Identifying periods of market inefficiency for return predictability

The article examines the efficiency of 31 stock index series spanning 26 countries across the world, using generalized spectral test (GST) and detects departure from the martingale difference hypothesis (MDH). A moving window of 24 months was used and p-values of GST were estimated. In order to explore whether the departure from market efficiency can be used for generating profitable trades, an exponentially weighted-moving-average-based trading rule was applied and was found that average profits per trade were significantly higher when p-value of the GST was less than 0.1. These observations are in consistent with the adapted market hypothesis.     

Random matrix models for datasets with fixed time horizons

This paper examines the use of random matrix theory as it has been applied to model large financial datasets, especially for the purpose of estimating the bias inherent in Mean-Variance portfolio allocation when a sample covariance matrix is substituted for the true underlying covariance. Such problems were observed and modeled in the seminal work of Laloux et al. [Noise dressing of financial correlation matrices. Phys. Rev. Lett., 1999, 83, 1467] and rigorously proved by Bai et al. [Enhancement of the applicability of Markowitz's portfolio optimization by utilizing random matrix theory. Math. Finance, 2009, 19, 639–667] under minimal assumptions. If the returns on assets to be held in the portfolio are assumed independent and stationary, then these results are universal in that they do not depend on the precise distribution of returns. This universality has been somewhat misrepresented in the literature, however, as asymptotic results require that an arbitrarily long time horizon be available before such predictions necessarily become accurate. In order to reconcile these models with the highly non-Gaussian returns observed in real financial data, a new ensemble of random rectangular matrices is introduced, modeled on the observations of independent Lévy processes over a fixed time horizon.     

基于核心通货膨胀视角的 物价走势判定研究*

核心通货膨胀对于央行准确判定物价走势、更好实现稳定物价目标具有重要意义。本文采用广义动态因子模型与频谱分解技术测度的核心通货膨胀相比于CPI通胀率能更准确反映我国物价变动的周期特征与长期趋势,对于央行更合理地引导社会公众的通胀预期,实现物价稳定与产出稳定的动态平衡具有一定的参考价值。鉴于中国转型时期通胀因素的叠加性与复杂性,本文还提出金融宏观调控应综合考虑包括核心CPI、CPI在内的多种通货膨胀指标,在维持长期物价稳定的同时,兼顾物价的短期波动。     

SVM分类法的参数优化研究——以黄河口湿地应用为例

ENVI中的SVM监督分类方法有四种核函数,通过选取不同的核函数及调整各核函数的参数可对分类精度造成影响。本文通过对同景黄河口湿地高光谱CHRIS影像进行分类的实验,尝试寻找并确定"最优"参数,同时横向比较各不同核函数对分类结果的影响,以进一步提高分类精度。     

基于SPIDER法测量飞秒脉冲的数值分析与模拟

阐述了目前国际上公认的用于飞秒脉冲测量的光谱相位相干电场重构法(SPIDER)的光谱图数值分析过程，并对高斯强度分布的变换极限脉冲、线性啁啾脉冲进行数值计算及模拟，都获得了准确还原的飞秒脉冲在频域和时域的强度包络和相位．     

VaR、ES与一致性风险测度

基于分位数的VaR(风险价值)不具有一致性,可能误导投资组合优化和风险管理,ES(预期短缺)测度克服了这一缺点。谱测度和失真风险测度更具一般性,考虑了投资者风险厌恶对风险测度的影响,其中VaR和ES均为其特例。从实用性看,ES仍是业界普遍采用的方法。     

基于谱分析原理的铁路物流运输周期波动评估

提出基于谱分析原理来建立铁路物流运输周期性波动的分析方法,对铁路物流运输发展波动的周期性特征进行了检验.通过采用谱分析方法对我国铁路物流运输中的货物周转量进行测定和分析后发现,我国铁路物流运输存在6至7年为主的中周期波动,并且围绕3年还存在一个作用相对较弱的短周期波动.     

Measuring persistence in aggregate output: ARMA models, fractionally integrated ARMA models and nonparametric procedures

Econometric issues in the estimation of persistence in macroeconomic time series are considered. In particular, the relative merits of estimates based on ARMA models, ARFIMA models and nonparametric procedures are investigated. It is shown that ARFIMA models are inappropriate for the purpose of estimating persistence. Furthermore, some of the criticism leveled in the literature against the use of ARMA models for estimating long run properties is put into perspective. Methodological issues arising in the estimation of ARMA models that are relevant to estimation of persistence are discussed. It is shown how overparameterization of an ARMA model may lead to severely downward biased estimates of persistence. The theoretical results are employed to explain some of the findings in Campbell & Mankiw (1987a) and Christiano & Eichenbaum (1990). The methodological aspects of the paper are also relevant for the problem of estimating the value of a spectral density at any given frequency. An empirical study confirms persistence estimates reported in Campbell & Mankiw (1987a), and shows that ARMA models as well as nonparametric procedures give very similar estimates of persistence if properly applied. First version received: May 1996/final version received: March 1998     

Sentinel-2红边波段在水稻识别中作用研究以浙江省德清县为例

目的 水稻是我国南方地区种植面积最大的粮食作物,准确及时地获取水稻的面积及空间分布信息对于加强农业生产管理、保障国家粮食安全具有重要意义。以往利用卫星遥感技术进行水稻分类研究中影像的红边波段类型没有确定,影响着水稻面积监测的精度和效率。方法 文章选取中国南方典型的水稻种植区——浙江省德清县为研究区,利用覆盖水稻主要生长期的5个时相（2019年5月24日、8月2日、8月17日、10月31日、11月15日）Sentinel-2卫星影像,采用随机森林算法进行水稻分类研究。分析研究区水稻及其余6种典型地物在不同波段下的反射光谱特征,采用J-M距离定量评价各时相下有无红边波段参与的各种地物光谱可分离性,比较不同红边波段下的水稻分类精度,优选水稻分类的关键时相和波段组合。结果 相较于红、绿、蓝波段,7种典型地物在红边、近红外波段光谱差异更为明显。相比于无红边波段,有红边波段参与时水稻与其余6种地物的J-M距离更大,可分离性更强。5个时相中,在8月17日（水稻拔节期）时,水稻与其他地物可分离性最大。当3个红边波段全部参与分类时,水稻与其余6种典型地物的总体分类精度最高为91.58%,Kappa系数为0.89。Sentinel-2影像参与分类的7个波段,按重要性排序是：近红外波段>红边波段2>红边波段3>红波段>红边波段1>绿波段>蓝波段。结论 该研究可为进一步提高国南方地区水稻面积监测精度与效率提供参考依据。     

数字货币市场是否达到了弱式有效？——基于广义谱方法的检验

数字货币市场近些年逐渐吸引了投资者注意，随着交易量的增大和交易者的增加，很多 投资者进入这个市场试图获得超额收益，对于这样缺乏基本面信息而主要靠量价信息驱动的市场， 检验市场弱式有效不仅对投资具有重要意义，也为评价市场信息效率提供了依据。本文选取 OKEX 交易所成交量最大的数字货币BCH、BTC、EOS、ETH 和ITC 的现货和期货合约、芝加哥 交易所的期货合约2017 年至2019 年4 月的日度数据，采用广义谱方法检验了世界主要数字货币 市场是否达到了弱式有效，此方法可以检验收益率序列存在的非线性序列依赖，并允许存在各种 未知形式的条件异方差。广义谱检验结果表明：交易量较高的现货和期货合约均已达到弱式有效， OKEX 交易所期货合约的有效性高于现货，芝加哥期货合约由于低成交量反而未达到弱式有效。