Culturally sensitive adaptation of PREPARE with Japanese premarital couples

In this study, we attempted to achieve a culturally sensitive adaptation of the PREmarital Personal And Relationship Evaluation (PREPARE) Inventory with Japanese premarital couples (n = 849). A translation, back-translation, and construct modification process was initiated by a group of U.S. and Japanese researchers to ensure a culturally sensitive and applicable adaptation of PREPARE. Alpha reliability analyses revealed good internal consistency among Japanese PREPARE scales. A Japanese premarital couple typology was developed using cluster analyses. Several unique features of the Japanese premarital types are identified and discussed. Suggestions are provided for continued enhancement in adaptation process of PREPARE with Japanese premarital couples.     

Risk Assessment of the Carbon Nanotube Group

This study assessed the health risks via inhalation and derived the occupational exposure limit (OEL) for the carbon nanotube (CNT) group rather than individual CNT material. We devised two methods: the integration of the intratracheal instillation (IT) data with the inhalation (IH) data, and the “biaxial approach.” A four‐week IH test and IT test were performed in rats exposed to representative materials to obtain the no observed adverse effect level, based on which the OEL was derived. We used the biaxial approach to conduct a relative toxicity assessment of six types of CNTs. An OEL of 0.03 mg/m³ was selected as the criterion for the CNT group. We proposed that the OEL be limited to 15 years. We adopted adaptive management, in which the values are reviewed whenever new data are obtained. The toxicity level was found to be correlated with the Brunauer‐Emmett‐Teller (BET)‐specific surface area (BET‐SSA) of CNT, suggesting the BET‐SSA to have potential for use in toxicity estimation. We used the published exposure data and measurement results of dustiness tests to compute the risk in relation to particle size at the workplace and showed that controlling micron‐sized respirable particles was of utmost importance. Our genotoxicity studies indicated that CNT did not directly interact with genetic materials. They supported the concept that, even if CNT is genotoxic, it is secondary genotoxicity mediated via a pathway of genotoxic damage resulting from oxidative DNA attack by free radicals generated during CNT‐elicited inflammation. Secondary genotoxicity appears to involve a threshold.     

Asymmetric Multivariate Stochastic Volatility

This paper proposes and analyses two types of asymmetric multivariate stochastic volatility (SV) models, namely, (i) the SV with leverage (SV-L) model, which is based on the negative correlation between the innovations in the returns and volatility, and (ii) the SV with leverage and size effect (SV-LSE) model, which is based on the signs and magnitude of the returns. The paper derives the state space form for the logarithm of the squared returns, which follow the multivariate SV-L model, and develops estimation methods for the multivariate SV-L and SV-LSE models based on the Monte Carlo likelihood (MCL) approach. The empirical results show that the multivariate SV-LSE model fits the bivariate and trivariate returns of the S&P 500, the Nikkei 225, and the Hang Seng indexes with respect to AIC and BIC more accurately than does the multivariate SV-L model. Moreover, the empirical results suggest that the univariate models should be rejected in favor of their bivariate and trivariate counterparts.     

The impact of jumps and leverage in forecasting covolatility

ABSTRACT

The paper investigates the impact of jumps in forecasting covolatility, accommodating leverage effects. We modify the preaveraged truncated covariance estimator of Koike (2016 Koike, Y. (2016). Estimation of integrated covariances in the simultaneous presence of non-synchronicity, microstructure noise and jumps. Econometric Theory 32:533–611.[Crossref], [Web of Science ®] , [Google Scholar]) such that the estimated matrix is positive definite. Using this approach, we can disentangle the estimates of the integrated covolatility matrix and jump variations from the quadratic covariation matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that the cojumps of two assets have a significant impact on future covolatility, but the impact is negligible for forecasting weekly and monthly horizons.     

Asymmetric Multivariate Stochastic Volatility

This paper proposes and analyses two types of asymmetric multivariate stochastic volatility (SV) models, namely, (i) the SV with leverage (SV-L) model, which is based on the negative correlation between the innovations in the returns and volatility, and (ii) the SV with leverage and size effect (SV-LSE) model, which is based on the signs and magnitude of the returns. The paper derives the state space form for the logarithm of the squared returns, which follow the multivariate SV-L model, and develops estimation methods for the multivariate SV-L and SV-LSE models based on the Monte Carlo likelihood (MCL) approach. The empirical results show that the multivariate SV-LSE model fits the bivariate and trivariate returns of the S&P 500, the Nikkei 225, and the Hang Seng indexes with respect to AIC and BIC more accurately than does the multivariate SV-L model. Moreover, the empirical results suggest that the univariate models should be rejected in favor of their bivariate and trivariate counterparts.     

Covariate selection for estimating the causal effect of control plans by using causal diagrams

Summary. Consider a case where cause–effect relationships between variables can be described by a causal path diagram and the corresponding linear structural equation model. The paper proposes a graphical selection criterion for covariates to estimate the causal effect of a control plan. For designing the control plan, it is essential to determine both covariates that are used for control and covariates that are used for identification. The selection of covariates used for control is only constrained by the requirement that the covariates be non-descendants of a treatment variable. However, the selection of covariates used for identification is dependent on the selection of covariates used for control and is not unique. In the paper, the difference between covariates that are used for identification is evaluated on the basis of the asymptotic variance of the estimated causal effect of an effective control plan. Furthermore, the results can be also described in terms of a graph structure.     

Optimizing a control plan using a structural equation model with an application to statistical process analysis

In the case where non-experimental data are available from an industrial process and a directed graph for how various factors affect a response variable is known based on a substantive understanding of the process, we consider a problem in which a control plan involving multiple treatment variables is conducted in order to bring a response variable close to a target value with variation reduction. Using statistical causal analysis with linear (recursive and non-recursive) structural equation models, we configure an optimal control plan involving multiple treatment variables through causal parameters. Based on the formulation, we clarify the causal mechanism for how the variance of a response variable changes when the control plan is conducted. The results enable us to evaluate the effect of a control plan on the variance of a response variable from non-experimental data and provide a new application of linear structural equation models to engineering science.     

Multivariate Stochastic Volatility: A Review

The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also reviewed.     

Reply

ABSTRACT

In the class of stochastic volatility (SV) models, leverage effects are typically specified through the direct correlation between the innovations in both returns and volatility, resulting in the dynamic leverage (DL) model. Recently, two asymmetric SV models based on threshold effects have been proposed in the literature. As such models consider only the sign of the previous return and neglect its magnitude, this paper proposes a dynamic asymmetric leverage (DAL) model that accommodates the direct correlation as well as the sign and magnitude of the threshold effects. A special case of the DAL model with zero direct correlation between the innovations is the asymmetric leverage (AL) model. The dynamic asymmetric leverage models are estimated by the Monte Carlo likelihood (MCL) method. Monte Carlo experiments are presented to examine the finite sample properties of the estimator. For a sample size of T = 2000 with 500 replications, the sample means, standard deviations, and root mean squared errors of the MCL estimators indicate only a small finite sample bias. The empirical estimates for S&;P 500 and TOPIX financial returns, and USD/AUD and YEN/USD exchange rates, indicate that the DAL class, including the DL and AL models, is generally superior to threshold SV models with respect to AIC and BIC, with AL typically providing the best fit to the data.