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The analysis of recurrent events in the presence of terminal events requires special attention. Several approaches have been suggested for such analyses either using intensity models or marginal models. When analysing treatment effects on recurrent events in controlled trials, special attention should be paid to competing deaths and their impact on interpretation. This paper proposes a method that formulates a marginal model for recurrent events and terminal events simultaneously. Estimation is based on pseudo-observations for both the expected number of events and survival probabilities. Various relevant hypothesis tests in the framework are explored. Theoretical derivations and simulation studies are conducted to investigate the behaviour of the method. The method is applied to two real data examples. The bivariate marginal pseudo-observation model carries the strength of a two-dimensional modelling procedure and performs well in comparison with available models. Finally, an extension to a three-dimensional model, which decomposes the terminal event per death cause, is proposed and exemplified.

Understanding changes in the frequency, severity, and seasonality of daily temperature extremes is important for public policy decisions regarding heat waves and cold snaps. A heat wave is sometimes defined in terms of both the daily minimum and maximum temperature, which necessitates the generation of forecasts of their joint distribution. In this paper, we develop time series models with the aim of providing insight and producing forecasts of the joint distribution that can challenge the accuracy of forecasts based on ensemble predictions from a numerical weather prediction model. We use ensemble model output statistics to recalibrate the raw ensemble predictions for the marginal distributions, with ensemble copula coupling used to capture the dependency between the marginal distributions. In terms of time series modelling, we consider a bivariate VARMA-MGARCH model. We use daily Spanish data recorded over a 65-year period, and find that, for the 5-year out-of-sample period, the recalibrated ensemble predictions outperform the time series models in terms of forecast accuracy.

This paper aims to investigate the COVID-19 pandemic impacts on the interconnectedness between the Chinese stock market and major financial and commodity markets—gold, silver, Bitcoin, WTI, S&P 500, and Euro STOXX 50—and analyze the portfolio design implications. Using daily data from 2018 to 2021, we first apply the wavelet power spectrum (WPS) to visualize volatility shifts. In contrast to previous research, we empirically identify the precise COVID-19 outbreak dates for each market using the Perron (1997) breakpoint test. Finally, we employ the bivariate DCC-GARCH model to analyze the connectedness between markets. The findings reveal that the COVID-19 pandemic caused volatility shifts of different intensities for all of the studied markets. Moreover, each return series exhibits one break date, which is specific to each market and corresponds to a distinct COVID-19-related event. Correlations, hedge ratios, and optimal portfolio weights changed significantly after the COVID-19 outbreak. There is evidence of contagion effects between the Chinese stock market and S&P 500, Euro STOXX 50, gold, and silver. Interestingly, the latter two assets lost their safe haven property with SSE. However, WTI and Bitcoin act as safe havens against SSE risks.

The Copula approach for continuous variables is highly developed, while discrete ones are underdeveloped due to computational difficulties and sometimes algorithm failure to convergent. Therefore, providing an alternative method for discrete variables becomes an essential issue. In this paper, a simple method is proposed to answer the problem by applying the Continuous Extension Technique (CET). This is carried out by adding random independent perturbations in the form of either Uniform distribution U(0,1) or (U(0,1)−1), and the discrete variables are treated as continuous. Subsequently, a Copula model for resulted variables is estimated based on the Copula theory for continuous variables. This method is called a Copula continuous extension technique. Our analytic and simulation approaches show that both random perturbation forms produce the same Kendall’s Tau measure and the selected Copula bivariate model. As illustrations, the proposed method is applied to the seismicity data obtained from the annual frequencies of earthquakes that occurred in the Sumatra megathrust of Indonesia, from January 1971 to December 2018, with magnitudes ( Mw ) of at least 4.6. Based on the selected Copula models, our simulations confirm the evidence of dependence seismic activity in each of the two adjacent large earthquake sources. These results provide new information regarding the seismicity behavior in the Sumatra megathrust.

This paper examines the competitive firm that has to make its production and hedging decisions under correlated price and background risks. The background risk can be either financial or non-financial, which is accommodated by using a bivariate utility function. The separation theorem is shown to hold in that the firm’s optimal output level depends neither on the firm’s bivariate utility function nor on the joint distribution of the price and background risks. We derive necessary and sufficient conditions under which the firm optimally opts for an over-hedge (under-hedge). We further derive necessary and sufficient conditions under which hedging has positive (negative) effect on the firm’s optimal output level. These conditions are shown to be related to the concept of expectation dependence and bivariate preferences that include correlation aversion (correlation loving) and cross-prudence (cross-imprudence).

The literature on financial inclusion and poverty connections has received considerable attention recently. There exist a scarcity of local studies examining the relationship between financial inclusion (FI) and poverty. Precisely, there is a lack of local studies who previously used FinScope data to investigate the mentioned relationship in South Africa. This study is motivated to fill the gap. To achieve the aims, the study will source data from FinScope (a secondary data) for the periods of 2011 and 2016. The Foster-Greer-Thorbecke indices were used to measure the level of poverty, while the lower-bound poverty (LBPL) line was used to differentiate the poor from the non-poor. Principal Component Analysis (PCA) was also applied to derive the financial inclusion index (FII). Probit regressions were run to measure the likelihood of being poor and being financially excluded. Ordinary Least Squares were run to identify the nature of the relationship between the dependent and the independent variables. Lastly, bivariate regression was also run to test the relationship between poverty and financial exclusion.

In this paper, we consider two bivariate risk models, which allow dependencies among the claim frequencies of an insurance portfolio. These models are described by the Poisson bivariate integer-valued moving-average process of order 1 (BINMA(1)) and the Poisson bivariate integer-valued autoregressive process of order 1 (BINAR(1)). For each proposed model, we derive an expression for the joint moment generating function of the bivariate aggregate claim amount. We present the distribution of the bivariate aggregate claim amount when the individual claim sizes are exponentially distributed. We provide numerical examples for computing related quantities such as the dependency measures, stop-loss premium, and tail risk measures.

In this paper, as opposed to the usual assumption of independence, we propose a credibility model in which the (unobservable) risk profiles of the claim frequency and the claim severity are dependent. Given the risk profiles, the (conditional) marginal distributions of frequency and severity are assumed to belong to the exponential family. A bivariate conjugate prior is proposed for the risk profiles, where the dependency is incorporated via a factorization structure of the joint density. The bivariate posterior is derived, and in turn the Bayesian premium for the aggregate claim is given along with some results on the predictive joint and marginal distributions involving the claim number and the aggregate claim in the next period. To demonstrate the generality of our proposed model, we provide four different examples of bivariate conjugate priors in relation to mixed Erlang, gamma mixture, Farlie-Gumbel-Morgenstern (FGM) copula, and bivariate beta, where each choice has different merits. In these examples, more explicit results can be obtained, and in particular the predictive variance and Value-at-Risk (VaR) of the aggregate claim certainly provide more information on the inherent risk than the Bayesian premium which is merely the predictive mean. Finally, numerical examples will be given to illustrate the effect of dependence on the results, including the use of a real data set that further takes observable risk factors into consideration under a regression setting.

In this paper, we examine the results of GDP trend–cycle decompositions from the estimation of bivariate unobserved components models that allow for correlated trend and cycle innovations. Three competing variables are considered in the bivariate setup along with GDP: the unemployment rate, the inflation rate, and gross domestic income. We find that the unemployment rate is the preferred variable to accompany GDP in the bivariate setup to obtain accurate estimates of its trend–cycle correlation coefficient and the cycle. We show that the key feature of the unemployment rate that allows for reliable estimates of the cycle of GDP is that its nonstationary component is small relative to its cyclical component. Using quarterly GDP and unemployment rate data from 1948:Q1 to 2019:Q1, we obtain a trend–cycle decomposition of GDP that resembles the conventional CBO estimates; we find positively correlated trend and cycle components.

Purpose This paper aims to analyze the time-varying connectedness of gold-backed cryptocurrencies and gold. This study determines the volatility spillovers in these two asset classes and the performance of bivariate portfolios based on net pairwise spillovers. Design/methodology/approach This research uses two Islamic and four conventional gold-backed cryptocurrencies and gold as variables. GJR-GARCH method under corrected DCC (cDCC) of Aielli (2013) evaluates the dynamic connectedness. Additionally, the spillovers are created using the dynamic connectedness of Diebold and Yilmaz (2012). A network-based spillover of Diebold and Yılmaz, (2014) is also made. A dynamic optimal weights strategy optimized with DCC-t-Copula determines bivariate portfolios’ performances. In general, there are 21 bivariate portfolios. Findings The outbreak of COVID-19 increases the dynamic connectedness of gold and gold-backed cryptocurrencies, which indicates a contagion effect. The results show that gold is the net volatility receiver during the COVID-19 pandemic. Moreover, a portfolio composed of gold and gold-backed cryptocurrency provides high profitability performance but zero hedge effectiveness under optimal weights strategy. Practical implications According to bivariate portfolios based on net pairwise spillovers, gold-backed cryptocurrencies' investors should not add gold to their portfolio during the pandemic because it is a net receiver of risk from the cryptocurrencies. Originality/value To the best of the author’s knowledge, this is the first paper to create bivariate portfolios composed of gold-backed cryptocurrencies and their underlying asset using DCC-t-Copula.

We study the relationship between weekly and monthly observations of CDS, interest, and exchange rates (USDTRY) during 2005-2020 in Turkey. The findings suggest a positive relationship between the variables. The bivariate Granger Coherence approach indicates that the dynamic causal and reverse causal interactions mainly intensify in the short- and intermediate-term. Using a bootstrap time-varying causality approach with a fixed size of 37 weeks, the casual linkages are strong but not homogenous in both non-crisis and crisis periods. There is also a unidirectional causality running from interest rates to foreign exchange rates during the period of COVID-19, yielding important implications for investors and policymakers., Bu çalışmada 2005 ve 2020 dönemi kapsamında haftalık ve aylık gözlemlerde CDS, faiz oranı ve USDTRY arasındaki ilişki incelenmiştir. Test sonuçlarına göre tüm değişkenler arasında pozitif yönlü bir ilişki bulunmuştur. Granger Coherence test sonucuna göre değişkenler arasında orta ve kısa döneme yoğunlaşan çift taraflı nedensellik sonucuna ulaşılmıştır. 37 pencere uzunluğundaki Boostraplı zamanla değişen nedensellik test bulgularına göre, değişkenler arasında hem kriz hem kriz dışı dönemlerde geçerli, ancak heterojen özellikler gösteren nedensellik bulunmuştur. Ayrıca, COVID-19 periyodunu içeren zaman aralığında, sadece faiz oranı ve döviz kuru arasında geçerli tek yönlü nedensellik bulgusuna rastlanmıştır. Bulgular hem yatırımcılar hem de politika yapıcılar için önemli sonuçlar doğurmaktadır.

Copulas have recently emerged as practical methods for multivari- ate modeling. To our knowledge, only a limited amount of work has been done to apply copula-based modeling in context analysis. In this study, we generalized Clayton copula under the appropriate weighted function. In some examples, bivariate distributions by using the weighted Clayton cop- ula are generalized. Also the properties of generalized Clayton copula are provided. The Clayton copula and weighted Clayton model cannot be used for negative dependence. These have been used to study left tail depen- dence. This property is stronger in weighted Clayton model with respect to ordinary Clayton copula. It will also be shown that the generalized Clayton copula is suitable for the probable modeling of the hydrology data.

Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response The proposed model includes many important models investigated recently as special cases By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step efficient estimation procedure is developed to iteratively estimate bivariate varying-coefficient functions, the vector of index parameters, and the covariance functions of random effects We also establish the asymptotic properties of the estimators including the convergence rate and their asymptotic distributions In addition, we develop a test statistic to check whether the dynamic interaction varies with time/spatial locations, and we prove the asymptotic normality of the test statistic The finite sample performance of our proposed method and of the test statistic are investigated with several simulation studies Our proposed DISeF model is also used to analyze the COVID-19 data and the ADNI data In both applications, hypothesis testing shows that the bivariate varying-coefficient functions significantly vary with the index and the time/spatial locations For instance, we find that the interaction effect of the population ageing and the socio-economic covariates, such as the number of hospital beds, physicians, nurses per 1,000 people and GDP per capita, on the COVID-19 mortality rate varies in different periods of the COVID-19 pandemic The healthcare infrastructure index related to the COVID-19 mortality rate is also obtained for 141 countries estimated based on the proposed DISeF model [ABSTRACT FROM AUTHOR] Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

A seasonal additive nonlinear vector autoregression (SANVAR) model is proposed for multivariate seasonal time series to explore the possible interaction among the various univariate series. Significant lagged variables are selected and additive autoregression functions estimated based on the selected variables using spline smoothing method. Conservative confidence bands are constructed for the additive autoregression function. The model is fitted to two sets of bivariate quarterly unemployment rate data with comparisons made to the linear periodic vector autoregression model. It is found that when the data does not significantly deviate from linearity, the periodic model is preferred. In cases of strong nonlinearity, however, the additive model is more parsimonious and has much higher out-of-sample prediction power. In addition, interactions among various univariate series are automatically detected.

Vine copula, constructed from bivariate copulas, provides great flexibility in modelling complex high-dimensional dependence. When applied to multi-population mortality modelling, vine copula yields significant improvement over traditional multivariate copulas. In this paper, we propose to capture time-varying features in mortality dependence with dynamic regular vine (R-vine) copula which is built from bivariate copulas with time-varying dependence parameters. We develop two dependence dynamics for R-vine copulas and illustrate the selection and estimation of dynamic R-vine copulas using mortality data from eight populations. The estimated R-vine copulas using the proposed dependence dynamics are shown to yield better goodness of fit than both static and regime-switching vine copulas. We further demonstrate the simulation of mortality paths using dynamic R-vine copulas and examine the impact of vine copula choice on the assessed effectiveness of longevity hedge.

Copulas and frailty models have been two major tools for modeling dependence in multivariate failure time distributions. The objective of this paper is to investigate multivariate failure time models that include copula models and frailty models as special cases. To this end, we revisit a broad family of multivariate failure time models proposed by Marshall and Olkin (JASA 83:834–841, 1988). This family accommodates both frailty and copulas, unlike the models that accommodate only one of them. However, their work focused on very specific copulas and is limited to bivariate models. Instead, we focus more on popular members of copulas and some multivariate models. Another novel feature of our paper is to restrict our attention to the shared frailty model, and call our restricted class as the generic name “frailty-copula”. This name yields a taxonomic classification of all the members of distributions. We also consider somewhat complex frailty distributions (two-parameter gamma, lognormal, truncated-normal, and folded-normal), which were not considered in Marshall and Olkin (1988) and other papers of frailty models. To illustrate the usefulness of the proposed model, we briefly discuss maximum likelihood estimation methods with some numerical evaluations.

Using the Trade Policy Uncertainty (TPU) index as a proxy for the level of trade policy uncertainty in the U.S. economy, we study the impact of the level of trade policy uncertainty on the conditional mean of market returns. Additionally, we investigate the predictive power of trade policy uncertainty on future market returns. Our findings show that after accounting for business cycle effects, TPU does not impact contemporaneous market returns. However, TPU is a robust predictor of future market returns in both univariate and bivariate regression tests. Specifically, our findings present unequivocal evidence of a positive relation between TPU and expected market returns.

Diabetes prevalence is on the rise in the United Kingdom, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to London data on diabetes prevalence and mortality. In order to improve the estimation of relative risks, we analyse jointly prevalence and mortality data to ensure borrowing strength over the two outcomes. The available data involve two spatial frameworks, areas (Middle Layer Super Output Areas, MSOAs) and general practices (GPs) recruiting patients from several areas. This raises a spatial misalignment issue that we deal with by employing the multiple membership principle. Specifically, we translate areal spatial effects to explain GP practice prevalence according to proportions of GP populations resident in different areas. A sparse implementation in RStan of both the multivariate conditional autoregressive (MCAR) and generalised MCAR (GMCAR) with multiple membership allows the comparison of these bivariate priors as well as exploring the different implications for the mapping patterns for both outcomes. The necessary causal precedence of diabetes prevalence over mortality allows a specific conditionality assumption in the GMCAR, not always present in the context of disease mapping. Additionally, an area-locality comparison is considered to locate high versus low relative risk clusters.

Flood is becoming the severe hydrologic issue at the Kelantan River basin in Malaysia. The joint distribution analysis amongst multiple interacting flood characteristics, i.e., flood peak discharge flow, volume, and duration series usually provide a comprehensive understanding of the hydrologic risk assessments through visualizing the multivariate exceedance probability or return periods. The traditional copulas-based methodology is frequently employed under parametric settings where parametric family functions are often employed to model univariate marginal distribution before capturing their dependence structure. Actually, no universal rules and literature are imposed to model any flood vectors through any fixed or predefined density function, which would follow the different distribution and needs to model by fitting most parsimonious function. Also, the copula function already relaxes the restriction of selecting marginal distributions from the same distribution families. Therefore, incorporation of non-parametric kernel density estimations or KDE would be much stable and less biased smoothing alternatives than the parametric approach. In this literature, the semi-parametric copula-based methodology is incorporated, where the flood marginals are modelled under the kernel functions and applied as a case study for 50 years annual maximum (AM) flood samples of the Kelantan River basin at the Gulliemard Bridge gauge station in Malaysia. The Archimedean families copulas (i.e., Frank, Gumbel and Clayton) and Elliptical copula (i.e., Gaussian copula) are tested, and thus best-fitted copulas are employed to model the bivariate joint distribution amongst flood characteristics, and which further employed to derive joint and conditional return periods.

Aims: The aim of this study is to examine Economic data using the multivariate GARCH model. Study design: The study used monthly data of Nigerian crude oil prices (dollar Per Barrel) and Consumer price Index Methodology: This work covers time series data on crude oil price and consumer price Index rural obtained from Central bank of Nigeria (CBN) from 2000 to 2019. To achieve the aim of the study, bivariate VECH and BEKK model were applied. Results: The results confirmed that returns on economic data were correlated. Also, diagonal multivariate VECH model confirmed one of the properties that it must be ‘positive semi-definite’ and the BEKK also confirmed the volatility spillover effects among the economic data. Conclusion: From the results obtained, it was confirmed that conditional variances depends only on own lags and own lagged square returns and conditional covariances depends only on own lags and own lagged cross products of returns. As for cross-volatility effects, past innovations in crude oil price have greatest influence on future volatility of returns on economic data. It was also confirmed that time varying covariance displays among these economic data and lower degree of persistence and based on Model selection criteria using the Akaike information criteria (AIC) diagonal VECH model is better fitted than the BEKK model.

As individuals age, death is a competing risk for Alzheimer's disease (AD) but the reverse is not the case. As such, studies of AD can be placed within the semi-competing risks framework. Central to semi-competing risks, and in contrast to standard competing risks , is that one can learn about the dependence structure between the two events. To-date, however, most methods for semi-competing risks treat dependence as a nuisance and not a potential source of new clinical knowledge. We propose a novel regression-based framework that views the two time-to-event outcomes through the lens of a longitudinal bivariate process on a partition of the time scales of the two events. A key innovation of the framework is that dependence is represented in two distinct forms, local and global dependence, both of which have intuitive clinical interpretations. Estimation and inference are performed via penalized maximum likelihood, and can accommodate right censoring, left truncation, and time-varying covariates. An important consequence of the partitioning of the time scale is that an ambiguity regarding the specific form of the likelihood contribution may arise; a strategy for sensitivity analyses regarding this issue is described. The framework is then used to investigate the role of gender and having ≥1 apolipoprotein E (APOE) ε4 allele on the joint risk of AD and death using data from the Adult Changes in Thought study.

Specific bivariate classes of distributions with given marginals can be used for contribution of the linking distribution between conditional and unconditional effectiveness using copulas. In this paper, a Bayesian model is proposed for meta-analysis of treatment effectiveness data which are generally discrete Binomial and sparse. A bivariate class of priors is imposed to accommodate a wide range of heterogeneity between the multicenter clinical trials involved in the study. Applications to real data are provided.

In this paper, we propose a stochastic order for bivariate copulas based on their hazard rates. We study its properties and the relationships it has with some stochastic orders and dependence notions. The utility of the new order in verifying concepts and orders involving dependence notions is also discussed.

We develop a closed-form bivariate expansion of the shape characteristics of the implied volatility surface generated by a stochastic volatility model with jumps in returns. We use the expansion to analyse the impact on the shape of the implied volatility surface of the various features of the stochastic volatility model and to determine which stochastic volatility models are capable of reproducing the observed characteristics of the implied volatility market data.

The purpose of this paper is to investigate the information transfer in the relation between stock prices and trading volume. While several theoretical models establish this relation, determining its direction remains an empirical question. Conventional linear approaches, such as Granger causality, provide only limited insights. Importantly, they do not take into account the nonlinear nature of this relation which is advocated by theoretical models of noninformational trading. Moreover, they cannot deduce the dominant direction of the information transfer. Both shortcomings can be addressed by relying upon the concept of Shannon transfer entropy. In an empirical application to a large sample of stocks, we employ this model-free measure and find: (i) A substantial amount of nonlinear information transfer across stocks, and (ii) this information predominantly flows from returns to trading volume growth. Thus, we present empirical evidence that the relation between these financial variables is in fact likely to be nonlinear.

Funding Information: This work was supported by the Estonian Research Council grant PSG 142. The publication costs of this article were covered by the Estonian Academy of Sciences. Publisher Copyright: © 2021 Authors. This paper presents a bottom-up bivariate analysis approach to estimate current harmonics by taking account of network and load variations. The current harmonics assessment in the presence of existing and future nonlinear loads is vital to study their impact on the distribution grid. The traditional harmonic analysis models consider only stable loads while neglecting the harmonic interaction among the devices. Modern nonlinear loads operate under different working modes and configurations. Thermal stability, harmonic cancellation, and dynamic network parameters influence the current harmonic estimations. In this paper, a probabilistic approach is presented to model harmonic emission in the low voltage distribution grid under network and load uncertainties. A case study is used to demonstrate effectiveness of the proposed model.

Relatively little is known about the extent to which culture moderates findings in applied psychology research. To address this gap, we leverage the metaBUS database of over 1,000,000 published findings to examine the extent to which six popular cross-cultural models explain variance in findings across 136 bivariate relationships and 56 individual cultural dimensions. We compare moderating effects attributable to Hofstede’s dimensions, GLOBE’s practices, GLOBE’s values, Schwartz’s Value Survey, Ronen and Shenkar’s cultural clusters, and the United Nations’ M49 standard. Results from 25,296 multilevel meta-analyses indicate that, after accounting for statistical artifacts, cross-cultural models explain approximately 5–7% of the variance in findings. The variance explained did not vary substantially across models. A similar set of analyses on observed effect sizes reveal differences of |r| = .05–.07 attributable to culture. Variance among the 136 bivariate relationships was explained primarily by sampling error, indicating that cross-cultural moderation assessments require atypically large sample sizes. Our results provide important information for understanding the overall level of explanatory power attributable to cross-cultural models, their relative performance, and their sensitivity to variance in the topic of study. In addition, our findings may be used to inform power analyses for future research. We discuss implications for research and practice.

Geyser eruption is one of the most popular signature attractions at the Yellowstone National Park. The interdependence of geyser eruptions and impacts of covariates are of interest to researchers in geyser studies. In this paper, we propose a parametric covariate-adjusted recurrent event model for estimating the eruption gap time. We describe a general bivariate recurrent event process, where a bivariate lognormal distribution and a Gumbel copula with different marginal distributions are used to model an interdependent dual-type event system. The maximum likelihood approach is used to estimate model parameters. The proposed method is applied to analyzing the Yellowstone geyser eruption data for a bivariate geyser system and offers a deeper understanding of the event occurrence mechanism of individual events as well as the system as a whole. A comprehensive simulation study is conducted to evaluate the performance of the proposed method., Comment: 29 pages

The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.

We address the issue of semiparametric efficiency in the bivariate regression problem with a highly persistent predictor, where the joint distribution of the innovations is regarded an infinite-dimensional nuisance parameter. Using a structural representation of the limit experiment and exploiting invariance relationships therein, we construct invariant point-optimal tests for the regression coefficient of interest. This approach naturally leads to a family of feasible tests based on the component-wise ranks of the innovations that can gain considerable power relative to existing tests under non-Gaussian innovation distributions, while behaving equivalently under Gaussianity. When an i.i.d. assumption on the innovations is appropriate for the data at hand, our tests exploit the efficiency gains possible. Moreover, we show by simulation that our test remains well behaved under some forms of conditional heteroskedasticity.

In this paper we propose a new bivariate long-term distribu- tion based on the Farlie-Gumbel-Morgenstern copula model. The proposed model allows for the presence of censored data and covariates in the cure parameter. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and inuential ob- servations, we develop a Bayesian case deletion inuence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on articial and real HIV data.

A new family of copulas generated by a univariate distribution function is introduced, relations between this copula and other well-known ones are discussed. The new copula is applied to model the dependence of two real data sets as illustrations.

Shared frailty models are often used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor (frailty) and baseline hazard function which is common to all individuals. There are certain as- sumptions about the baseline distribution and distribution of frailty. Mostly assumption of gamma distribution is considered for frailty distribution. To compare the results with gamma frailty model, we introduce three shared frailty models with generalized exponential as baseline distribution. The other three shared frailty models are inverse Gaussian shared frailty model, compound Poisson shared frailty model and compound negative binomial shared frailty model. We t these models to a real life bivariate survival data set of McGilchrist and Aisbett (1991) related to kidney infection using Markov Chain Monte Carlo (MCMC) technique. Model comparison is made using Bayesian model selection criteria and a better model is suggested for the data.

The association between bivariate binary responses has been studied using Pearson's correlation coefficient, odds ratio, and tetrachoric correlation coefficient. This paper introduces a copula to model the association. Numerical comparisons between the proposed method and the existing methods are presented. Results show that these methods are comparative. However, the copula method has a clearer interpretation and is easier to extend to bivariate responses with three or more ordinal categories. In addition, a goodness-of-fit test for the selection of a model is performed. Applications of the method on two real data sets are also presented.

This paper extends the analysis of the bivariate Seemingly Unrelated (SUR) Tobit by modeling its nonlinear dependence structure through copula and assuming non-normal marginal error distributions. For model estimation, the use of copula methods enables the use of the (classical) Inference Function for Margins (IFM) method by Joe and Xu (1996), which is more computationally attractive (feasible) than the full maximum likelihood approach. However, our simulation study shows that the IFM method provides a biased estimate of the copula parameter in the presence of censored observations in both margins. In order to obtain an unbiased estimate of the copula association parameter, we propose/develop a modified version of the IFM method, which we refer to as Inference Function for Augmented Margins (IFAM). Since the usual asymptotic approach, that is the computation of the asymptotic covariance matrix of the parameter estimates, is troublesome, we propose the use of resampling procedures (bootstrap methods) to obtain confidence intervals for the copula-based SUR Tobit model parameters. The satisfactory results from the simulation and empirical studies indicate the adequate performance of our proposed model and methods. We illustrate our procedure using bivariate data on consumption of salad dressings and lettuce by U.S. individuals.

In this paper, a new copula model with given unit marginals is proposed, based on Rüschendorf’s Method. A new bivariate copula family is introduced by adding a proper term to independence copula. Thus, we avoid the complexity of the proposed copula model. By choosing a baseline copula from the same marginal, we derive a new copula that can approach from above towards the independence copula. Furthermore, it is established that a bivariate copula constructed by this method allows some flexibility in the dependence measure according to Spearman’s correlation coefficient. Additionally, tail dependence measures are investigated. Illustrative examples are given taking into account the specific choices of a baseline copula.

In this study, we investigate the no-arbitrage valuation of options on two assets whereby one asset is exchanged for another. For incorporating surprising events in the underlying asset price dynamics, we model such stochastic processes by a correlated bivariate jump-diffusion model with capturing both individual jumps and systematic cojumps. Furthermore, we employ the technique of Esscher transform to determine a pricing kernel for option valuation under an incomplete market setting. Finally, the estimated results and numerical examples are given.

In this paper, we consider the optimal strategies in asset allocation, consumption, and life insurance for a household with an exogenous stochastic income under a self-contagious market which is modeled by bivariate self-exciting Hawkes jump processes. By using the Hawkes process, jump intensities of the risky asset depend on the history path of that asset. In addition to the financial risk, the household is also subject to an uncertain lifetime and a fixed retirement date. A lump-sum payment will be paid as a heritage, if the wage earner dies before the retirement date. Under the dynamic programming principle, explicit solutions of the optimal controls are obtained when asset prices follow special jump distributions. For more general cases, we apply the Feynman–Kac formula and develop an iterative numerical scheme to derive the optimal strategies. We also prove the existence and uniqueness of the solution to the fixed point equation and the convergence of an iterative numerical algorithm. Numerical examples are presented to show the effect of jump intensities on the optimal controls.

Hurricanes are destructive forces of nature that have the ability to cause vast devastation both economically and socially. Estimating the potential damage caused by hurricanes aids local, state and federal governments as well as insurance and reinsurance companies to plan for future hurricanes. Direct damages caused by hurricanes are difficult to estimate. There are multiple factors that could contribute to the damages caused by a hurricane. Wind is typically considered the most important factor to account for when estimating potential damage. Aside from the complex physical processes, the difficultly in estimating hurricane damages is further compounded by limited data and a changing climate. Fitting models with limited data presents a series of challenges. These challenges include outliers that could form a large proportion of the data, overfitting, missing data and it becomes difficult to leave out a portion of the data for external validation. This study found a significant positive correlation using the Kendall rank correlation coefficient between hurricane damages, measured by the area of total destruction and the maximum landfalling wind speed (τ = 0.451). A copula-based approach was used to model their dependency. Both bivariate Archimedean and elliptical copulae families were assessed as potential models. A bivariate Frank copula with Weibull marginals was found to be the most appropriate fitting model based on a visual inspection of the contour plots of the fitted copulae. Simulation from the fitted copula was qualitatively similar to observation. This study demonstrated a potential method to overcome the limitation of small data facing models to estimate hurricane damages.

The current research has explored the trade-energy nexus for Ghana using annual data within 1970-2011, using the Autoregressive distributed lag model (ARDL) and the Granger predictive causality test in bivariate modelling. The research findings provide evidence of a cointegration association between trade liberalisation (proxied by trade openness) and energy resources (proxied by aggregate energy use, electricity energy use, and fossil fuel energy use). However, there is an insignificant positive long-run effect of trade on energy use for all the energy resources under investigation. In the short-term, there is a positive significant effect of trade on electricity energy use and fossil fuel energy use, but an insignificant positive effect of trade on aggregate energy use. Concerning aggregate energy use, the research findings show that for aggregate energy use, there is causality from trade to aggregate energy use without feedback. However, in the case of electricity use and fossil fuel energy use, trade predicts energy use with feedback. For policy, energy conservation (fossil fuel energy use, and electricity energy use) may not have a deleterious effect on trade in Ghana. Future research may focus on how structural breaks and panel analysis improves the current study, controlling for the effect of other variables that influence energy use.

This paper examines the recession probability in the Eurozone within the next 12 months at the zero lower bound (ZLB) and explores two new perspectives: a revised measure of the traditional term spread and a modification to detect unstable dynamics driven by animal spirits. We find that the yield curve largely lost its forecasting ability at the ZLB. To remove the downward rigidity of short‐term rates, we suggest a modified version of the term spread which uses a “shadow policy rate,” rather than the 3‐month rate, as the front leg of the spread. We further show that a bivariate specification including both the current state of an indicator as well as its lagged deviation from its trend augments the predictive capability for most indicators significantly.

Background This paper concerns the spatial determinants of the first two waves of COVID-19 at the neighbourhood level. Methods Using data for the first and second waves of COVID-19 at the neighbourhood level in Barcelona, we analyse whether local characteristics acted in the same way during the two waves and identify typologies of areas depending on such determinants. Univariate and bivariate local Moran’s I and count data models are used. Results Some structural effects at the neighbourhood level consistently either boost (e.g. population density) or reduce (e.g. income) COVID-19 cases. Other effects differ between the two waves (i.e. age composition, schools and transport infrastructures). Conclusions Since certain characteristics influenced the virus diffusion in opposite ways between the two pandemic waves, territorial heterogeneity alone is insufficient to explain COVID-19 outbreaks—individual behaviour also needs to be factored in. Consequently, both econometric and spatial analysis techniques are recommended for tracking the spatiotemporal spread of this disease and for monitoring the effectiveness of policy measures across heterogeneous neighbourhoods.

The current study develops a methodology to identify spatially relevant buffer sizes for micro-place evaluation research. It applies this methodology in an examination of the causal impact of demolitions on crime in Detroit, Michigan. We utilize Ripley’s bivariate K-function to guide our choice of buffer size. We select a buffer size as the distance at which the examined spatial features exhibit significant sustained attraction prior to the introduction of the intervention. We argue that buffers that are identified in this way capture the spatial relationship between environmental features and are therefore better-suited to capture the actual impact of the treatment on crime. We apply this knowledge in a synthetic control design that estimates the citywide effect of demolitions on disaggregated crime outcomes. With the exception of burglaries, we find fairly limited evidence of a strong, consistent effect of demolitions on crime. The largest negative effects were observed in the immediate months following demolition. Overall, the considerable uncertainty of our estimates suggests that the effect of demolitions may not be consistent across all neighborhoods. At the very least, demolition programs may help temporarily reduce burglaries in areas immediately around demolition sites. However, additional crime reductions gains may be possible if demolition efforts are coupled with complementary crime prevention approaches that focus on the restoration of vacant land. We hope future micro-place evaluation research will use and expand upon our buffer size selection protocol to help improve how places are understood and captured.

Exchanges of practical or financial help between people living in different households are a major component of intergenerational exchanges within families and an increasingly important source of support for individuals in need. Using longitudinal data, bivariate dynamic panel models can be applied to study the effects of changes in individual circumstances on help given to and received from non-coresident parents and the reciprocity of exchanges. However, the use of a rotating module for collection of data on exchanges leads to data where the response measurements are unequally spaced and taken less frequently than for the time-varying covariates. Existing approaches to this problem focus on fixed effects linear models for univariate continuous responses. We propose a random effects estimator for a family of dynamic panel models that can handle continuous, binary or ordinal multivariate responses. The performance of the estimator is assessed in a simulation study. A bivariate probit dynamic panel model is then applied to estimate the effects of partnership and employment transitions in the previous year and the presence and age of children in the current year on an individual’s propensity to give or receive help. Annual data on respondents’ partnership, employment status and dependent children, and data on exchanges of help collected at 2- and 5-year intervals are used in this study.

This paper analyzes the effects of recently introduced macroprudential policies on the financial stability of the Irish economy. We use an empirical approach that is based on the statistical modeling of bivariate loan-income distributions, and find that for First Time Buyers (FTBs) borrowing limits improve financial stability risks; meaning, excessive lending has been effectively restricted. Nonetheless, preliminary evidence for Second Subsequent Buyers (SSBs) shows that financial stability actually worsens. This is because, for the sample at hand, imposing borrowing limits leads lower-income earners to increase their mortgage-borrowing. This finding challenges the one size fits all approach of Irish policy makers.

Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert information is available in terms of bivariate marginals. Such expert information allows for one to choose from among the many possible parametric families of bivariate copulas. By considering a neighborhood of distance $\rho$ around the bivariate marginals with the Kullback-Leibler divergence measure, we model the trade-off between conservatism in the worst-case risk measure and confidence in the expert information. Our bound is developed when the only information available on the bivariate marginals forms a tree structure in which case it is efficiently computable using convex optimization. For consistent marginals, as $\rho$ approaches $\infty$, the bound reduces to the comonotonic upper bound and as $\rho$ approaches $0$, the bound reduces to the worst-case bound with bivariates known exactly. We also discuss extensions to inconsistent marginals and instances where the expert information which might be captured using other parameters such as correlations., Comment: 23 pages, 6 figures

Some coastal structures must be redesigned in the future due to rising sea levels caused by climate change. The design of structures subjected to the actions of waves requires an accurate estimate of the long return period of such parameters as wave height, wave period, storm surge and more specifically their joint exceedance probabilities. The simplified Defra method that is currently used in particular for European coastal structures makes it possible to directly connect the joint exceedance probabilities to the product of the univariate probabilities by means of a single factor. These schematic correlations do not, however, represent all the complexity of the reality because of the use of this single factor. That may lead to damaging errors in coastal structure design. The aim of this paper is therefore to remedy the lack of robustness of these current approaches. To this end, we use copula theory with a copula function that aggregates joint distribution functions to their univariate margins. We select a bivariate copula that is adapted to our application by the likelihood method. In order to integrate extreme events, we also resort to the notion of tail dependence. The optimal copula parameter is estimated through the analysis of the tail dependence coefficient, the likelihood method and the mean error. The most robust copulas for our practical case with applications in Saint-Malo and Le Havre (in northern France) are the Clayton copula and the survival Gumbel copula. The originality of this paper is the creation of a new and robust trivariate copula with an analysis of the sensitivity to the method of construction and to the choice of the copula. Firstly, we select the best fitting of the bivariate copula with its parameter for the two most correlated univariate margins. Secondly, we build a trivariate function. For this purpose, we aggregate the bivariate function with the remaining univariate margin with its parameter. We show that this trivariate function satisfies the mathematical properties of the copula. We finally represent joint trivariate exceedance probabilities for a return period of 10, 100 and 1000 years. We finally conclude that the choice of the bivariate copula is more important for the accuracy of the trivariate copula than its own construction.

PurposeThe study aims at investigating the impact of real earnings management (REM) on the cross-sectional stock return after considering the moderating role of market effect, size effect, value effect and momentum effect.Design/methodology/approachThe study uses weekly and monthly data of 3,085 Bombay Stock Exchange listed stocks spanning over twenty years, from January 2000 to December 2019. REM is measured through metrics developed by Roychowdhury (2006), namely, abnormal levels of operating cash flows, production costs and discretionary expenditure. The study employs univariate and bivariate portfolio-level analysis.FindingsThe findings deduced from the empirical results demonstrate that investors perceive downward REM as an element of risk; hence, they discount the stock prices at a higher rate. On the contrary, results show that investors positively perceive upward REM; hence, they hold the stocks even at a lower rate of return. This anomaly is found to be robust for all kinds of considered moderations.Practical implicationsThe findings have important managerial implications as investors are found to assign different weights to different forms of REM, depending upon the perception regarding the magnitude of risk involved in different forms. Managers can accommodate this information during their short- and long-term corporate planning.Originality/valueFirst, the study is among the earlier attempts to examine the association between REM and stock returns by considering the moderating role of cross-sectional effects. Second, the study considers the direction and endogenous nature of REM while investigating the issue.