Has market concentration fostered on-time performance? A case study of seventy-two U.S. airports

The study compares a multivariate with a quantile regression model to determine whether utilized airport capacity, departure and airborne delays, departure and arrival demand, and market structure explained variations in on-time gate arrivals and arrival delays. In both models, airport departure delays, arrival and departure demand explained variations in the two response variables in prioritized and non-prioritized metroplexes, holding other variables constant. After 2008, the consolidation of the airline industry through mergers coincided with the implementation of NextGen programs, which may have contributed to improvements in on-time performance, especially at prioritized metroplexes where airspace was redesigned.     

Islamic equity market integration and volatility spillover between emerging and US stock markets

The purpose of this paper is to study the conditional correlations across the US market and a sample of five Islamic emerging markets, namely Turkey, Indonesia, Pakistan, Qatar, and Malaysia. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic equity index since it applies stringent restrictions to include companies. Indeed, two main restrictions must be met: (i) the business activity must be compliant with Shari’ah (i.e., Islamic law) guidelines and (ii) interest-bearing investments and leverage ratios should not exceed upper limits. Three models are used: multivariate GARCH BEKK, CCC, and DCC. The estimation results of the three models show that the US and Islamic emerging equity markets are weakly correlated over time. No sheer evidence supports that the US market spills over into the Islamic emerging equity markets. Besides interpreting the results in terms of weak market integration, the peculiar specificities of the Islamic finance industry and the admittance conditions to the MSCI Islamic equity index contribute to explaining them. Indeed, Islamic finance bans interest-bearing investments and imposes some rules, such as asset-backing, which has sizeable impacts on volatility spillover and shocks transmissions, alongside with the close linkage between real and financial sectors. These findings suggest that investors should take caution when investing in the Islamic emerging equity markets and diversifying their portfolios in order to minimize risk.     

Examining dynamic currency linkages amongst South Asian economies: An empirical study

In this paper, we examine the currency market linkages of South Asian member countries using daily data from 6 January 2004 to 31st March 2016. Time invariant and varying Copula GARCH models show that South Asian countries, except for India and Nepal/Bhutan, have low levels of currency market linkages which can be ascribed to poor levels of intra-regional trade intensity and portfolio flows. We reconfirm the copula results through Diebold and Yilmaz methodology and document that currency market connectedness is very limited in the South Asian region. The trends of the fundamental determinants of currency co-movements for the South Asian member countries were compared with its neighbouring regional economic bloc in Asia which has a much longer history and a wider membership base i.e ASEAN + 6. From a comparative analysis, it was found that South Asia member states have to work on their governance parameters, improve on their trade linkages and trade tariffs and work towards greater degree of capital account convertibility with adequate safeguards to achieve higher levels of currency market linkages.     

Nonlinear causality testing with stepwise multivariate filtering: Evidence from stock and currency markets

We examine the spillovers of the US subprime crisis to Asian and European economies and in particular to what extent currency and stock markets have been affected by the crisis. Linear and nonlinear dependencies are detected after pairwise and system-wise causality analysis. A new stepwise multivariate filtering approach is implemented after controlling for conditional heteroskedasticity in the raw data and in VAR/VECM residuals using multivariate GARCH models. Significant nonlinear causal linkages persisted even after the application of GARCH-BEKK, CCC-GARCH and DCC-GARCH modelling. This indicates that volatility effects might partly induce nonlinear causality. Perhaps new short-term asset-pricing models could be developed to explain this stylized fact. These results might also have important implications for hedging, trading strategies and financial market regulation.     

Insurance ratemaking using a copula-based multivariate Tweedie model

The Tweedie distribution, featured with a mass probability at zero, is a convenient tool for insurance claims modeling and pure premium determination in general insurance. Motivated by the fact that an insurance policy typically provides multiple types of coverage, we propose a copula-based multivariate Tweedie regression for modeling the semi-continuous claims while accommodating the association among different types. The proposed approach also allows for dispersion modeling, resulting in a multivariate version of the double generalized linear model. We demonstrate the application in insurance ratemaking using a portfolio of policyholders of automobile insurance from the state of Massachusetts in the United States.     

基于pair-copula的社保基金投资组合风险测度研究

社保基金是社会保障事业健康发展的基石,风险管理是社保基金保值增值的关键问题之一。提出了pair-copula-GARCH-EVT模型以测度社保基金投资组合风险,与传统的n维copula-GARCH-EVT模型相比,该模型不仅考虑了维数的影响,而且还能灵活地选择copula的类型。实证研究发现,基于pair-copula-GARCH-EVT的模型测度社保基金投资组合风险的准确性要高于传统的copula-GARCH-EVT模型。     

Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also allow time-varying correlations among factors, which may be exploited in order to improve volatility forecasts. We couple multi-period, out-of-sample forecasting with portfolio analysis using standard and novel benchmark neutral portfolios. Detailed studies of stock index and FX time series include: multi-period, out-of-sample forecasting, statistical model comparisons, and portfolio performance testing using raw returns, risk-adjusted returns and portfolio volatility. We find uniform improvements on all measures relative to standard dynamic factor models. This is due to the parsimony of latent threshold models and their ability to exploit between-factor correlations so as to improve the characterization and prediction of volatility. These advances will be of interest to financial analysts, investors and practitioners, as well as to modeling researchers.     

Litteraturanmeldelse

This article examines the notion of distortion of copulas, a natural extension of distortion within the univariate framework. We study three approaches to this extension: (1) distortion of the margins alone while keeping the original copula structure; (2) distortion of the margins while simultaneously altering the copula structure; and (3) synchronized distortion of the copula and its margins. When applying distortion within the multivariate framework, it is important to preserve the properties of a copula function. For the first two approaches, this is a rather straightforward result; however, for the third approach, the proof has been exquisitely constructed in Morillas (2005). These three approaches unify the different types of multivariate distortion that have scarcely scattered in the literature. Our contribution in this paper is to further consider this unifying framework: we give numerous examples to illustrate and we examine their properties particularly with some aspects of ordering multivariate risks. The extension of multivariate distortion can be practically implemented in risk management where there is a need to perform aggregation and attribution of portfolios of correlated risks. Furthermore, ancillary to the results discussed in this article, we are able to generalize the formula developed by Genest &; Rivest (2001) for computing the distribution of the probability integral transformation of a random vector and extend it to the case within the distortion framework. For purposes of illustration, we applied the distortion concept to value excess of loss reinsurance for an insurance policy where the loss amount could vary by type of loss.