments, conditional deletion of the BMP receptor in Shh-Cre;Bmpr1a(flox/flox) embryos allows the formation of a multilayered squamous epithelium but this fails to differentiate, as shown by the absence of expression of the suprabasal markers loricrin and involucrin. Together, these findings suggest multiple roles for BMP signaling in the developing esophagus and forestomach.

Effects of neuronal PIK3C3/Vps34 deletion on autophagy and beyond.

PIK3C3/Vps34 plays important roles in the endocytic and autophagic pathways, both of which are essential for maintaining neuronal integrity. However, it is unclear how inactivating PIK3C3 may affect neuronal endosomal versus autophagic processes in vivo. We generated a conditional null allele of the Pik3c3 gene in mouse, and specifically deleted it in postmitotic sensory neurons. Subsequent analyses reveal several interesting and surprising findings.

Carbon mitigation costs for the commercial building sector: Discrete-continuous choice analysis of multifuel energy demand

We estimate a carbon mitigation cost curve for the U.S. commercial sector based on econometric estimation of the responsiveness of fuel demand and equipment choices to energy price changes. The model econometrically estimates fuel demand conditional on fuel choice, which is characterized by a multinomial logit model. Separate estimation of end uses (e.g., heating, cooking) using the U.S. Commercial Buildings Energy Consumption Survey allows for exceptionally detailed estimation of price responsiveness disaggregated by end use and fuel type. We then construct aggregate long-run elasticities, by fuel type, through a series of simulations; own-price elasticities range from -0.9 for district heat services to -2.9 for fuel oil. The simulations form the basis of a marginal cost curve for carbon mitigation, which suggests that a price of $20 per ton of carbon would result in an 8% reduction in commercial carbon emissions, and a price of $100 per ton would result in a 28% reduction. © 2008 Elsevier B.V. All rights reserved.

Association between DNA damage response and repair genes and risk of invasive serous ovarian cancer.

BACKGROUND: We analyzed the association between 53 genes related to DNA repair and p53-mediated damage response and serous ovarian cancer risk using case-control data from the North Carolina Ovarian Cancer Study (NCOCS), a population-based, case-control study. METHODS/PRINCIPAL FINDINGS: The analysis was restricted to 364 invasive serous ovarian cancer cases and 761 controls of white, non-Hispanic race. Statistical analysis was two staged: a screen using marginal Bayes factors (BFs) for 484 SNPs and a modeling stage in which we calculated multivariate adjusted posterior probabilities of association for 77 SNPs that passed the screen. These probabilities were conditional on subject age at diagnosis/interview, batch, a DNA quality metric and genotypes of other SNPs and allowed for uncertainty in the genetic parameterizations of the SNPs and number of associated SNPs. Six SNPs had Bayes factors greater than 10 in favor of an association with invasive serous ovarian cancer. These included rs5762746 (median OR(odds ratio)(per allele) = 0.66; 95% credible interval (CI) = 0.44-1.00) and rs6005835 (median OR(per allele) = 0.69; 95% CI = 0.53-0.91) in CHEK2, rs2078486 (median OR(per allele) = 1.65; 95% CI = 1.21-2.25) and rs12951053 (median OR(per allele) = 1.65; 95% CI = 1.20-2.26) in TP53, rs411697 (median OR (rare homozygote) = 0.53; 95% CI = 0.35 - 0.79) in BACH1 and rs10131 (median OR( rare homozygote) = not estimable) in LIG4. The six most highly associated SNPs are either predicted to be functionally significant or are in LD with such a variant. The variants in TP53 were confirmed to be associated in a large follow-up study. CONCLUSIONS/SIGNIFICANCE: Based on our findings, further follow-up of the DNA repair and response pathways in a larger dataset is warranted to confirm these results.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).

Copper signaling axis as a target for prostate cancer therapeutics.

Previously published reports indicate that serum copper levels are elevated in patients with prostate cancer and that increased copper uptake can be used as a means to image prostate tumors. It is unclear, however, to what extent copper is required for prostate cancer cell function as we observed only modest effects of chelation strategies on the growth of these cells in vitro. With the goal of exploiting prostate cancer cell proclivity for copper uptake, we developed a "conditional lethal" screen to identify compounds whose cytotoxic actions were manifested in a copper-dependent manner. Emerging from this screen was a series of dithiocarbamates, which, when complexed with copper, induced reactive oxygen species-dependent apoptosis of malignant, but not normal, prostate cells. One of the dithiocarbamates identified, disulfiram (DSF), is an FDA-approved drug that has previously yielded disappointing results in clinical trials in patients with recurrent prostate cancer. Similarly, in our studies, DSF alone had a minimal effect on the growth of prostate cancer tumors when propagated as xenografts. However, when DSF was coadministered with copper, a very dramatic inhibition of tumor growth in models of hormone-sensitive and of castrate-resistant disease was observed. Furthermore, we determined that prostate cancer cells express high levels of CTR1, the primary copper transporter, and additional chaperones that are required to maintain intracellular copper homeostasis. The expression levels of most of these proteins are increased further upon treatment of androgen receptor (AR)-positive prostate cancer cell lines with androgens. Not surprisingly, robust CTR1-dependent uptake of copper into prostate cancer cells was observed, an activity that was accentuated by activation of AR. Given these data linking AR to intracellular copper uptake, we believe that dithiocarbamate/copper complexes are likely to be effective for the treatment of patients with prostate cancer whose disease is resistant to classical androgen ablation therapies.

Huntingtin is required for normal excitatory synapse development in cortical and striatal circuits.

Huntington's disease (HD) is a neurodegenerative disease caused by the expansion of a poly-glutamine (poly-Q) stretch in the huntingtin (Htt) protein. Gain-of-function effects of mutant Htt have been extensively investigated as the major driver of neurodegeneration in HD. However, loss-of-function effects of poly-Q mutations recently emerged as potential drivers of disease pathophysiology. Early synaptic problems in the excitatory cortical and striatal connections have been reported in HD, but the role of Htt protein in synaptic connectivity was unknown. Therefore, we investigated the role of Htt in synaptic connectivity in vivo by conditionally silencing Htt in the developing mouse cortex. When cortical Htt function was silenced, cortical and striatal excitatory synapses formed and matured at an accelerated pace through postnatal day 21 (P21). This exuberant synaptic connectivity was lost over time in the cortex, resulting in the deterioration of synapses by 5 weeks. Synaptic decline in the cortex was accompanied with layer- and region-specific reactive gliosis without cell loss. To determine whether the disease-causing poly-Q mutation in Htt affects synapse development, we next investigated the synaptic connectivity in a full-length knock-in mouse model of HD, the zQ175 mouse. Similar to the cortical conditional knock-outs, we found excessive excitatory synapse formation and maturation in the cortices of P21 zQ175, which was lost by 5 weeks. Together, our findings reveal that cortical Htt is required for the correct establishment of cortical and striatal excitatory circuits, and this function of Htt is lost when the mutant Htt is present.

Polymorphisms in the kinesin-like factor 1 B gene and risk of epithelial ovarian cancer in Eastern Chinese women.

The kinesin-like factor 1 B (KIF1B) gene plays an important role in the process of apoptosis and the transformation and progression of malignant cells. Genetic variations in KIF1B may contribute to risk of epithelial ovarian cancer (EOC). In this study of 1,324 EOC patients and 1,386 cancer-free female controls, we investigated associations between two potentially functional single nucleotide polymorphisms in KIF1B and EOC risk by the conditional logistic regression analysis. General linear regression model was used to evaluate the correlation between the number of variant alleles and KIF1B mRNA expression levels. We found that the rs17401966 variant AG/GG genotypes were significantly associated with a decreased risk of EOC (adjusted odds ratio (OR) = 0.81, 95 % confidence interval (CI) = 0.68-0.97), compared with the AA genotype, but no associations were observed for rs1002076. Women who carried both rs17401966 AG/GG and rs1002076 AG/AA genotypes of KIF1B had a 0.82-fold decreased risk (adjusted 95 % CI = 0.69-0.97), compared with others. Additionally, there was no evidence of possible interactions between about-mentioned co-variants. Further genotype-phenotype correlation analysis indicated that the number of rs17401966 variant G allele was significantly associated with KIF1B mRNA expression levels (P for GLM = 0.003 and 0.001 in all and Chinese subjects, respectively), with GG carriers having the lowest level of KIF1B mRNA expression. Taken together, the rs17401966 polymorphism likely regulates KIF1B mRNA expression and thus may be associated with EOC risk in Eastern Chinese women. Larger, independent studies are warranted to validate our findings.

PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.

Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.