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.
Economic research has focused intensely in recent years on the role played by financial markets for real economic activity. Based on ideas tracing back at least to Schumpeter (1912), and inspired by the early contributions of Goldsmith (1969), Gurley and Shaw (1955), and McKinnon (1973), the work of King and Levine (1993 a,b), Demirguc-Kunt and Maksimovic (1998), Levine and Zervos (1998), Rajan and Zingales (1998), Levine, Loayza and Beck (2000), among others, has provided robust empirical evidence that broader, deeper financial markets are strongly associated, causally, with better prospects for future economic growth.
Having established this basic finding, the research effort is now focused on the analysis of the mechanisms through which finance affects real economic activity. What are the specific characteristics of financial markets that seem to affect firms and industries in non-financial sectors of production? For example, does it matter whether banks are privately or government owned (La Porta, Lopez-de-Silanes and Shleifer, 2001), or whether there is higher or lower protection for financial contracts (Levine, 1999), or whether banks are in a more or less competitive environment (Jayaratne and Strahan, 1996, Cetorelli and Gambera, 2001)? And, what specific characteristics of firms and industries are especially affected by finance so that it eventually translates into higher economic activity?
In recent years, some studies have highlighted the cost and convenience benefits of using retail electronic payments and, in particular, card payment instruments. However, cash and other paper-based payment instruments are still being largely used by consumers in most developed countries. Card issuers have incurred substantial costs to launch incentive programs to stimulate payments with debit and credit cards, presumably assuming that these rewards would significantly increase the use of these cards based on standard comparisons.
However, card issuers are facing a great uncertainty on how to allocate the resources to make the incentive programs as effective as desired. On a microeconomic basis, little is known on how to encourage consumers to increase the use of debit and credit cards. Thus, understanding how rewards programs affect consumers' preferences for payment instruments has become a key strategic question in the financial industry. This limited knowledge is, at least partially, due to the lack of comprehensive microeconomic data on consumers' preferences towards payment instruments and on the related role of incentive related mechanisms.
