大豆期现货市场价格动态关系的实证研究

本文利用VAR模型和误差修正模型等方法对国内和国际大豆的现货价格和期货价格之间的长短期关系进行实证分析。根据研究得出,国内和国际大豆现货价格之间存在长期的协整关系,国际大豆现货价格对国内大豆现货价格有显著影响,此结论同样适用于国际和国内大豆期货价格之间的分析。但是国际大豆的现货价格在调整速度上明显快于大豆的期货价格。最后,本文从大豆的现货价格、期货市场和自身技术三个方面提出稳定国内大豆价格的建议。     

期货对现货价格预测的作用——基于ARIMA模型的实证探讨

本文通过建立ARIMA模型和ARIMAX模型,以我国HS300指数为研究对象。在准确识别的基础上,实证检验了我国hs300指数的日内指数现货价格序列。通过ARIMAX模型的输入变量包含了IF8888指数期货价格序列,将指数期货价格信息反映到现货价格的预测过程中,同时与ARIMA模型作比较。研究发现,带指数期货价格序列输入变量的ARIMAX模型与不存在其他输入变量的ARIMA模型在相同的参数条件下,前者的拟合误差下降,预测精度显著提高。说明期货价格信息可以更好地预测现货指数价格。同时为了说明预测的可信性,本文选取期货交易所的官方数据。在数据的平稳性检验部分用了ADF检验来进行平稳性的检验和对d值的确定,在对p值和q值的确定上,使用了枚举法来进行最佳组合的选取,这些都保证了预测的精确性和可信性。     

基于聚类分析和时间序列分析的期货预测

时间序列分析方法在金融市场,尤其是股票指数、汇率、利率、期货等证券风险大小的度量、风险收益的计算与市场效率的检验中得到广泛应用。为了预测出下个阶段的期货价格的总体水平,进而帮助投资者提早的对自己的投资选择进行分配,将多元统计分析中的聚类分析方法和非平稳时间序列模型相结合,先将样本数据中的期货价格分类,求出每个类中的价格均值,进而对这些均值做ARIMA模型拟合和预测,预测出接下来的期货价格水平。     

基于聚类分析和时间序列分析的期货预测

时间序列分析方法在金融市场,尤其是股票指数、汇率、利率、期货等证券风险大小的度量、风险收益的计算与市场效率的检验中得到广泛应用.为了预测出下个阶段的期货价格的总体水平,进而帮助投资者提早的对自己的投资选择进行分配,将多元统计分析中的聚类分析方法和非平稳时间序列模型相结合,先将样本数据中的期货价格分类,求出每个类中的价格均值,进而对这些均值做ARIMA模型拟合和预测,预测出接下来的期货价格水平.     

公路交通运输量GM-Markov综合预测模型研究

为了提高公路交通运输量的预测精度,在介绍一般模型的基础上,建立了GM-Markov预测模型,它是将灰色预测方法与Markov预测模型优化组合,用灰色预测模型GM(1,1)预测随机时间序列数据的总体发展趋势,而用Markov模型预测各数据在总体趋势下的随机波动性变化,得到随机时间序列数据趋势预测模型的解。通过公路货运量的实际数据进行了验证,结果表明:GM-Markov预测模型既能预测参数随机数据序列的总体趋势,又能适应波动性较大的随机序列变化,其预测精度高于GM(1,1)模型的预测精度。     

基于GA—BP神经网络模型的期货价格预测与分析

本文选取20091月5日～10月29日的大豆期货主力i1001合约共200个交易数据作为训练数据，10月30日～11月12日的10个数据为测试数据，利用BP神经网络对期货价格建立预测模型，并用遗传算法进行修正，从而实现对大豆期货交易价格的预测分析。结果表明，改进后的GA—BP神经网络模型拟合精度明显高于BP神经网络模型，并对期货价格走势有良好的预测效果，可给期货市场的投资者提供投资建议。此外，利用改进后的模型可对期货市场操纵现象进行预警，对监管者具有一定参考价值。     

对我国农产品期货市场价格有效性的实证检验

本文从考察期货价格与未来现货价格之间的关系入手,在风险溢价理论框架下,借助协整分析法对我国两大农产品期货市场的价格有效性进行了规范的实证检验。结果显示:大豆和小麦期货价格与未来现货价格之间均存在协整关系,期货价格对最后交易日现货价格具有预测能力,且大豆、小麦期货市场都支持风险溢价假说,在风险溢价条件下呈现有效状态。     

一种超宽带与惯导融合的LSTM室内定位算法

针对复杂环境下的室内高精度定位需求,提出了一种超宽带和惯导融合定位方案。结合位置估计过程可被划分为时间序列预测问题的特点,提出了一种基于长短时记忆(Long Short Term Memory,LSTM) 网络的联合定位算法,并对其总体架构设计、数据预处理方法、网络结构设计、模型训练方法进行了研究。在此基础上,通过仿真和实测实验对联合定位算法进行验证,实验结果表明,该LSTM神经网络联合定位算法的定位精度优于传统TOA(Time of Arrival)、UKF(Unscented Kalman Filter)联合定位算法,适用复杂室内定位。     

慢性病药品采购价格指数预测研究——基于山东省药品集中采购数据的分析

药品集中采购对于稳定药品价格和降低患者用药负担具有重要意义。本文基于山东省药品集中采购数据，编制慢性病药品链式拉氏价格指数，评估山东省药品集中采购政策的实施效果。构建LSTM、双向LSTM和ConvLSTM模型，预测价格指数波动趋势，为政府及管理者提供科学的决策依据。研究结果表明：慢性病药品价格大幅下降，大大减轻了慢性病患者的用药负担，未来慢性病药品价格将呈现稳中有降的趋势，双向LSTM和ConvLSTM模型的预测精度优于传统LSTM模型，两模型泛化能力更好，对山东省慢性病药品价格指数预测具有一定的应用价值。据此，提出健全药品集中采购价格监测体系、创新药品价格监测技术手段和深化药品生产流通体制改革等建议，推动药品采购与监测技术研究一体化建设。     

基于随机森林模型的生猪价格预测及调控机制研究

生猪产业在国民经济中的重要地位及自身的“弱质性”决定了国家对生猪产业发展的原则是“市场主导、政府调控”,而国家对生猪产业宏观调控的有效性取决于用科学的方法 对生猪价格进行预测。研究得出:随机森林价格预测模型中的两个最优参数ntree、mtry的值分别为450和4,随机森林模型预测的残差平方和0.72,拟合优度为98.25%,说明拟合效果较好;在特征选取上,综合平均下降精度和平均下降基尼系数的排序结果,得出影响生猪价格因素最重要的两个变量是去皮带骨猪肉和仔猪价格;随机森林模型和BP神经网络模型对价格的预测对比发现:随机森林模型中均方误差和平均绝对误差分别为0.0231和0.408,BP神经网络模型中均方误差和平均绝对误差为0.0828和0.71,随机森林在生猪价格预测方面预测精度更优。     

Forecasting volatility: Roles of sampling frequency and forecasting horizon

This study empirically tests how and to what extent the choice of the sampling frequency, the realized volatility (RV) measure, the forecasting horizon and the time‐series model affect the quality of volatility forecasting. Using highly synchronous executable quotes retrieved from an electronic trading platform, the study avoids the influence of various market microstructure factors in measuring RV with high‐frequency intraday data and in inferring implied volatility (IV) from option prices. The study shows that excluding non‐trading‐time volatility produces significant downward bias of RV by as much as 36%. Quality of prediction is significantly affected by the forecasting horizon and RV model, but is largely immune from the choice of sampling frequency. Consistent with prior research, IV outperforms time‐series forecasts; however, the information content of historical volatility critically depends on the choice of RV measure. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark     

The impact of oil price movements on bank non-performing loans: Global evidence from oil-exporting countries

It is generally believed that economic and financial performance in oil-rich countries are interlinked to oil price movements. On this assumption, we consider whether oil prices shocks have any impact on bank non-performing loans (NPLs), and if so, whether the effect is homogenous across banks. This paper addresses these questions by applying a dynamic GMM model on data from 2310 commercial banks in 30 oil-exporting countries over the period 2000–2014. Three main results emerge. First, changes in oil prices do have a significant impact on bank NPLs: A rise (fall) in oil prices is associated with a decrease (increase) in NPLs. Second, oil prices shocks have asymmetric effects on bank problem loans, with negative oil price movements generally have a greater impact than positive oil price movements. Third, the unfavourable impact of adverse oil prices shocks on the quality of bank loans tends to be more pronounced in large banks. Overall, these robust results favour the adaptation of appropriate macroprudential policies and diversification of the economy, in order to mitigate the adverse impact of oil prices shocks.     

Immigration and international prices

This paper considers the relation between immigration and prices in a number of countries across the world over the period from 1990 to 2006. Immigration is shown to have a negative impact on international relative prices. A 10% increase in the share of immigrant workers in total employment decreases the prices of final products by as much as 3%. Our results suggest that the tendency of this factor of production to relocate to relatively expensive high-wage countries exerts downward pressure on prices of tradeables and non-tradeables there relative to other locations. The effect of immigration on prices is more evident for goods consumed by immigrants as compared to goods produced by immigrants.     

铁矿石价格上涨的宏观经济影响

近年来,铁矿石价格的持续飙升给我国经济造成了巨大的影响。本文采用国家信息中心与澳大利亚MONASH大学共同开发的SICGE模型对进口铁矿石涨价的影响进行了模拟。结果显示,与2010年基准情景相比,铁矿石价格上涨90%对我国总体经济影响很小(-0.37%)。从物价水平角度来看,与现有文献的结论有很大的不同,铁矿石价格上涨并没有导致输入型的通胀,模拟结果2010年整体物价水平有小幅的下降(-1.96%)。模拟结果也表明,这种生产要素价格上涨确实会向下游企业传导,但是这种传导效应只集中在大量直接使用铁矿石的部门(炼铁、炼钢、钢压延加工业等),对终端消费品(房地产、汽车、家电等)价格影响很小。     

Demand forecasting based on natural computing approaches applied to the foodstuff retail segment

The purpose of this paper is to compare the accuracy of demand forecasting between two classical linear forecasting models (Autoregressive and Integrated Moving Average -ARIMA and Holt-Winter) and two nonlinear forecasting models based on natural computing approaches (Wavelets Neural Networks - WNN and Takagi-Sugeno Fuzzy System - TS), all applied to the aggregated retail sales of three groups of perishable food products from 2005 to 2013. Moreover, this paper evaluates the impact of demand forecasting accuracy on the demand satisfaction rate and on the overall economic performance of retail business operations. The most accurate model, WNN, had a demand satisfaction rate of 98.27% for Group A, 98.83% for Group B and 98.80% for Group C. WNN estimated a loss of revenue of R$1329.14 million/year with a minimum loss of 166 tons/year, which means that the results of WNN are 37.67% more efficient than the TS, 57.49% higher than the ARIMA and 76.79% higher than HW. This paper presents three main contributions: (i) it examines a question not evaluated in the literature on demand forecasting based on natural computing approaches in the foodstuff retail segment that generates better practical results, (ii) it proposes that a single forecasting model could be applied to different product groups and serves the organization as a whole with a good relationship between the cost and the benefit of the process and (iii) like previous studies, it proves that demand forecasting plays an important role and can generate a competitive advantage for the organization to be incorporated into its strategy.     

Forecasting volatility

The forecasting ability of the most popular volatility forecasting models is examined and an alternative model developed. Existing models are compared in terms of four attributes: (1) the relative weighting of recent versus older observations, (2) the estimation criterion, (3) the trade‐off in terms of out‐of‐sample forecasting error between simple and complex models, and (4) the emphasis placed on large shocks. As in previous studies, we find that financial markets have longer memories than reflected in GARCH(1,1) model estimates, but find this has little impact on outofsample forecasting ability. While more complex models which allow a more flexible weighting pattern than the exponential model forecast better on an in‐sample basis, due to the additional estimation error introduced by additional parameters, they forecast poorly out‐of‐sample. With the exception of GARCH models, we find that models based on absolute return deviations generally forecast volatility better than otherwise equivalent models based on squared return deviations. Among the most popular time series models, we find that GARCH(1,1) generally yields better forecasts than the historical standard deviation and exponentially weighted moving average models, though between GARCH and EGARCH there is no clear favorite. However, in terms of forecast accuracy, all are dominated by a new, simple, nonlinear least squares model, based on historical absolute return deviations, that we develop and test here. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:465–490, 2005     

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Toward this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter’s social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.     

土地管制、市场价格与政策选择

本文从宏观和微观两个层面考察土地管制政策对市场价格的影响,利用35个大中城市的宏观数据,检验土地供给是否对住房价格产生影响,以及其影响程度与时间路径;利用杭州286宗住宅用地微观数据,量化微观管制政策对土地价格的影响方向与程度。我们发现土地供给对住房供给在长期内(1～2年)有显著影响,短期内(1年以内)没有影响;而土地供给对住房价格在长期与短期内都有影响,如通过改变预期影响当年住房价格,通过控制住房供给影响滞后1年的住房价格等。土地出让约束条款能够显著影响土地的出让价格,容积率每增加1%,会引起土地价格上升0.777%,出让地块面积增加1%会导致单位出让价格下降0.108%。进而,从宏观和微观两个层面提出政策建议,促进住房市场与价格保持稳定。     

EXACT SOLUTION OF A MARTINGALE STOCHASTIC VOLATILITY OPTION PROBLEM AND ITS EMPIRICAL EVALUATION

Exact explicit solution of the log-normal stochastic volatility (SV) option model has remained an open problem for two decades. In this paper, I consider the case where the risk-neutral measure induces a martingale volatility process, and derive an exact explicit solution to this unsolved problem which is also free from any inverse transforms. A representation of the asset price shows that its distribution depends on that of two random variables, the terminal SV as well as the time average of future stochastic variances. Probabilistic methods, using the author's previous results on stochastic time changes, and a Laplace–Girsanov Transform technique are applied to produce exact explicit probability distributions and option price formula. The formulae reveal interesting interplay of forces between the two random variables through the correlation coefficient. When the correlation is set to zero, the first random variable is eliminated and the option formula gives the exact formula for the limit of the Taylor series in Hull and White's (1987) approximation. The SV futures option model, comparative statics, price comparisons, the Greeks and practical and empirical implementation and evaluation results are also presented. A PC application was developed to fit the SV models to current market prices, and calculate other option prices, and their Greeks and implied volatilities (IVs) based on the results of this paper. This paper also provides a solution to the option implied volatility problem, as the empirical studies show that, the SV model can reproduce market prices, better than Black–Scholes and Black-76 by up to 2918%, and its IV curve can reproduce that of market prices very closely, by up to within its 0.37%.