The Integration of Artificial Neural Networks and Text Mining to Forecast Gold Futures Prices

Although a previous study found that neural network forecasts were more accurate than time series models for predicting Latin American stock indexes, the forecasting accuracy of neural network for predicting gold futures prices has never been discussed. Therefore, the first objective of this study is to compare the forecasting accuracy of a neural network model with that of ARIMA models. Furthermore, the fluctuations in gold futures are not only influenced by the quantitative variables, but also by many nonquantifiable factors, such as wars, international relations, and terrorist attacks. The second objective of this study is therefore to propose the integration of text mining and an artificial neural network to forecast gold futures prices. The historical gold futures prices from 1999 to 2008 were used as training data and testing data, and the prices of 2009 were used to examine the effectiveness of the proposed model. The results of empirical analysis showed that an artificial neural network forecasted gold futures prices better than ARIMA models did. In addition, text mining provided a reasonable explanation of the trend in gold futures prices.     

A Dynamic Analysis of Prices in the U.S. Rice Marketing Channel

Granger causality tests and dynamic multipliers are used to explore the dynamic relationship among prices in the U.S. rice marketing channel. The dynamic multipliers provide information about the speed and magnitude of dynamic price adjustments that is not provided by the Granger causality tests. This information is shown to be useful in explaining the underlying economic relationship among the time series of prices.     

The Effects of Detrending in Granger Causality Tests

Nonstationary time series are frequently detrended in empirical investigations by regressing the series on time or a function of time. The effects of the detrending on the tests for causal relationships in the sense of Granger are investigated using quarterly U.S. data. The causal relationships between nominal or real GNP and M1, inferred from the Granger–Sims tests, are shown to depend very much on, among other factors, whether or not series are detrended. Detrending tends to remove or weaken causal relationships, and conversely, failure to detrend tends to introduce or enhance causal relationships. The study suggests that we need a more robust test or a better definition of causality.     

On Granger causality and the effect of interventions in time series

We combine two approaches to causal reasoning. Granger causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially causal relations. On the other hand, the notion of causality as effect of interventions is predominant in fields like medical statistics or computer science. In this paper, we consider the effect of external, possibly multiple and sequential, interventions in a system of multivariate time series, the Granger causal structure of which is taken to be known. We address the following questions: under what assumptions about the system and the interventions does Granger causality inform us about the effectiveness of interventions, and when does the possibly smaller system of observable times series allow us to estimate this effect? For the latter we derive criteria that can be checked graphically and are in the same spirit as Pearl’s back-door and front-door criteria (Pearl 1995).     

中国农产品期货市场的多重分形特征及其成因分析——基于MF--DFA分析法

运用多重分形消除趋势波动分析方法（MF—DFA）和多重分形谱分析法,对中国豆一、玉米、硬麦、强麦4种主要农产品期货价格波动的多重分形特征进行了实证分析和比较。研究发现：中国农产品期货市场存在显著的多重分形特征,硬麦、强麦、玉米和豆一期货的多重分形性依次减弱。通过对原始数据的重排和相位随机化处理,可以看出中国农产品期货市场的多重分形特征是由长程相关性和收益序列的厚尾概率分布两个因素共同导致,其中非正态分布起主导作用。     

能源与经济因果关系检验:回顾与展望

针对国内外能源与经济因果关系众说纷纭的现状,在总结Granger因果关系检验方法发展脉络的基础上,归纳检验变量之间因果关系的统计方法及其在国内外能源消费与经济发展关系实证检验的结果,做出四个代际划分,并按照检验因果关系四个代际的区分与相应的优劣,给出适用于时间序列和面板数据统计因果关系检验的一般步骤。同时指出面板数据、非线性因果关系检验方法将成为研究的发展方向。     

股指期货价格发现功能的实证研究——基于现货指数变化趋势

依据香港股指期货市场数据，分别对香港恒生指数在指数振荡上行与振荡下降两阶段，利用Granger因果关系检验、方差分解、脉冲响应等方法，对股指期货与股指现货价格的领先滞后关系进行研究，得出股指期货在恒指振荡上行阶段具有价格发现功能，在恒指振荡下降阶段，现货领先期货，股指期货不具备价格发现功能。     

大连商品交易所大豆与豆粕期货价格之间的套利研究

摘　　要:本文对大连商品交易所大豆与豆粕期货价格之间的动态关系及套利交易进行了研究,研究结果表明,大豆与豆粕期货价格之间存在长期均衡关系,二个品种期货价格之间相互影响、相互作用。而样本内套利交易的模拟结果显示,无论是否考虑交易费用,多头套利交易的平均利润均大于零,且在统计上显著：而空头套利交易以及所有套利交易的平均利润虽然也大于零,但在统计上并不显著;而从样本外模拟结果来看,套利交易的平均利润并不显著。总体而言,大连商品交易所大豆与豆粕期货价格之间套利交易的赢利能力并不明显。     

On causality test for time series of counts based on poisson ingarch models with application to crime and temperature data

In this study, we consider the causality test for the integer-valued time series. Using the mean equation of Poisson INGARCH models, we construct a regression that includes exogenous variables. The test is then constructed based on the least squares estimator and is shown to follow a chi-square distribution under the null of no causal relationships. A simulation study and real data analysis using the crime and temperature data in Chicago are provided for illustration.     

Bayesian Causality

Abstract

Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes.     

一种协整时间序列的动态因果效应估计与推断方法

为了进一步规避经典面板数据模型因果推断方法中对照样本异质性的问题,以及中断时间序列方法可能存在的“伪回归”问题和“二阶矩平稳”等条件的限制,本文借助时间序列的协整理论,并结合误差修正模型和结构突变检验方法提出了一种基于协整时间序列的实验设计方法,扩展了政策因果效应估计与推断方法。该方法将传统的趋势平稳过程中断时间序列扩展到随机趋势过程,不仅可以推断和估计处理效应,而且解决了政策效应的暂时性和持续性识别问题;另外,协整时间序列的因果效应推断方法更适用于不存在有效对照样本的政策因果效应分析。此外,本文评估了上海市和重庆市的房地产税试点政策,研究发现,长期来看试点政策对两市的商品房价格增长具有抑制作用,但政策效应表现出显著的差异性,上海市的试点政策对抑制房价上涨的力度大、起效快速,但重庆市的房产税政策存在较长期的时滞。     

我国期货市场期货价格收益、交易量、波动性关系的动态分析

我国期货市场发展至今已有十多年的时间 ,但对期货市场内部结构、运行特征的研究非常缺乏 ,本文以国内期货市场最为活跃的期货品种上海期货交易所铜、铝 ,大连商品交易所大豆为代表 ,研究期货价格收益、交易量、波动性之间的动态关系 ,揭示出我国期货市场的内在特征 ,填补国内这方面研究的空白。研究期货价格收益、交易量、波动性之间的动态关系对我们全面认识和把握期货市场具有重要的意义。对期货价格收益与交易量之间关系的研究有助于对期货市场内部结构 ,市场信息传播方式的了解 ;有助于对期货价格分布特征的解释 ;在期货价格收益与交易…     

Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models

The concept of causality is naturally related to processes developing over time. Central ideas of causal inference like time‐dependent confounding (feedback) and mediation should be viewed as dynamic concepts. We shall study these concepts in the context of simple dynamic systems. Time‐dependent confounding and its implications are illustrated in a Markov model. We emphasize the distinction between average treatment effect, ATE, and treatment effect of the treated, ATT. These effects could be quite different, and we discuss the relationship between them. Mediation is studied in a stochastic differential equation model. A type of natural direct and indirect effects is considered for this model. Mediation analysis of discrete measurements from such processes may give misleading results, and one needs to consider the underlying continuous process. The dynamic and time‐continuous view of causality and mediation is an essential feature, and more attention should be payed to the time aspect in causal inference.     

期货报酬时间序列统计特征

期货报酬时间序列统计特性徐剑刚ＡＢＳＴＲＡＣＴＴｈｅｐａｐｅｒｈａｓｓｔｕｄｉｅｄｔｈｅｓｔａｔｉｓｔｉｃａｌｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｒｅｗａｒｄｔｉｍｅｓｅｒｉｅｓ，ａｎｄｆｏｕｎｄａｎｏｒｍａｌｉｔｙａｎｄｄｅｐｅｎｄｅｎｃｙｏｆｆｕｔ...     

Measuring Nonlinear Granger Causality in Mean

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than 6 months. We also find that there is a high degree of predictability at the 1-month horizon, that can be attributed to a nonlinear causal effect. Supplementary materials for this article are available online.     

Testing for spurious and cointegrated regressions: A wavelet approach

This paper proposes a wavelet-based approach to analyze spurious and cointegrated regressions in time series. The approach is based on the properties of the wavelet covariance and correlation in Monte Carlo studies of spurious and cointegrated regression. In the case of the spurious regression, the null hypotheses of zero wavelet covariance and correlation for these series across the scales fail to be rejected. Conversely, these null hypotheses across the scales are rejected for the cointegrated bivariate time series. These nonresidual-based tests are then applied to analyze if any relationship exists between the extraterrestrial phenomenon of sunspots and the earthly economic time series of oil prices. Conventional residual-based tests appear sensitive to the specification in both the cointegrating regression and the lag order in the augmented Dickey–Fuller tests on the residuals. In contrast, the wavelet tests, with their bootstrap t-statistics and confidence intervals, detect the spuriousness of this relationship.     

Real-Time Forecasts of the Real Price of Oil

We construct a monthly real-time dataset consisting of vintages for 1991.1–2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to 1 year. In some cases, real-time mean squared prediction error (MSPE) reductions may be as high as 25% 1 month ahead and 24% 3 months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on autoregressive (AR) and autoregressive moving average (ARMA) models, and the no-change forecast. In addition, these VAR models have consistently higher directional accuracy.     

交通拥堵与雾霾污染的因果关系——基于收敛交叉映射技术的经验研究

交通拥堵与雾霾污染是制约现代城市发展的两大顽疾，准确识别交通拥堵与雾霾污染之间的交互影响，有助于城市管理者重新审视现行治堵与治霾政策的合理性。本文借助大数据平台采集了我国99个城市的高德拥堵延迟指数（CDI）、空气质量指数（AQI）及六种分项空气污染物浓度日报数据，首次采用收敛交叉映射（CCM）方法实证考察了交通拥堵与雾霾污染之间的因果关系。研究发现，CDI与AQI以及CDI与分项污染物组成的动态系统均呈现明显的非线性与弱耦合特征。基于CCM检验结果，大多数城市的CDI与AQI之间不存在显著的因果关系；从分项空气污染物的角度，大多数城市的CDI与主要空气污染物之间不存在显著因果关系，但与次要空气污染物之间却存在显著的单向或双向因果关系。上述结果表明，尽管交通拥堵与雾霾污染之间有一定关联，但在因果关系上现有的经验证据并不支持两者相互影响，治堵和治霾不能“一箭双雕”而必须“双管齐下”。本文的研究在经验上丰富了关于交通拥堵与雾霾污染交互影响的讨论，对城市管理者更加谨慎与合理地制定治堵政策与治霾政策有重要现实意义。     

Vector autoregression and causality: a theoretical overview and simulation study

This paper provides a theoretical overview of Wald tests for Granger causality in levels vector autoregressions (VAR's) and Johansen-type error correction models (ECM's). The theory is based on results in Toda and Phillips (1991a) and allows for stochastic and deterministic trends as well as arbitrary degrees of cointegration. We recommend some operational procedures for conducting Granger causality tests that are based on the Gaussian maximum likelihood estimation of ECM's. These procedures are applicable in the important practical case of testing the causal effects of one variable on another group of variables and vice versa. This paper also investigates the sampling properties of these testing procedures through simulation exercises. Three sequential causality tests in ECM's are compared with conventional causality tests in levels and differences VAR's.     

Vector autoregression and causality: a theoretical overview and simulation study

This paper provides a theoretical overview of Wald tests for Granger causality in levels vector autoregressions (VAR's) and Johansen-type error correction models (ECM's). The theory is based on results in Toda and Phillips (1991a) and allows for stochastic and deterministic trends as well as arbitrary degrees of cointegration. We recommend some operational procedures for conducting Granger causality tests that are based on the Gaussian maximum likelihood estimation of ECM's. These procedures are applicable in the important practical case of testing the causal effects of one variable on another group of variables and vice versa. This paper also investigates the sampling properties of these testing procedures through simulation exercises. Three sequential causality tests in ECM's are compared with conventional causality tests in levels and differences VAR's.