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The improvements of longevity are intensifying the need for capital markets to be used to manage and transfer the risk through longevity-linked securities. Nevertheless, the difference between the reference population of the hedging instrument and the population of members of a pension plan, or the beneficiaries of an annuity portfolio, determines a significant heterogeneity causing the so-called basis risk. In particular, it is shown that if insurers use financial instruments based on national indices to hedge longevity risk, this hedge can become imperfect. For this reason, it is fundamental to arrange a model allowing to quantify the basis risk for minimising it through a correct calibration of the hedging instrument.

The paper provides a framework for measuring the basis risk impact on the. To this aim, we propose a model that measures the population basis risk involved in a longevity hedge, in the functional data model setting. hedging strategies.

The innovative contribution of the paper occurs in two key points: the modelling of mortality and the hedging strategy. Regarding the first point, the paper proposes a functional demographic model framework (FDMF) for capturing the basis risk. The FDMF model generally designed for single population combines functional data analysis, nonparametric smoothing and robust statistics. It allows to capture the variability of the mortality trend, by separating out the effects of several orthogonal components. The novelty is to set the FDMF for modelling the mortality of the two populations, the hedging and the exposed one. Regarding the second point, the basic idea is to calibrate the hedging strategy determining a suitable mixture of q-forwards linked to mortality rates to maximise the degree of longevity risk reduction. This calibration is based on the key q-duration intended as a measure allowing to estimate the price sensitivity of the annuity portfolio to the changes in the underlying mortality curve.

The novelty lies in linking the shift in the mortality curve to the standard deviation of the historical mortality rates of the exposed population. This choice has been determined by the observation that the shock in a mortality rate is age dependent. The main advantage of the presented framework is its strong versatility, being the functional demographic setting a generalisation of the Lee-Carter model commonly used in mortality forecasting, it allows to adapt to different demographic scenarios. In the next developments, we set out to compare other common factor models to assess the most effective longevity hedge. Moreover, the parsimony for considering together two trajectories of the populations under consideration and the convergence of long-term forecast are important aspects of our approach.

Manufacturing sector growth continues to be topical in the growth journey of Indian economy. The purpose of this paper is to present the modelling and analysis of the combined impact of three key driving sub-systems on the Indian manufacturing growth. It has provided relevant insights and recommendations for its sustainable growth.

The impact of three key driving sub-systems: quality of highway-related infrastructure, manufacturing labour productivity and circular material-consumption in the growth of manufacturing has been studied. A System Dynamics (SD) based model to understand long-term implications of the policy variables on manufacturing growth has been developed. Five scenarios have been simulated for analysis.

Seven policy variables have been identified which have a significant impact on Indian manufacturing growth. Some relevant insights from the analysis of SD based system-behaviour have been provided which would facilitate the manufacturing growth.

The paper has addressed the dynamics of only three sub-systems in the study of manufacturing growth. The other sub-systems which also have an impact on the manufacturing growth: Good governance, education quality and technology are recommended to be studied through SD based modelling.

Specific recommendations for accelerating the manufacturing growth have been made in the paper which has strong practical implications for growth of Indian economy.

Manufacturing sector continues to have a significant impact on the prosperity of India. It facilitates in enhancement of employment and the micro-economic health aspects. Therefore, there is a need to understand the dynamics of the key policy variables affecting manufacturing growth which is very relevant for the society at large.

An application of the SD approach to analyse long-term implication of policy variables of three sub-systems that have a significant impact in manufacturing growth and five specific recommendations to the policy makers is the value-add.

Consumption analysis is one of the most refined branches of economic theory. Over a century of scholarly efforts in utility analysis has created an impressive body of logic that incorporates the most advanced tools of mathematics. But despite the elaborate refinements it has also become one of the least operational of economic analyses. It is so highly general that it offers little in the way of testable predictions[l].

This paper aims to forgo the conventional (degree of operating leverage) risk measure by replacing elasticity of operating profits with respect to output with elasticity of free cash flow (FCF) with respect to optimal output and by considering exogenous random demand shocks for the firm’s products as a source of risk.

The elasticity risk measure accounts for corporate taxes and the cost of bankruptcy. The methodology is selecting optimal level of production investment and capital structure to generate efficient frontier of expected FCF and its risk in terms of its elasticity with respect to output.

The risk measure leads to efficient frontier between expected FCF and its idiosyncratic managerial risk. The model also resolves the empirical debate on the tradeoff between operating and financial leverages.

It is the first elasticity risk measure that embodied the impact of future level of capital expenditure, total level of assets and their sensitivity to random shocks in the product market.