Knowledge that Transforms

This paper discusses a new approach to controlling for the environment when estimating efficiency. In response to the literature on the international comparison of bank efficiency, we draw the attention to a local dimension of comparison. By introducing geographical weights and estimating local frontiers for each US savings bank in the 2001–09 period, we find that the bank technical performance is higher for most banks in comparison to a fixed-effects approach. This result highlights the importance of taking into account the local environment and constraints while analyzing banks’ performance, so as not to consider the factors that are exogenous to these institutions as inefficiencies. Further analysis could improve the weighs calculation by employing other measures of interconnectedness besides geographical distance.

In credit card markets banks provide both payment and credit services. Two regulations were recently enacted in the Turkish credit card market: one on payment services in 2005 and the other on credit services in 2006. By employing the well-known Panzar and Rosse, 1982, Panzar and Rosse, 1987 method and a unique quarterly data set for 21 Turkish banks between 2002 and 2008, we investigate the extent of banks’ market power in the Turkish credit card market before and after the regulations. Unlike most of the existing literature, which considers competition and regulation for either credit or payment services and ignores the externalities between them, we consider the entire market by taking both services into account. Fixed effects estimations reveal that banks enjoyed collusive oligopoly power before the regulations. Although the first regulation did not have much impact, the second led to rises in both banks’ total revenues and competition in the entire market.

This study models and forecasts the evolution of intraday implied volatility on an underlying EUR–USD exchange rate for a number of maturities. To our knowledge we are the first to employ high frequency data in this context. This allows the construction of forecasting models that can attempt to exploit intraday seasonalities such as overnight effects. Results show that implied volatility is predictable at shorter horizons, within a given day and across the term structure. Moreover, at the conventional daily frequency, intraday seasonality effects can be used to augment the forecasting power of models. The type of inefficiency revealed suggests potentially profitable trading models.

In Italy, as in many other European countries, listed firms will normally go dark through controlling owner-initiated tender offers. We find that institutional investors play a central role in the bid process and can protect minority shareholders from being frozen out in the bid. Specifically, tender offers are less likely to succeed when a firm has institutional investors in its ownership structure. When public-to-private offers are accepted, bid premiums are significantly greater if a financial institution (particularly when it is foreign, independent or activist) has a stake in the firm. We explore the effect of a number of hitherto unexplored factors on the takeover premium and find that shareholder agreements facilitate public-to-private acquisitions. Other factors, such as a threat to merge the target if the bid fails, or external validation of the offer price, have no impact on either the likelihood of delisting or the premium paid by the bidder.

This paper proposes a new approach to estimate the idiosyncratic volatility premium. In contrast to the popular two-pass regression method, this approach relies on a novel GMM-type estimation procedure that uses only a single cross-section of return observations to obtain consistent estimates. Also, it enables a comparison of idiosyncratic volatility premia estimated using stock returns with different holding periods. The approach is empirically illustrated by applying it to daily, weekly, monthly, quarterly, and annual US stock return data over the course of 2000–2011. The results suggest that the idiosyncratic volatility premium tends to be positive on daily return data, but negative on monthly, quarterly, and annual data. They also indicate the presence of a January effect.

We measure the time-varying degree of world stock market integration of five developed countries (Germany, France, UK, US, and Japan) over the period 1970:1–2011:10. Time-varying financial market integration of each country is measured through the conditional variances of the country-specific and common international risk premiums in equity excess returns. The country-specific and common risk premiums and their conditional variances are estimated from a latent factor decomposition through the use of state space methods that allow for GARCH errors. Our empirical results suggest that stock market integration has increased over the period 1970:1–2011:10 in all countries but Japan. And while there is a structural increase in stock market integration in four out of five countries, all countries also exhibit several shorter periods of disintegration (reversals), i.e. periods in which country-specific shocks play a more dominant role. Hence, stock market integration is measured as a dynamic process that is fluctuating in the short run while gradually increasing in the long run.

We find that the aggregate asset allocation decisions of US mutual fund investors depend on economic conditions. Both anticipated economic downturns and periods of turmoil lead investors to direct flow away from risky equity funds and towards lower-risk money market funds. These patterns are markedly stronger for investors in low cost and low turnover funds relative to investors in high cost and high turnover funds, consistent with sophisticated investors being more sensitive to changing conditions. Benchmarked against a buy-and-hold strategy, these asset allocation strategies reduce risk without degrading the risk-return trade-off. Our evidence suggests that individual investors, often dismissed as noise traders, collectively react to economic signals in a sensible manner when determining asset allocations.

This paper introduces a regime-switching combination approach to predict excess stock returns. The approach explicitly incorporates model uncertainty, regime uncertainty, and parameter uncertainty. The empirical findings reveal that the regime-switching combination forecasts of excess returns deliver consistent out-of-sample forecasting gains relative to the historical average and the Rapach et al. (2010) combination forecasts. The findings also reveal that two regimes are related to the business cycle. Based on the business cycle explanation of regimes, excess returns are found to be more predictable during economic contractions than during expansions. Finally, return forecasts are related to the real economy, thus providing insights on the economic sources of return predictability.

It is common knowledge that the more prices deviate from fundamentals, the more likely it is for prices to reverse. Taking this into account, we propose a simple statistical model to identify speculative bubbles in financial markets. Through the estimates of the time varying parameters, including transition probabilities, we can identify when and how newly born bubbles grow and burst over time. The model can be estimated by recursive computations, which require a huge storage capacity for standard computers. For this reason, we introduce an approximation in the computation, maintaining the recursive nature of our estimation technique. We then apply this model to the stock markets of the United States, Japan, and China, estimate its parameters and the probabilities of a bubble crash, and obtain several interesting results: the time series data of the stock price bubble show an inherently non-stationary development and the probability of a bubble crash indeed increases as the stock price becomes too high or too low.