中国通货膨胀与通货膨胀不确定性的非线性关系

本文利用1983年1月~2007年1月CPI月度数据,应用Hamilton提出的能同时发现数据线性和非线性关系的随机场回归模型,对中国通货膨胀与通货膨胀不确定性的非线性关系进行实证研究。实证的结论支持了Friedman-Ball假设和Cukierman-Meltzer假设。前者呈现U型的非线性关系,这意味高的通货膨胀和通货紧缩都将导致高的不确定性,这一发现补充了Friedman-Ball假设;而对于后者,呈现出更为复杂的N型非线性关系。本文的结论对中央银行控制通货膨胀不确定性具有一定的借鉴意义。     

Statistically based quarterly earnings expectation models for nonseasonal firms

We present empirical evidence on the relative predictive power of statistically based quarterly earnings expectation models for firms that are characterized as nonseasonal in nature. We are particularly interested in nonseasonal firms for two reasons. First, it appears that a sizable and growing percentage of firms exhibit quarterly earnings patterns that are clearly nonseasonal in nature. We present new evidence that is consistent with this trend. Specifically, 36% of our sample firms (n = 296) are nonseasonal compared to 12% reported in Lorek and Bathke (J Acc Res 22:369–379, 1984) (n = 29); 17% in Brown and Han (J Acc Res 38:149–164, 2000) (n = 155); and 28.2% in Bathke et al. (J Business Inquiry 5:39–49, 2006) (n = 167). Second, we also find that 43.6% of the nonseasonal firms in our sample have no analyst coverage. Therefore, interest in the predictive ability of statistically based models for such firms is greatly enhanced. Our predictive findings indicate that the random walk model provides significantly more accurate pooled, one-step ahead quarterly earnings predictions across 40 quarters in the 1994–2003 holdout period than the first-order autoregressive model popularized in the literature. We attribute the superior performance of the random walk model to at least three contributing factors: (1) its parsimonious nature; (2) the reduced levels of autocorrelation observed in our quarterly earnings data relative to previous work; and (3) a significantly greater frequency of loss quarters evidenced by nonseasonal versus seasonal firms.

新凯因斯DSGE模型与货币政策法则之汇率动态分析

对于小型开放经济而言,当经济存在价格僵硬的情况下,中央银行在面对不同冲击发生时,各政策法则执行对汇率波动的影响及动态调整过程差异较大。从中国台湾地区的情况为案例来看,在稳定汇率波动方面:当国内技术冲击时,货币法则优于利率法则;当国外通货膨胀时,利率法则优于货币法则;当国外利率冲击时,执行利率法则或货币法则,其结果无显著差异。在汇率动态调整方面:当国外利率调升时,中央银行执行利率法则与货币法则下,汇率的瞬时反应为过度贬值;当国外物价膨胀时,执行利率法则与货币法则下,汇率的瞬时反应表现为立即升值;当国内技术进步冲击时,因为国外冲击对小型开放经济体系影响力道较强,使得国内技术进度对体系的影响相对较小,其中在利率法则下,汇率微幅贬值,而在货币法则下,汇率微幅升值。     

具有索引和地址搜索系统的VHS录像机

本文详细地介绍了一种具有最新的“索引”和“地址”这两种随机存取功能的VHS格式录像机及其整个系统的结构、工作原理和功能。这种录像机具有功能多、使用方便等优点。     

Random walk and breaking trend in financial series: An econometric critique of unit root tests

The present note sheds light on several pitfalls associated with unit root tests that are overlooked by a growing volume of literature in financial economics. Specifically, several studies have confused unit root tests with the Random Walk hypothesis. Unit root tests are not designed for such a task since they aim at investigating whether a time series is difference-stationary or trend-stationary and are not, therefore, predictability tests. Secondly, we emphasize some serious shortcomings associated with the widely used unit root test developed by Zivot and Andrews [Zivot, E. & Andrews, D.W.K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10, 251–270.]. In particular, we stress that results from the Zivot–Andrews test are sensitive to the methods employed to calculate the critical values and to select the maxim lag k. Furthermore, Zivot–Andrews test imposes a one time structural break in a time series; however recent studies showed that not counting for other true structural breaks may bias the results and may cause a spurious rejection of the unit root null hypothesis. Finally, we support our arguments by an empirical example based on the findings of Narayan and Smyth [Narayan, K.P. & Smyth, R. (2004). Is South Korea’s stock market efficient? Applied Economics Letters, 11, 707–710.] with regards to the efficiency of South Korean stock market. We show that contrary to what the authors claim, the KSE (KOSPI) price index is predictable, and hence the South Korean stock market is not informationally efficient.     

A Market Efficiency Comparison of Islamic and Non-Islamic Stock Indices

This article examines the martingale difference hypothesis (MDH) and the random walk hypothesis (RWH) for nine conventional and nine Islamic stock indices: Asia-Pacific, Canadian, Developed Country, Emerging, European, Global, Japanese, UK, and United States. It investigates whether Islamic stock indices are more, less, or as efficient as their conventional counterparts. We test four sub-periods of bullish and bearish stock markets, together with the financial meltdown and its recovery, over the period 1997–2012. We use the Escanciano and Lobato’s (2009) automatic portmanteau test (AQ) and Deo’s (2000) test for the MDH. We also apply the automatic variance ratio test (AVR) developed by Choi (1999) and Kim (2009) for the RWH. Over the period from 1997 to 2012, we find that three conventional indices (Europe, Japan, and UK) are efficient, but that none of the Islamic indices are efficient in these markets. During the recent financial crisis, our results indicate slightly more efficiency for the Islamic indices than their conventional counterparts. Our study finds that overall the conventional indices are more efficient than their Islamic counterparts. Nevertheless, during periods of general downturns the Islamic indices have shown the same level of efficiency as their counterparts. Furthermore, it appears that during the last two sub-periods under study, the Islamic indices have moved toward efficiency, displaying the same level of efficiency as their counterparts.     

手机分期消费贷款信用风险研究——基于RF-Logistic模型的实证分析

识别手机分期消费贷款违约因子是防范手机消费贷款业务信用风险的关键所在。为此,基于融合随机森林（RF）和逻辑回归（Logistics）两阶段模型,通过数据挖掘揭示风险特征重要性含义,并结合经济计量方法诠释异质性客户信用违约的基准逻辑。结果表明：入网时长、终端个数、客户月流量、终端时长是影响手机分期消费贷款客户信用风险的重要性特征变量,且边际影响分别为-0.039%、3.18%、-0.01%、-1.06%,模型泛化能力强,准确率达到74%。所以,要完善手机分期消费贷款信用风险管理应从交叉数据获取、社交网络、兴趣热点和消费习惯等方面着手。     

Bagging集成方法在保险欺诈识别中的应用研究

保险欺诈不仅危及保险公司的正常经营,增加投保人的负担,甚至有可能影响到国家的金融稳定。随着大数据时代的到来,保险反欺诈亟需引入革命性技术。Bagging集成方法以其可调节模型结构、易于部署、参数空间可控、支持并行运算等特点成为保险公司进行保险反欺诈一个好的选择。Bagging方法主要包括Bagging算法、Random Subspace算法、Random Patches算法,它们又能与不同基学习器结合构成新的分支算法及算法特例。本文基于这些算法对保险欺诈问题进行了实证检验,分析了各算法及与基学习器的适用性问题,以及基学习器个数对算法表现的影响。分析发现:针对保险欺诈识别问题,在Bagging、Random Subspace、Random Patches三者之中,Random Patches算法的表现最好,Bagging的运行时间最短;不同算法适用的基学习器不同,但总体来说最适合Bagging集成方法的是决策树;基于决策树的方法都一致选择是否委托律师代理作为最重要的特征;基学习器个数对不同Bagging算法表现的影响并不一致。     

Buy-and-hold mean-variance portfolios with a random exit strategy

We show how buy-and-hold investors can move from horizon uncertainty to profit opportunity. The analysis is conducted under a risk-averse framework rather than the standard Markowitz formulation in the case of i.i.d. asset processes. We make this practical achievement by considering a threshold stopping rule as the strategy to determine when to exit the market. The resulting investment horizon is random and can be correlated with the market. Under this setting, we first provide an analytical approximation to optimal weights, and then identify a class of reference variables associated with the stopping rule that leads to ex-ante improvements in portfolio allocation, vis-a-vis the fixed exit time alternative. The latter conclusion is based on a generalization of the Sharpe ratio, adjusted for horizon uncertainty. The obtained investment suggestion is simple and can be implemented empirically.     

A reappraisal of the Meese–Rogoff puzzle

Several explanations have been put forward for the Meese–Rogoff puzzle that exchange rate models cannot outperform the random walk in out-of-sample forecasting. We suggest that a simple explanation for the puzzle is the use of the root mean square error (RMSE) to measure forecasting accuracy, presenting a rationale as to why it is difficult to beat the random walk in terms of the RMSE. By using exactly the same exchange rates, time periods and estimation methods as those of Meese and Rogoff, we find that their results cannot be overturned even if the models are estimated with time-varying coefficients. However, we also find that the random walk can be outperformed by the same models if forecasting accuracy is measured in terms of the ability to predict direction, in terms of a measure that combines magnitude and direction and in terms of profitability.