Knowledge that Transforms

This study is the first to employ an econometric model to examine the pricing behaviour of British financial institutions with respect to key bank products/services offered to small and medium sized enterprises (SMEs) including current accounts, investment accounts, business loans, and mortgages. A mean group approach is used on a panel of monthly data to gauge individual banks’ reactions to identify factors influencing the setting of deposit and loan rates, and to assess the competitive structure that best describes the UKs SME banking market. Though the results should be interpreted with caution, the empirical evidence is suggestive of a complex oligopoly. Policies directed at improving information and making it easier for small businesses to change banks/accounts would reduce inertia and improve competition among financial institutions.

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.

This paper applies the extreme-value (EV) generalised pareto distribution to the extreme tails of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses tail estimators from these contracts to estimate spectral risk measures, which are coherent risk measures that reflect a user’s risk-aversion function. It compares these to VaR and expected shortfall (ES) risk measures, and compares the precision of their estimators. It also discusses the usefulness of these risk measures in the context of clearinghouses setting initial margin requirements, and compares these to the SPAN measures typically used.

Subsequent to the influential paper of [Chan, K.C., Karolyi, G.A., Longstaff, F.A., Sanders, A.B., 1992. An empirical comparison of alternative models of the short-term interest rate. Journal of Finance 47, 1209–1227], the generalised method of moments (GMM) has been a popular technique for estimation and inference relating to continuous-time models of the short-term interest rate. GMM has been widely employed to estimate model parameters and to assess the goodness-of-fit of competing short-rate specifications. The current paper conducts a series of simulation experiments to document the bias and precision of GMM estimates of short-rate parameters, as well as the size and power of [Hansen, L.P., 1982. Large sample properties of generalised method of moments estimators. Econometrica 50, 1029–1054], J-test of over-identifying restrictions. While the J-test appears to have appropriate size and good power in sample sizes commonly encountered in the short-rate literature, GMM estimates of the speed of mean reversion are shown to be severely biased. Consequently, it is dangerous to draw strong conclusions about the strength of mean reversion using GMM. In contrast, the parameter capturing the levels effect, which is important in differentiating between competing short-rate specifications, is estimated with little bias.

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 paper looks at the performance record of M&As that took place in the European Union financial industry in the period 1998–2002. First, the paper reports evidence on shareholder returns from the merger. Merger announcements implied positive excess returns to the shareholders of the target company around the date of the announcement, with a slight positive excess-return on the 3-months period prior to announcement. Returns to shareholders of the acquiring firms were essentially zero around announcement. One year after the announcement, excess returns were not significantly different from zero for both targets and acquirers. The paper also provides evidence on changes in the operating performance for the subsample of merges involving banks. M&As usually involved targets with lower operating performance than the average in their sector. The transaction resulted in significant improvements in the target banks performance beginning on average 2 years after the transaction was completed. Return on equity of the target companies increased by an average of 7%, and these firms also experience efficiency improvements.

We consider portfolio allocation in which the underlying investment instruments are hedge funds. We consider a family of utility functions involving the probability of outperforming a benchmark and expected regret relative to another benchmark. Non-normal return vectors with prescribed marginal distributions and correlation structure are modeled and simulated using the normal-to-anything method. A Monte Carlo procedure is used to obtain, and establish the quality of, a solution to the associated portfolio optimization model. Computational results are presented on a problem in which we construct a fund of 13 CSFB/Tremont hedge-fund indices.

Many large financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation based methods, but the methods’ properties are not well understood. This paper theoretically and empirically examines the historical simulation method, a variant of historical simulation introduced by Boudoukh et al. [Boudoukh, J., Richardson, M., Whitelaw, R., 1998. The best of both worlds, Risk 11(May) 64–67] (BRW), and the filtered historical simulation method (FHS) of Barone-Adesi et al. [Barone-Adesi, G., Bourgoin F., Giannopoulos, K., 1998. Don’t look back. Risk 11(August) 100–104; Barone-Adesi, G., Giannopoulos K., Vosper L., 1999. VaR without correlations for nonlinear portfolios. Journal of Futures Markets 19(April) 583–602]. The historical simulation and BRW methods are both under-responsive to changes in conditional risk; and respond to changes in risk in an asymmetric fashion: measured risk increases when the portfolio experiences large losses, but not when it earns large gains. The FHS method is promising, but its risk estimates are variable in small samples, and its assumption that correlations are constant is violated in large samples. Additional refinements are needed to account for time-varying correlations; and to choose the appropriate length of the historical sample period.

We conduct the first robust study of the oldest known form of technical analysis, candlestick charting. Candlestick technical analysis is a short-term timing technique that generates signals based on the relationship between open, high, low, and close prices. Using an extension of the bootstrap methodology, which allows for the generation of random open, high, low and close prices, we find that candlestick trading strategies do not have value for Dow Jones Industrial Average (DJIA) stocks. This is further evidence that this market is informationally efficient.