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This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances and correlations of daily returns based on measures of realized variances and correlations built from intraday data. Formulas for multi-step forecasts of conditional variances and correlations are provided. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecasts the scalar HEAVY models outperform the scalar BEKK-HEAVY model based on realized covariances and the scalar BEKK, DCC, and DECO multivariate GARCH models based exclusively on daily data.

We propose a way of testing a subset of equations of a DSGE model. The test draws on statistical inference for limited information models and the use of indirect inference to test DSGE models. Using the numerical small sample distribution of our test for two subsets of equations of the Smets-Wouters model we show that the test has accurate size and good power in small samples, and better power than using asymptotic distribution theory. In a test of the Smets-Wouters model on US Great Moderation data we reject the specification of the wage-price but not the expenditure sector. This points to the wage-price sector as the source of overall model rejection.

We examine the empirical evidence bearing on whether UK trade is governed by a Classical model or by a Gravity model, using annual data from 1965 to 2015 and the method of Indirect Inference which has very large power in this application. The Gravity model here differs from the Classical model in assuming imperfect competition and a positive effect of total trade on productivity. We found that the Classical model passed the test comfortably, and that the Gravity model also passed it but at a rather lower level of probability, though as the test power was raised it was rejected. The two models’ policy implications are similar.

We develop a general form logarithmic vector multiplicative error model (log-vMEM). The log-vMEM improves on existing models in two ways. First, it is a more general form model as it allows the error terms to be cross-dependent and relaxes weak exogeneity restrictions. Second, the log-vMEM specification guarantees that the conditional means are non-negative without any restrictions imposed on the parameters. We further propose a multivariate lognormal distribution and a joint maximum likelihood estimation strategy. The model is applied to high frequency data associated with a number of NYSE-listed stocks. The results reveal empirical support for full interdependence of trading duration, volume and volatility, with the log-vMEM providing a better fit to the data than a competing model. Moreover, we find that unexpected duration and volume dominate observed duration and volume in terms of information content, and that volatility and volatility shocks affect duration in different directions. These results are interpreted with reference to extant microstructure theory.

We review recent findings in the application of indirect inference to DSGE models. We show that researchers should tailor the power of their test to the model under investigation in order to achieve a balance between high power and finding a robust model; this will involve choosing only a limited number of variables on whose behaviour they should focus. Also recent work reveals that it makes little difference which these variables are or how their behaviour is measured whether via a VAR, IRFs or moments. We also review identification issues, how to test part of a model and whether alternative evaluation methods such as forecasting or likelihood ratio tests are potentially helpful.

Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, in small samples power can differ. We explore small sample power with three different auxiliary models: a VAR, average Impulse Response Functions and Moments. The latter corresponds to the Simulated Moments Method. We find that in a small macro model there is no difference in power. But in a large complex macro model the power with Moments rises more slowly with increasing misspecification than with the other two which remain similar.

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Even though the volatility spillover effects in global equity markets have been documented extensively, the transmission of illiquidity across national borders has not. In this paper, we propose a multiplicative error model (MEM) for the dynamics of illiquidity. We empirically study the illiquidity and volatility spillover effects in eight developed equity markets during and after the recent financial crisis. We find that equity markets are interdependent, both in terms of volatility and illiquidity. Most markets show an increase in volatility and illiquidity spillover effects during the crisis. Furthermore, we find volatility and illiquidity transmission are highly relevant. Illiquidity is a more important channel than volatility in propagating the shocks in equity markets. Our results show an overall crucial role for illiquidity in US markets in influencing other equity markets' illiquidity and volatility. These findings are of importance for policy makers as well as institutional and private investors.

With Monte Carlo experiments on models in widespread use we examine the performance of indirect inference (II) tests of DSGE models in small samples. We compare these tests with ones based on direct inference (using the Likelihood Ratio, LR). We find that both these tests have power so that a substantially false model will tend to be rejected by both; but that the power of the II test is substantially greater, both because the LR is applied after re-estimation of the model error processes and because the II test uses the false model's own restricted distribution for the auxiliary model's coefficients. This greater power allows users to focus this test more narrowly on features of interest, trading off power against tractability.

Empirical market microstructure literature widely employs the non-linear and non-Gaussian multiplicative error class of models (MEMs) in modelling the dynamics of trading duration and financial marks. It routinely maintains the weak exogeneity of duration vis-a-vis marks in estimations. However, microstructure theory states that trade duration, volume and transaction prices are simultaneously determined. We propose Lagrange-multiplier (LM) tests for weak exogeneity for the MEMs. Our LM tests are extensions of the weak exogeneity tests applicable to VAR or VECM models with Gaussian distributions. Empirical assessments show that (i) weak exogeneity is widely rejected by the data in the MEMs and (ii) the failure of weak exogeneity seriously biases parameter estimates. We hope our tests will be of interest in future empirical applications.

Engle and Russell (1998) introduce the autoregressive conditional duration (ACD) model to model the dynamics of financial duration. It is recognized that the ACD model can be specified in ARMA form. We show that as long as the innovations of the ACD model follows a lognormal distribution, the equivalent ARMA model will be Gaussian distributed. Motivated by this fact, we develop a lognormal autoregressive conditional duration (LNACD) model. The LNACD model permits a humped-shaped hazard function with one free shape parameter, which has a computational advantage compared to the existing ACD specification in the literature. We compare the performance of the LNACD model with alternative specification of ACD model. The empirical results show that the LNACD model is always superior to Exponential and Weibull ACD models and its performance is similar to the Burr and Generalized Gamma ACD models.