Discussion on the Benefits Distributing Structure in Village Land Deal

According to everyone average welfare value of land resources; this paper constitutes the base line of glebe usufruct deal value by analyzing the factors, which are affecting glebe usufruct deal value. Having adopted share＇s quotient of ownership and contracting power, the paper erects assigning mechanism to the income of glebe deal. Because of the share dynarnicity being adopted which shows the flexible strong points, the shares of ownership and contracting power avoid many unnecessary otiose disputes on income of glebe deal, adding the stability to the glebe usufruct deal. It is beneficial to the freedom flow and validity manufacture factor installment and also to the nonagricultural changing course.     

Relative Guarantees

Many real-world financial contracts have some sort of minimum rate of return guarantee included. One class of these guarantees is so-called relative guarantees, i.e., guarantees where the minimum guaranteed rate of return is given as a function of the stochastic return on a reference portfolio. These guarantees are the topic of this paper. We analyse a wide range of different functional specifications for the minimum guaranteed rate of return, hereunder both so-called maturity and multi-period guarantees. Several closed form solutions are presented.     

竖向地震力的冲量计算法

一般认为地震在地表引起的破坏主要是S波的水平震动,很少考虑地震竖向力的作用。单从考虑地震水平力的作用是不易全面反映震害特征的。提出用冲量法来研究计算竖向地震力,通过对最大竖向地震力分布研究,探讨竖向地震力对建筑物的影响与破坏,为进一步提高工程抗震设计水平提供理论依据。     

Analysis of cointegrated VARMA processes

VARMA (vector autoregressive moving average) processes are proposed for modelling cointegrated variables. For this purpose the echelon form is combined with the error correction form. Procedures for estimating the Kronecker indices which characterize the echelon form and for specifying the cointegration rank are discussed. The asymptotic distribution of the coefficient estimators is given. An example based o n US macroeconomic data illustrates the procedure and demonstrates its feasibility in practice.     

On the individual moving average inequality

Summary A very simple deduction of a recently treated inequality is presented and some variations on this proof, drawing together various properties, are discussed. Closure of moving average processes undermultiplication is also mentioned.     

股票价格均值回归理论研究综述

随机漫步理论认为股票的价格是不能预测的,许多实证检验的结果也支持了这一结论。但是,近年来均值回归理论对随机漫步理论提出了挑战。从长期来看,股票价格呈均值回归是必然的;均值回归具有不对称性;政府行为对股票的均值回归具有一定的影响。     

油气田开发项目平均投资利润率和利税率的确定

行业平均投资利润率和行业平均投资利税率是投资者进行投资机会选择的主要参考依据之一，是投资者对其投资项目收益的最低期望要求。从投资者角度来讲，恰当地确定平均投资利润率和行业平均投资利税率是一个相当重要而又比较棘手的问题。文章主要讨论了如何确定油气田勘探开发项目经济评价中平均投资利润率和行业平均投资利税率的一种简单方法。     

Performance of Moving Average Trading Rules in a Volatile Stock Market: The Russian Evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Russian stock market over the 2003–12 period. It contributes to the existing technical analysis (TA) literature by testing, for the first time, the applicability of ordered weighted moving averages (OWMA) as an alternative calculation basis for determining DMACs. In addition, this article provides the first comprehensive performance comparison of DMAC trading rules in the stock market that is known as one of the most volatile markets in the world. The results show that the best trading strategies of the in-sample period can also outperform their benchmark portfolio during the subsequent out-of-sample period. Moreover, the outperformance of the best DMAC strategies is mostly attributable to their superior performance during bearish periods and, particularly, during stock market crashes.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.