Building Sustainability Resiliency During Post Covid World Focusing ESG And Climate Risk Issue

Purpose of this study: In accordance with the current world economy, building climate sustainability resiliency is very important under the physical risk and transitional risk mitigation. This classification of climate risks could have an enormous positive impact focusing on ESG (Environmental, Social and Corporate Governance) goal achievement during the post-COVID pandemic situation focusing on climate risk issues. The European Green Deal has also increased the EU’s climate ambitions. In addition, global cooperation on sustainable finance has increased and the international context has changed. The financial sector will play a critical role in our transition to sustainability.The strategy of this study aims to support the European Green Deal aims, as well as an inclusive and sustainable recovery from the COVID-19 pandemic consequences. Methodology: In this study, the relative carbon risk and absolute carbon risk is shown based on the dynamic common factor model. The graphical representation of absolute versus relative carbon risk is measured in this time series data based research on the ten years timeline of 2010 to 2019. Main findings: The study shows the graphical figure regardingregion-wise dynamics of the relative and absolute carbon emissions risk in an average by adopting the dynamic common factor model throughout the global level by obtaining the Kalman filtering tool. Research limitations/Implications: Lack of resources of primary data is the main creating hindering effect that is faced in this study. This article portrays the increase in CO2 emissions leading to consequences of climate risk also accelerating these problems within the regions and countries mentioned in this research. Novelty/Originality: Due to the COVID-19 outbreak, the developed nations, as well as emerging economies, are facing vulnerability in the area of financial, governmental, environmental to be sustainably resilient. This is the high time of detecting these problems and taking precautionary measures by the policymakers and government in the economic sector by adopting implementable methodologies. This study may benefit readers by advancing the existing knowledge or creating new knowledge in this subject. The current study reflects the situation of forthcoming researchers who intends to study as well as interested in this particular area.

A DOUBLE COMMON FACTOR MODEL FOR MORTALITY PROJECTION USING BEST-PERFORMANCE MORTALITY RATES AS REFERENCE

We construct a double common factor model for projecting the mortality of a population using as a reference the minimum death rate at each age among a large number of countries. In particular, the female and male minimum death rates, described as best-performance or best-practice rates, are first modelled by a common factor model structure with both common and sex-specific parameters. The differences between the death rates of the population under study and the best-performance rates are then modelled by another common factor model structure. An important result of using our proposed model is that the projected death rates of the population being considered are coherent with the projected best-performance rates in the long term, the latter of which serves as a very useful reference for the projection based on the collective experience of multiple countries. Our out-of-sample analysis shows that the new model has potential to outperform some conventional approaches in mortality projection.

The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk

We investigate the impact of model uncertainty on hedging longevity risk with index-based derivatives and assessing longevity basis risk, which arises from the mismatch between the hedging instruments and the portfolio being hedged. We apply the bivariate Lee–Carter model, the common factor model, and the M7-M5 model, with separate cohort effects between the two populations, and various time series processes and simulation methods, to build index-based longevity hedges and measure the hedge effectiveness. Based on our modeling and simulations on hypothetical scenarios, the estimated levels of hedge effectiveness are around 50% to 80% for a large pension plan, and the model selection, particularly in dealing with the computed time series, plays a very important role in the estimation. We also experiment with a modified bootstrapping approach to incorporate the uncertainty of model selection into the modeling of longevity basis risk. The hedging results under this approach may approximately be seen as a “weighted” average of those calculated from the different model candidates.

From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

Disentangling the source of non-stationarity in a panel of seasonal data

In dealing with a panel of seasonal data with cross-section dependence, this paper establishes a common factor model to investigate whether the seasonal and non-seasonal non-stationarity in a series is pervasive, or specific, or both. Without knowing a priori whether the data are seasonal stationary or not, we propose a procedure for consistently estimating the model; thus, the seasonal non-stationarity of common factors and idiosyncratic errors can be separately detected accordingly. We evaluate the methodology in a series of Monte Carlo simulations and apply it to test for non-stationarity and to disentangle their sources in panels of worldwide real exchange rates and of consumer price indexes for 37 advanced economies.

WorseThanMeasurementError

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model, such that they consist of construct variance plus error. In this paper, we explore what happens when a set of items that are not generated from a common factor construct model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical re-analysis, we show that (1) common factor models tend to produce extremely biased and highly variable structural parameter estimates when the population model is not a common factor model; (2) model fit is a poor indicator of the degree of bias; and (3) composite models are sometimes more reliable than common factor models under alternative measurement structures, though they also lead to unacceptably bad solutions in some cases.

SMOOTHING POISSON COMMON FACTOR MODEL FOR PROJECTING MORTALITY JOINTLY FOR BOTH SEXES

We consider a modification to the Poisson common factor model and utilise a generalised linear model (GLM) framework that incorporates a smoothing process and a set of linear constraints. We extend the standard GLM model structure to adopt Lagrange methods and P-splines such that smoothing and constraints are applied simultaneously as the parameters are estimated. Our results on Australian, Canadian and Norwegian data show that this modification results in an improvement in mortality projection in terms of producing more accurate forecasts in the out-of-sample testing. At the same time, projected male-to-female ratio of death rates at each age converges to a constant and the residuals of the models are sufficiently random, indicating that the use of smoothing does not adversely affect the fit of the model. Further, the irregular patterns in the estimates of the age-specific parameters are moderated as a result of smoothing and this model can be used to produce more regular projected life tables for pricing purposes.