An Asset-Liability Management model with a novel strategy for controlling risk of under-funding is presented in this paper. The basic model involves multiperiod decisions (portfolio rebalancing) and deals with the usual uncertainty of investment returns and future liabilities. Therefore it is well-suited to a stochastic programming approach. A stochastic dominance concept is applied to measure (and control) risk of underfunding. A small numerical example is provided to demonstrate advantages of this new model which includes stochastic dominance constraints over the basic model.
Adding stochastic dominance constraints comes with a price. It complicates the structure of the underlying stochastic program. Indeed, new constraints create a link between variables associated with different scenarios of the same time stage. This destroys the usual tree-structure of the constraint matrix in the stochastic program and prevents the application of standard stochastic programming approaches such as (nested) Benders decomposition. A structure exploiting interior point method is applied to this problem. A specialized interior point solver OOPS can deal efficiently with such problems and outperforms the industrial strength commercial solver CPLEX. Computational results on medium scale problems with sizes reaching about one million of variables demonstrate the efficiency of the specialized solution technique. The solution time for these nontrivial asset liability models seems to grow sublinearly with the key parameters of the model such as the number of assets and the number of realizations of the benchmark portfolio, and this makes the method applicable to truly large scale problems.
This paper aims to answer the question why the bulk of institutional cash pools are not invested directly in deposits in the traditional banking system but in deposit alternatives and primarily in the so-called “shadow” banking system. It analyzes the portfolio allocation rationale of institutional cash pools with the aim to better understand the systemic risks inherent in their allocations presently.
Illiquidity plays a major role in corporate bond markets. While some corporate bonds are traded on a daily basis, many other bonds trade less frequently. The corporate bond market is therefore very well suited to study the price effects of liquidity and several studies have recently examined whether illiquidity affects corporate bond prices. Most of these studies regress a panel of credit spreads on liquidity measures, thus using liquidity as a bond characteristic. A few recent articles analyze whether there is a premium associated with exposure to systematic liquidity risk, but do not include liquidity as a characteristic.
