供应链融资业务中钢材质押贷款动态质押率设定的VaR方法

异于债券、股票等质押融资业务,存货质押业务动态质押的核心在于预测其长期价格风险。从分析存货质押市场收益率的统计特征出发,以场外现货交易为主的钢材((HRB335)日数据为例,建立能刻画钢材收益率序列异方差性和尖峰厚尾特性的VaR-GARCH(1,1)-GED模型。同时,提出置于多风险窗口下运用样本外预测未来质押期内钢材价格风险水平,给出厚尾分布下长期风险VaR的计算解析式,得出与银行风险承受能力相一致的质押率。进而,基于失效率法则建立长期风险的碰撞序列函数,回测多风险窗口下长期VaR值。实证分析显示,模型得到的质押率在控制好风险的同时降低了效率损失,为商业银行提供一种动态质押率的风险管理模式和框架。     

基于重现时间间隔分析的极端收益预测及交易策略研究

极端收益的预测在金融风险管理中非常重要。本文系统研究了极端收益重现时间间隔的统计规律,提出了一种基于重现时间间隔分析的早期预警模型,并对极端收益的重现进行预测,检验了模型在样本内外的预测性能;最后分别针对极端正收益和极端负收益的样本外预测结果,设计了看涨和看跌的两种交易策略,并以中国上证指数、法国CAC40指数、英国富时指数、香港恒生指数和日本日经指数为例,对交易策略的日均收益率进行了统计显著性检验。研究结果表明,极端收益的重现时间间隔具有右偏、尖峰厚尾和强自相关等典型特征;极端收益预测模型在样本内和样本外检验中都具有良好的预测能力;看涨和看跌交易策略在卖出区间均能有效地避开下跌阶段,看涨策略有更显著的盈利水平。     

基于GARCH模型对人民币汇率波动的实证研究

目的/意义近年来中国已成为日本第一大进出口贸易伙伴,而人民币汇率改革使得人民币的波动区间不断增加,人民币对日元的汇率波动对中日贸易的影响显得十分重要。设计/方法利用门限回归模型将人民币对日元的汇率波动分为高波动和低波动两类,在这两类波动情况下,汇率波动对出口分别有不同的影响。结论/发现实证研究结果有：（1）门限效应检验表明汇率波动对出口有非线性影响。（2）门限回归模型的参数估计显示人民币对日元汇率波动较小时,中国对日本的出口贸易受到汇率波动的负向影响;而波动较大时,影响不显著。（3）对样本分不同时间预测,结果显示汇率波动是预测出口变化的一个重要因素,并且门限非线性模型优于线性模型。     

书刊需求量的预测分析方法

运用数理统计原理 ,结合实例 ,分析了弹性系数法、市场总潜力预测法、线性回归预测法在书刊需求预测中的合理运用。     

汽车销售混合预测方法研究

市场分析和预测已成为企业重要的决策依据和手段。就汽车销量问题提出了一种ARMA模型与RBF神经网络相结合的混合预测方法。采用ARMA模型对汽车销量趋势进行初步线性预测,利用RBF神经网络对线性预测的残差建模,得到非线性预测,两部分预测输出和为总的预测值。该方法既体现了销售量数据间的线性关系, 又揭示了数据内部的非线性特征,克服了单一方法的局限性,提高了预测精度。仿真结构分析表明,该方法预测效果最佳。     

Short-term forecasting with a computationally efficient nonparametric transfer function model

In this paper a semi-parametric approach is developed to model non-linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non-linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real-life data set to forecast hourly electricity usage. The non-linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post-sample forecasting and compared with several well-accepted models. The results show the performance of the proposed model is comparable with a long short-term memory deep learning model.     

On the role of local blockchain network features in cryptocurrency price formation

Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. The Canadian Journal of Statistics 48: 561–581; 2020 © 2020 Statistical Society of Canada     

A Benchmarking Approach to Forecast Combination

This article is concerned with the development of a statistical model-based approach to optimally combine forecasts derived from an extrapolative model, such as an autoregressive integrated moving average (ARIMA) time series model, with forecasts of a particular characteristic of the same series obtained from independent sources. The methods derived combine the strengths of all forecasting approaches considered in the combination scheme. The implications of the general theory are investigated in the context of some commonly encountered seasonal ARIMA models. An empirical example to illustrate the method is included.     

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.     

Bayesian Dynamic Factor Models and Portfolio Allocation

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions.