A Stochastic Linear Programming Model for Asset Liability Management: The Case of an Indian Insurance Company

Asset - Liability management is one of the most critical tasks for any financial institution for determining its cushion against the risk and the net returns. The problem of asset liability management for an insurance company requires matching the cash inflows from premium collections and investment income with the cash outflows due to casualty and maturity claims. Thus, what is required is a prudent investment strategy such that the returns earned on the assets match the liability claims at all points of time in future. Conventionally, the asset allocation has been done using the Mean Variance approach due to Markowitz (1952, 1959). While such a strategy ensures that the asset value always match or are greater than the liability for the next year, it does not maximise the net worth of the firm nor does it take care of all the cash inflows and outflows over a long term period. A stochastic linear programming model (on the lines of Pirbhai, 2004) maximises the net worth of the firm and also takes care of the uncertainties. While there are instances of stochastic linear programming being applied for ALM in financial institutions in developed markets, no such practical application has been reported in this area in Indian context as yet. In this paper, we describe the development of a multi stage stochastic linear programming model for insurance companies. The multi-stage stochastic linear programming model was developed on the modelling language AMPL (Fourer, 2002).

Term Structure Estimation in Illiquid Government Bond Markets: An Empirical Analysis for India

With increasing liquidity of the Indian sovereign debt market from 1997, it has become possible to estimate the term structure in India. However, several frictions that cause individual securities to be priced differently from the "average" pricing in the market characterize the market. In such a scenario, traditional estimation procedures like ordinary least squares using various functional forms do not perform well. In this paper, we find that mean absolute deviation is a better estimation procedure in illiquid markets than the ordinary least square. We further find out a novel liquidity weighted objective function for parameter estimation. We model the liquidity function using the exponential and hyperbolic tangent functions and suggest the most robust model for estimating term structures in India.

Term Structure Estimation in Illiquid Government Bond Markets: An Empirical Analysis for India

With increasing liquidity of the Indian sovereign debt market from 1997, it has become possible to estimate the term structure in India. However, several frictions that cause individual securities to be priced differently from the "average" pricing in the market characterize the market. In such a scenario, traditional estimation procedures like ordinary least squares using various functional forms do not perform well. In this paper, we find that mean absolute deviation is a better estimation procedure in illiquid markets than the ordinary least square. We further find out a novel liquidity weighted objective function for parameter estimation. We model the liquidity function using the exponential and hyperbolic tangent functions and suggest the most robust model for estimating term structures in India.

A Stochastic Linear Programming Model for Asset Liability Management: The Case of an Indian Insurance Company

Asset - Liability management is one of the most critical tasks for any financial institution for determining its cushion against the risk and the net returns. The problem of asset liability management for an insurance company requires matching the cash inflows from premium collections and investment income with the cash outflows due to casualty and maturity claims. Thus, what is required is a prudent investment strategy such that the returns earned on the assets match the liability claims at all points of time in future. Conventionally, the asset allocation has been done using the Mean Variance approach due to Markowitz (1952, 1959). While such a strategy ensures that the asset value always match or are greater than the liability for the next year, it does not maximise the net worth of the firm nor does it take care of all the cash inflows and outflows over a long term period. A stochastic linear programming model (on the lines of Pirbhai, 2004) maximises the net worth of the firm and also takes care of the uncertainties. While there are instances of stochastic linear programming being applied for ALM in financial institutions in developed markets, no such practical application has been reported in this area in Indian context as yet. In this paper, we describe the development of a multi stage stochastic linear programming model for insurance companies. The multi-stage stochastic linear programming model was developed on the modelling language AMPL (Fourer, 2002).