This paper reports a systematic examination of the determinants of deposit-related retail banking fees using a set of survey data that is unusual for its size, specificity, and sampling properties. The analysis focuses explicitly on six different fees associated with checking accounts and automated teller machine (ATM) usage. A preliminary analysis documents that, on average, multimarket banks charge substantially higher fees than do typically smaller, single-market banks. A more detailed econometric analysis yields results consistent with predictions of recent models. In particular, it finds that the greater the presence of multimarket banks in the local market, the higher are the retail deposit fees of single-market banks (except in highly concentrated markets) and the weaker is the positive relationship between those fees and market concentration.

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.

In this paper we present estimates of Korean bank inefficiency and productivity change for the period 1992–2002 that are derived from the directional technology distance function. Our method controls for loan losses that are an undesirable by-product arising from the production of loans and allows the aggregation of individual bank inefficiency and productivity growth to the industry level. Our findings indicate that technical progress during the period was more than enough to offset efficiency declines so that the banking industry experienced productivity growth.

This study examines whether investment banker reputation impacts the initial period returns of two stage combinations (carve-outs followed by spin-offs). All prior literature regarding investment banker reputation is based on single-stage events (IPOs). An analysis is conducted of all completed two-stage carve-outs/spin-offs from 1981 to 2002. Findings indicate that underwriters for two-stage combinations retain their reputation in contrast with the single-stage findings of Carter et al. [Carter, R.B., Dark, F.H., Piwowar, M.S., 2002. IPOs and underwriter reputation: Redeeming the value of reputation. Working Paper, Iowa State University] that investment bankers redeemed (cashed-in) their reputation. Also, this study finds that the Carter et al. reputation factors dominate the Loughran and Ritter [Loughran, T., Ritter, J.R., 2002. Why don’t issuers get upset about leaving money on the table in IPOs? The Review of Financial Studies 15 (2), 413–443] reputation factors.

We explore the valuation and hedging of discretely observed volatility derivatives using three different models for the price of the underlying asset: Geometric Brownian motion with constant volatility, a local volatility surface, and jump-diffusion. We begin by comparing the effects on valuation of variations in contract design, such as the differences between specifying log returns or actual returns and incorporating caps on the level of realized volatility. We then focus on the difficulties associated with hedging these products. Delta hedging strategies are ineffective for hedging volatility derivatives since they require very frequent rebalancing. Moreover, they provide limited protection in the jump-diffusion context. We study the performance of a hedging strategy for volatility swaps that establishes small, fixed positions in vanilla options at each volatility observation.

This paper contributes to portfolio selection methodology using a Bayesian forecast of the distribution of returns by stochastic approximation. New hierarchical priors on the mean vector and covariance matrix of returns are derived and implemented. Comparison’s between this approach and other Bayesian methods are studied with simulations on 25 years of historical data on global stock indices. It is demonstrated that a fully hierarchical Bayes procedure produces promising results warranting more study. We carried out a numerical optimization procedure to maximize expected utility using the MCMC (Monte Carlo Markov Chain) samples from the posterior predictive distribution. This model resulted in an extra 1.5 percentage points per year in additional portfolio performance (on top of the Hierarchical Bayes model to estimate μ and Σ and use the Markowitz model), which is quite a significant empirical result. This approach applies to a large class of utility functions and models for market returns.

We use an affine asset pricing model to jointly value stocks and bonds. This enables us to derive endogenous correlations and to explain how economic fundamentals influence the correlation between stock and bond returns. The presented model is implemented for G7 post-war economies and its in-sample and out-of-sample performance is assessed by comparing the correlations generated by the model with conventional statistical measures. The affine framework developed in this paper is found to generate stock–bond correlations that are in line with empirically observed figures.

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.

Prior studies, such as Claessens et al.’s [Claessens, S., Djankov, S., Fan, J., Lang, L., 2002. Disentangling the incentive and entrenchment effects of large shareholding. Journal of Finance 57, 2741–2771], suggest that deviation between ultimate control and ownership decreases firm value (due to the entrenchment effects of large shareholding). Using a sample of Canadian firms, we study the relation of ultimate control and ownership with an important dimension of stock liquidity – bid–ask spread. We find that stocks with greater deviations between ultimate control and ownership have a larger information asymmetry component of their bid–ask spread and wider bid–ask spread. Our results are consistent with the notion that the ultimate owners of these stocks may have selfish agendas. To increase the probability of the agendas being implemented, the firms may have poor information disclosure, resulting in poor stock liquidity.