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Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect.

[Background] The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case–control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. ; [Methods] The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. ; [Results] By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. ; [Conclusions] Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification. ; AG was funded by a Methodology Research Fellowship from Medical Research Council UK (grant ID G1002296). AT was supported by a Salvador Madariaga's grant of the Ministry of Education of the Spanish Government. ; Peer reviewed

PurposeThis paper aims to examine the pricing effects of risks conditional on market situations.Design/methodology/approachThe model used to test for the conditional pricing effects of risks is a modified version of Pettengill et al.'s cross‐sectional regression model, based on Hong Kong equity data.FindingsThe paper postulates a five‐factor asset pricing model, which hypothesizes that five risk factors are relevant in the pricing of equity stocks, namely beta, size, book‐to‐market equity, market leverage, and share price, but conditional on market situations, i.e. whether the market is up or down.Practical implicationsThe findings enrich our understanding of capital market behaviour, and should prove helpful to investors and corporate managers in both their domestic and international financial decisions.Originality/valueThis study yields important results on a Chinese market, which lend support to the conditional risk pricing hypotheses originally developed in the US, implying that conditional risk pricing is applicable not only in the US market but also in other markets around the globe.

With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so‐called re‐weighted Nadaraya‐Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well‐known Nadaraya‐Watson smoother. With a detailed asymptotic analysis, we demonstrate that the RNW smoother preserves the superior large‐sample bias property of the local linear smoother of the conditional density recently proposed in the literature. As a matter of independent statistical interest, the limit distribution of the RNW estimator is also derived.

This article enquires whether modern practices of punishment are undergoing a transformation by investigating the effect that the development of citizenship has had upon modern punishment. Citizenship requires that people expand their horizons of recognition and treat people, who had previously been disregarded, as equals. This is one of the primary cultural influences upon punishment and explains why modern punishment has always partly been an inclusionary project. The effect of citizenship is ambivalent as punishment is designed not only to incorporate offenders but also to underline their `conditional' status as citizens, hence the persistence of brutal treatment. This article traces the influence of citizenship, through the emergence of civil, political and social rights, upon punishment from the late 18th century to the present day. It argues that the `inclusionary' aspect of punishment is waning as the conditions of citizenship are becoming ever more stringent.

Spending based fiscal adjustment requires decisions on when? and how much to cut public spending to minimise adveræ effects on economic growth. This requires precise estimates of the response of output to changes in the functional components of public spending. I call this the functional multiplier.I disaggregate government spending in 36 low-income countries over the Period 1984-2013 into its functional components. Using a GMM-IV model with fuel subsidies as the instrument for government spending I exploit difference in the length of exposure to statehood as a proxy for current absorptive capacity to estimate functional multipliers conditional on absorptive capacity.The estimated functional multipliers vary from -1.11 for economic ærvices to 1.82 for social protection. I find that a one standard deviation change in absorptive capacity yields a 18 percent larger multiplier compared to the average level of absorptive capacity. A similar exercise for contestability shows that a one standard deviation change in contestability yields a 14 percent larger multiplier compared to the average level of absorptive capacity. I subject the GMM-IV estimates to a rigorous test to determine whether the estimated relationships an causal and not simply spurious correlation. Post-double LASSO estimates show that that the conditional functional multipliers are precisely estimated and of the same order of magnitude (though slightly larger) than the GMM-IV estimates. To obtain multipliers for individual countries (and not averages) I use a Bayesian iterative shrinkage estimators. The shrinkage estimates show that countries with higher than average absorptive capacity have multipliers that are 15 percent larger than thoæ below the average.