Knowledge that Transforms

This paper assesses the relative technical efficiency of institutions operating in a market that has been significantly affected by environmental and market factors in recent years, the Hong Kong banking system. These environmental factors are specifically incorporated into the efficiency analysis using the innovative slacks-based, second stage Tobit regression approach advocated by Fried et al. [Fried, H.O., Schmidt, S.S., Yaisawarng, S., 1999. Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis 12, 249–267]. A further innovation is that we also employ Tone’s [Tone, K., 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130, 498–509] slacks-based model (SBM) to conduct the data envelopment analysis (DEA), in addition to the more traditional approach attributable to Banker, Charnes and Cooper (BCC) [Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science 30, 1078–1092]. The results indicate: high levels of technical inefficiency for many institutions; considerable variations in efficiency levels and trends across size groups and banking sectors; and also differential impacts of environmental factors on different size groups and financial sectors. Surprisingly, the accession of Hong Kong to the People’s Republic of China, episodes of financial deregulation, and the 1997/1998 South East Asian crisis do not seem to have had a significant independent impact on relative efficiency. However, the results suggest that the impact of the last-mentioned may have come via the adverse developments in the macroeconomy and in the housing market.

Recently, several behavioral finance models based on the overconfidence hypothesis have been proposed to explain anomalous findings, including a short-term continuation (momentum) and a long-term reversal in stock returns. We characterize the overconfidence hypothesis by the following four testable implications: First, if investors are overconfident, they overreact to private information and underreact to public information. Second, market gains make overconfident investors trade more aggressively in subsequent periods. Third, excessive trading of overconfident investors in securities markets contributes to the observed excessive volatility. Fourth, overconfident investors underestimate risk and trade more in riskier securities. To document the presence of overconfidence in financial markets, we empirically evaluate these four hypotheses using aggregate data. Overall, we find empirical evidence in support of the four hypotheses.

Using the stochastic frontier approach to investigate the cost efficiency of commercial banks in Hong Kong, this paper found that the average X-efficiency of Hong Kong banks was about 16–30% of observed total costs. However, X-efficiency was found to decline over time, indicating that Hong Kong banks were operating closer to the cost frontier than before, consistent with technological innovations in the banking industry. Furthermore, the average large bank was found to be less efficient than the average small bank, but the size effect appears to be related to differences in portfolio characteristics among different size banks.

We analysed the distribution of the TARGET cross-border interbank payment flows from both a cross-section and a time-series point of view using average daily data for the period 1999–2002. Our findings were, first, that “location matters” in the sense that bilateral payment flows seem to reflect an organisation of interbank trading between countries in which the size of the banking sector, geographic proximity and cultural similarities play a significant role. This result was also confirmed by a model developed drawing on the gravity models literature. Second, we found that the payment traffic in TARGET is strongly affected by technical market deadlines. In addition, such traffic is positively related mainly to the liquidity conditions and to the turnover of the euro area money market (particularly the unsecured overnight segment). Our model also provides a good explanation of the determinants of the interbank payments settled in the EURO 1 system.

We present a new empirical decomposition of the effects of financial liberalization on economic growth and on the incidence of crises. Our empirical estimates show that the direct effect of financial liberalization on growth by far outweighs the indirect effect via a higher propensity to crisis. We also discuss several models of financial liberalization and growth whose predictions are consistent with our empirical findings.

Pricing of an American option is complicated since at each time we have to determine not only the option value but also whether or not it should be exercised (early exercise constraint). This makes the valuation of an American option a free boundary problem. Typically at each time there is a particular value of the asset, which marks the boundary between two regions: to one side one should hold the option and to other side one should exercise it. Assuming that investors act optimally, the value of an American option cannot fall below the value that would be obtained if it were exercised early. Effectively, this means that the American option early exercise feature transforms the original linear pricing partial differential equation into a nonlinear one. We consider a penalty method approach in which the free and moving boundary is removed by adding a small and continuous penalty term to the Black–Scholes equation; consequently,the problem can be solved on a fixed domain. Analytical solutions of the Black–Scholes model of American option problems are seldom available and hence such derivatives must be priced by stable and efficient numerical techniques. Standard numerical methods involve the need to solve a system of nonlinear equations, evolving from the finite difference discretization of the nonlinear Black–Scholes model, at each time step by a Newton-type iterative procedure. We implement a novel linearly implicit scheme by treating the nonlinear penalty term explicitly, while maintaining superior accuracy and stability properties compared to the well-known θ-methods.

We consider the problem of constructing a portfolio of finitely many assets whose return rates are described by a discrete joint distribution. We propose a new portfolio optimization model involving stochastic dominance constraints on the portfolio return rate. We develop optimality and duality theory for these models. We construct equivalent optimization models with utility functions. Numerical illustration is provided.

This paper investigates the interest rate pass-through in the euro-zone’s retail banking markets by differentiating between expected and unexpected monetary policy impulses. The paper introduces interest futures as measures of expected interest rates into pass-through studies. By allowing various specifications of the pass-through process, including asymmetric adjustment, we find a faster pass-through in loan markets when interest rate changes are correctly anticipated. In contrast, deposit markets are found to be more rigid. Overall, our results suggest that a well-communicated monetary policy is important for a speedier and a more homogenous pass-through but may also be complemented by competition policies.

Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. The concept of scenarios is typically employed for modeling random parameters in a multi-period stochastic programming model, and scenarios are constructed via a tree structure. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [Hibiki, N., 2001b. A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation. In: Takahashi, H. (Ed.), The Japanese Association of Financial Econometrics and Engineering. JAFEE Journal 89–119 (in Japanese); Hibiki, N., 2003. A hybrid simulation/tree stochastic optimization model for dynamic asset allocation. In: Scherer, B. (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, pp. 269–294], and it is called a hybrid model. The advantage of the simulated path structure compared to the tree structure is to give a better accuracy to describe uncertainties of asset returns. In this paper, we compare the two types of multi-period stochastic optimization models, and clarify that the hybrid model can evaluate and control risk better than the scenario tree model using some numerical tests. According to the numerical results, an efficient frontier of the hybrid model with the fixed-proportion strategy dominates that of the scenario tree model when we evaluate them on simulated paths. Moreover, optimal solutions of the hybrid model are more appropriate than those of the scenario tree model.

Criteria VaR (Value-at-Risk) and CVaR (Conditional Value-at-Risk), which are well-known in financial mathematics, are compared. Some connection between them is established. Ways of choice a level of confidence probability for the quantile optimization problem are suggested. The ways are based on some equations of balance between VaR and CVaR. Examples are discussed.