免疫蚁群算法在常规医疗器械配送路径优化中的应用

将免疫蚁群算法引入到求解常规医疗器械物流配送路径优化问题中,结合免疫算法和蚁群算法的优点,相对于基本蚁群算法求解常规医疗器械配送路径优化问题,免疫蚁群算法具有更快的收敛速度、更短的搜索时间。通过实验仿真,证明免疫蚁群算法的优化结果优于蚁群算法。     

电子商务环境下物流配送路径优化研究

电子商务环境下的物流配送路径优化是NP—hard问题。考虑电子商务环境下的物流配送特点,提出了基于禁忌搜索的混合遗传算法解决电子商务环境下物流配送路径优化,并在MATLAB7．0平台对算例进行仿真。     

A Note on Minimax Mixture of Distributions Free Procedure for Inventory Model with Variable Lead Time

In recent papers, Moon and Choi (1998) and Hariga and Ben-Daya (1999)considered a continuous review inventory model with a mixture of backordersand lost sales in which the lead time, the order quantity, and the reorder pointare decision variables was studied. Moreover, they also develop a minimaxdistribution free procedure for the problem. While the demands of differentcustomers are not identical in the lead time, then we can't only use a singledistribution (such as Moon & Choi (1998) and Hariga & Ben-Daya (1999))to describe the demand of the lead time. Hence, we correct and extend the modelof Moon and Choi (1998) and Hariga and Ben-Daya (1999) by considering thelead time demand with the mixture of distributions. In addition, we also applythe minimax mixture of distributions free approach to the model by simultaneouslyoptimizing the order quantity, the reorder point, and the lead time to devise a practical procedure which can be used without specific information on demand distribution.     

A two–stage approach to additive time series models

For nonlinear additive time series models, an appealing approach used in the literature to estimate the nonparametric additive components is the projection method. In this paper, it is demonstrated that the projection method might not be efficient in an asymptotic sense. To estimate additive components efficiently, a two–stage approach is proposed together with a local linear fitting and a new bandwidth selector based on the nonparametric version of the Akaike information criterion. It is shown that the two–stage method not only achieves efficiency but also makes bandwidth selection relatively easier. Also, the asymptotic normality of the resulting estimator is established. A small simulation study is carried out to illustrate the proposed methodology and the two–stage approach is applied to a real example from econometrics.     

The RAS Approach in Updating Input–Output Matrices: An Instrumental Variable Interpretation and Analysis of Structural Change

The main purposes of the paper are to reconsider the rationale of the RAS method, and to attempt to improve on its interpretation and usefulness. The substitution and fabrication factors in the RAS method are interpreted as statistical estimates obtained by the method of instrumental variables. This enables the computation of asymptotic standard errors for the factors and the relative precision of the predicted technical coefficients. Furthermore, an adjustment cost minimization model to describe how a sector determines its substitution and fabrication factors is presented. The solutions of the model provide another rationale for the RAS method, and the associated Lagrangian multipliers can be useful for assessing structural constraints and structural change.     

Price discovery in tick time

This paper develops a tick time model for the quote setting dynamics on Nasdaq. The model decomposes quotes into an efficient price, asymmetric information and noise. Both the evolution of the efficient price and the information contents of quotes depend on quote durations. New measures for the contribution to price discovery are defined within this model. When aggregated to fixed calendar time intervals, they relate closely to Hasbrouck [Hasbrouck, Joel, 1995, One security, many markets: determining the contribution to price discovery, Journal of Finance 50, 1175–1199] information shares. Empirical results for 20 Nasdaq stocks indicate that ECNs, in particular Island, contribute most to price discovery for active stocks. For less active stocks, wholesale market makers contribute most.     

Have cryptocurrencies become an inflation hedge after the reopening of the U.S. economy?

In this paper, we study how the comovement between cryptocurrencies and the U.S. inflation expectation rates has changed during the post-reopening of the U.S. economy after the Covid-19 crisis. To do so, we develop a new concept of “exceedance co-kurtosis” which allows us to quantify asymmetry in strong comovement between each cryptocurrency and the inflation expectation rate. The key findings are as follows. First, we show the change in the co-kurtosis asymmetry for major cryptocurrencies: the downside co-kurtosis was higher than the upside co-kurtosis but it decreased after the reopening of the economy. Although the unconditional correlations between cryptocurrencies and the inflation expectation rates remain very low, our results indicate that the major cryptocurrencies become a slightly better inflation hedge after the reopening. Second and more interestingly, the results do not depend on whether a cryptocurrency has a cap on maximum supply or not. Therefore, treating the major cryptocurrencies as digital commodities could be misleading from the viewpoint of portfolio optimization.     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.     

Portfolio optimization with a defaultable security

In this paper we derive a closed-form solution for a representative investor who optimally allocates her wealth among the following securities: a credit-risky asset, a default-free bank account, and a stock. Although the inclusion of a credit-related financial product in the portfolio selection is more realistic, no closed-form solutions to date are given in the literature when a recovery value is considered in the event of a default. While most authors have assumed some recovery scheme in their initial model set up, they do not address the portfolio problem with a recovery when a default actually occurs. Given the tractability of the recovery of market value, we solved the optimal portfolio problem for the representative investor whose utility function is a Constant Relative Risk Aversion utility function. We find that the investor will allocate larger fraction of wealth to the defaultable security as long as the default-event risk is priced. These results are very intuitive and reasonable since it indicates that if the default risk premium is not priced properly the investor purchases less defaultable securities.