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。     

不同时间窗下铁矿石期货价格预测精度研究——基于CNN-LSTM注意力模型的分析

利用时间窗提升铁矿石期货价格预测精度对铁矿石期货市场平稳发展具有重要意义。本文选取2013年10月至2021年12月铁矿石期货价格及同期相关数据,采用STL分解方法对铁矿石期货价格进行特征分析,构造基于自注意力机制的CNN-LSTM模型,预测铁矿石期货价格并进行对比分析。结果表明：将铁矿石期货季节性规律应用于时间窗可以提升铁矿石期货价格预测结果准确性。在4、7、30、365天时间窗下,最佳预测结果是4天时间窗。模型预测结果的平均绝对误差MAE值为11.5,相较于LSTM、LSTM-ATT、CNN-LSTM基准模型分别降低了32.70%、19.12%、22.28%。构建模型具有较好的泛化性,MAE在7天、30天、365天时间窗下均为最低。基于此,应关注价格的时间窗特征,完善铁矿石期货市场环境,推动铁矿石现货市场保供稳价。     

基于深度学习的期权价格预测研究

期权作为一种高杠杆的金融衍生品,拥有出色的套利和对冲性能。传统的无套利期权定价方法,包括Black-Scholes期权定价模型、Merton模型和Heston模型,他们的诞生均具有严格的假设条件,同时使用随机过程拟合期权价格走势。然而,由于实际期权市场与上述严格的假设条件具有较大差异,因此传统的无套利期权定价方法无法还原实际市场中的期权定价过程。由此,本文着眼于数据驱动方法,采取深度学习算法来模拟期权定价过程。本文选取上证50ETF期权的数据,在传统无套利的Black-Scholes期权定价理论基础上,利用深度学习中的BP神经网络及LSTM神经网络模型对欧式期权定价进行可行性研究。本文建立两个期权价格预测模型,并利用这两个模型分别对期权价格进行预测,用MSE、MAE和R-squared这三个模型评价指标来描述不同模型的预测精度。实证结果表明,LSTM模型的预测精度在预测上证50ETF期权价格时具有显著优势。     

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。     

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。     

Cointegration,unbiased expectations,and forecasting in the BIFFEX freight futures market

The relationship between freight cash and futures prices is investigated using cointegration econometrics. Results illustrate that the BIFFEX futures market is unbiased, and hence efficient for the current, one, two, and quarterly contract horizons. Since the futures contract is based on an index of various shipping routes, which has undergone several changes since its inception, stability in the relationship between the spot and futures rates is investigated using rolling cointegration techniques. Results indicate that the futures contract appears to have become more efficient over time in predicting the spot rate, and that the decrease in trading volume found in the BIFFEX market is not driven by a lack of efficiency in this market. Rather, the decrease in futures trading might be attributed to the growth rate of the freight forward market. This article incorporates the long‐run cointegrating relationships between cash and futures prices in a forecasting model and compares the forecasting performance of this model with several alternatives. It is found that while the futures price is the best predictor of future spot rates for the current‐month contract, time‐series models can outperform the futures contract at longer contract horizons. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:545–571, 2000.     

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%.