Trading mechanisms for bottleneck permits with multiple purchase opportunities

This paper extends the theory of tradable bottleneck permits system to cases with multiple period markets and designs its implementation mechanism. The multiple period markets can achieve more efficient resource allocation than a single period market when users’ valuations of tradable permits change over time. To implement the multiple period trading markets, we propose an evolutionary mechanism that combines a dynamic auction with a capacity control rule that adjusts a number of permits issued for each market. Then, we prove that the proposed mechanism has the following desirable properties: (i) the dynamic auction is strategy-proof within each period and guarantees that the market choice of each user is optimal under a perfect information assumption of users and (ii) the mechanism maximizes the social surplus in a finite number of iterations. Finally, we show that the proposed mechanism may work well even for an incomplete information case.     

Spatial efficiency in the bulk freight market

In the short run, there can be substantial differences in spot freight earnings between geographical regions of the global freight market for bulk carriers. Such differences can be consistent with an efficient market if they are temporary and if they cannot be exploited financially by pursuing chartering strategies that are based on publicly available information. In this paper, we apply a simple optimal switching model to evaluate whether such chartering strategies exist. We model the spot freight rate differential between the Atlantic and Pacific basins as a mean-reverting Ornstein–Uhlenbeck process and the entry–exit decision using the discount factor approach, which results in optimal trigger values for the entry/exit from each basin. Our empirical results suggest that the market is spatially efficient during normal freight market conditions when there is a surplus of vessels. The tight market conditions during the 2003–2008 freight market boom caused a persistent upward bias in Atlantic freight rates, but also here we find little added value from pursuing an active switching strategy.     

Estimation of Dynamic VaR in Chinese Stock Markets Based on Time Scale and Extreme Value Theory

The accuracy and time scale invariance of value-at-risk (VaR) measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory (EVT) is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution (GPD), and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE (Hill) at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.     

Estimating the effects of entry regulation in the Istanbul taxicab market

The economic literature shows that entry regulation in taxicab markets brings about a dramatic increase in medallion prices or license values of taxicabs. However, there is no study estimating what the effect of regulation is exactly on real medallion prices. We develop a model to estimate the effect of entry restrictions in the Istanbul taxicab market over real medallion prices and inflation. Our findings contribute that entry regulation in taxicab markets increases medallion prices. Moreover, we find that entry regulation in Istanbul pressures inflation rates as well.