基于动态Nelson—Siegel模型的国债管理策略分析

本文研究动态Nelson—Siegel模型在中国国债市场上的定价能力、预测能力和套期保值能力。实证发现动态模型对国债利率的样本内定价效率高,适用于中期债券定价;应用于动态预测未来市场利率,预测效果显著优于其他时间序列模型,超出了传统的无套利均衡模型;采用该模型下的久期向量免疫技术,能为国债组合提供更好的动态套期保值效果。本研究在中国国债组合积极和消极管理策略中具有实用价值。     

利率互换的定价模型及其实证研究

本文研究利率互换的定价模型，以及利率互换的定价过程。从选取债券到拟合理论即期利率曲线、远期利率曲线，最后拟合出互换利率曲线，并对上述的定价模型和过程进行实证研究。对拟合结果与目前市场报价的相同点和差异进行分析，并对国内利率互换的套期保值策略进行实证研究。     

国债期货对现货价格发现功能的研究

2013年9月6日,国债期货在我国正式重新上市交易. 国债期货作为一种利率衍生工具,是否具备价格发现功能,是否能够对国债现货价格未来走势进行准确的预期和提示,是否能够预期和揭示未来市场利率水平,关系着国债期货市场套期保值者的套保效率,也关系着国债期货市场投机者、套利者对国债期、现价格走势的判断及投资效率.     

中国大豆期货市场最优套期保值比率的实证研究

在总结评述国际上成熟的最优套期保值比率估计方法的基础上,采用OLS、VAR、B-ECM、B-GARCH、ECM-B-GARCH五种模型和Lien提出的套期保值绩效衡量指标,对我国大豆期货市场的套期保值比率和套期保值绩效进行了实证研究。结果表明:对于中国大豆期货市场而言,按照OLS模型估计的最优套期保值比率进行动态套期保值能够最大程度地降低风险;基于VAR模型与B-ECM模型的结果次之;按照B-GARCH模型和ECM-B-GARCH模型估计的最优套期保值比率进行动态套期保值,风险降低程度最小。     

国债期货在我国商业银行利率风险管理中的应用研究

国债期货的出现为商业银行提供了一种成本低、流动性高的利率期货产品,有助于商业银行进行利率风险管理。本文在充分阐述商业银行在利率市场化提速进程中面临的各种风险的基础上,深入探究了国债期货在商业银行利率风险敞口套期保值,组合投资及套利交易,以及商业银行理财产品创新中的多种用途及风险。鉴于国债期货是一种复杂的金融衍生产品,建议商业银行发展国债期货业务的前提是增强风险控制能力,健全风险控制机制。     

国债利率期限结构的动态变化规律研究——基于Nelson-Siegel曲线的动态建模

本文首先利用我国国债交易价格数据估计了Nelson-Siegel曲线的参数序列,研究发现,Nelson-Siegel曲线的3个参数序列代表了影响期限结构曲线风险变动的长期、斜率和曲率因素.进一步的,在对Nelson-Siegel曲线的参数序列动态建模的基础土,实现了利率期限结构曲线整体的动态变化规律的研究.通过同其他基于短期利率期限结构曲线模型的样本外预测精度比较研究发现,本文提出的方法在预测精度和稳定性上具有显著的优势,能够更好地从整体上拟合我国国债利率期限结构曲线的动态变化规律,该研究可以为债券组合的定价,投资和风险管理策略制定,以及宏观经济政策的前瞻性制定提供可靠的理论及实证依据.     

中国国债利率期限结构的宏观经济效应与预测能力研究

中国国债收益率曲线与宏观经济指标的关联关系和先行关系是宏观调控和金融市场的共同关注点,其中的非线性和时频特征有待拓展研究。基于2006—2022年国债收益率曲线数据,运用动态NS模型拟合国债利率期限结构的研究发现,国债利率期限结构呈现一定的周期性波动特征,随着到期期限的延长,收益率曲线呈逐渐收敛的趋势。运用分位数向量自回归模型研究不同经济水平下国债利率期限结构对宏观经济指标的非线性影响发现,国债收益率水平因子和斜率因子对产出和通货膨胀的影响主要呈现负向效应,当宏观经济处于不同水平时,这种负向效应存在非线性特征,尤其在高经济增长且高通货膨胀时期影响强度更大。采用小波相位谱方法探究时频维度上国债利率期限结构对宏观经济指标预测能力的动态变化发现,水平因子和斜率因子对产出有较强的预测能力,而对通货膨胀的预测能力在2019年后有所弱化。因此,未来应进一步促进国债市场建设,加强国债收益率期限结构监测,优化财政货币政策协调机制。     

国际碳市场与能源市场动态相依关系研究与启示——基于DCC-MVGARCH模型

本文应用基于条件多元t分布的DCC-MVGARCH模型研究CERs期货价格收益同能源期货价格收益之间的动态相依关系,旨在探讨跨品种套期保值的可行性及操作策略,为国内减排企业及时对冲CERs价格波动风险提供经验依据。实证结果显示:CERs期货价格收益和能源期货价格收益之间存在正相关性;相较Euro天然气期货合约与GlobalCoaL期货合约,BRenT原油期货合约更适用于构造套期保值组合;以动态条件相关系数测算时变套期保值比率明显降低了组合收益的方差并提升了组合收益的均值,其套期保值绩效要优于条件相关系数。基于此结果本文认为,国内减排企业应当积极采取相关套期保值策略并定期更新动态条件相关系数均值,政府主管部门亦应着手实施碳资源战略储备以对冲风险。     

关于中国铝期货市场套期保值绩效的实证研究

套期保值是企业利用期货市场进行风险控制的重要过程。利用金融衍生产品进行风险管理的关键问题是确定套期保值比率。通过对中国沪铝期货套期保值绩效的实证研究发现基于DVECH-GARCH的动态套期保值比基于OLS的静态套期保值避险效果好。选择一定的套保模型进行对冲交易,铝加工企业能够有效地分散铝现货的市场风险,稳定企业生产经营。     

引入基差影响因素的套期保值模型优化与效用对比分析——以中国大豆加工与经营企业为例

中国是全球最大的大豆进口国,国内大豆压榨企业在用境外定价中心的期货合约进行套期保值时,面临较大的基差风险。现有套期保值模型中,多将基差作为套期保值模型的不可观测变量,这与大豆压榨企业现实需求不符。为此,将基差影响因素中可解释部分引进套期保值模型,得到基差调整后的套期保值比率和套期保值有效性。运用Copula-GARCH模型实证分析后发现,引入基差影响因素的套期保值模型效果大多数优于原有套期保值模型,这对我国压榨企业的套期保值实践具有重要的指导意义。     

中国利率期限结构的货币政策含义

本文采用Nelson-Siegel参数模型连续估计了中国利率期限结构曲线,实证了远期利率对未来即期利率的预测能力,分析了央行货币政策措施对利率期限结构的影响和实施效果,研究了利率期限结构与未来通货膨胀的关系。研究结果表明,中国利率期限结构能够为研究制定货币政策提供大量有用的信息。     

货币政策对利率期限结构的影响——基于动态Nelson-Siegel模型的实证分析

以中国人民银行发行的央票利率为货币政策变量,以动态Nelson-Siegel模型为基础构造动态因子模型,采用卡尔曼滤波估计利率期限结构因子,与货币政策变量一起建立误差修正模型,以此分析货币政策对利率期限结构的短期动态影响和长期均衡影响;同时基于中国银行间市场债券交易数据进行的实证分析表明:货币政策和利率期限结构之间的短期动态影响表现出非对称性,即债券市场对货币政策变化的反应较为迟缓,但货币政策对市场利率的变化反应敏锐。而长期均衡关系则表明,货币政策对银行间债券市场利率期限结构有显著影响,但银行间债券市场对央行的利率调控目标不敏感,不能形成明确预期。另一方面,货币政策对目标利率的市场引导效果十分敏感,银行间市场债券交易信息是央行制定货币政策的依据。     

Predicting Canadian recessions using dynamic probit modelling approaches

Abstract This paper examines the ability of various financial and macroeconomic variables to forecast Canadian recessions. It evaluates four model specifications, including the advanced dynamic, autoregressive, dynamic autoregressive probit models as well as the conventional static probit model. The empirical results highlight several significant recession predictors, notably the government bond yield spread, growth rates of the housing starts, the real money supply and the composite index of leading indicators. Both the in‐sample and out‐of‐sample results suggest that the forecasting performance of the four probit models is mixed. The dynamic and dynamic autoregressive probit models are better in predicting the duration of recessions while the static and autoregressive probit models are better in forecasting the peaks of business cycles. Hence, the advanced dynamic models and the conventional static probit model can complement one another to provide more accurate forecasts for the duration and turning points of business cycles.     

Forecasting Canadian time series with the New Keynesian model

Abstract.  This paper assesses the out-of-sample forecasting accuracy of the New Keynesian Model for Canada. We estimate a variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. We compare these forecasts with those arising from vector autoregression (VAR) models, using econometric tests of forecasting accuracy. We show that the forecasting accuracy of the New Keynesian Model compares favourably with that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model could become a useful forecasting tool for Canadian time series.     

FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods that incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.     

基于少数据的城市住宅价格预测分析

本文选取了影响城市住宅价格的多种因素,结合中国城市住宅市场下少数据的具体现实,在灰色理论的预测方法与技术的基础上,基于单因素的GM(1,1)预测模型构建了城市住宅价格多因素预测模型,结合西安市的城市住宅价格以及相关数据构建了西安市城市住宅价格预测模型,并对西安市未来城市住宅价格进行了模拟预测.     

Forecasting US recessions with various risk factors and dynamic probit models

This paper extends probit recession forecasting models by incorporating various recession risk factors and using the advanced dynamic probit modeling approaches. The proposed risk factors include financial market expectations of a gloomy economic outlook, credit or liquidity risks in the general economy, the risks of negative wealth effects resulting from the bursting of asset price bubbles, and signs of deteriorating macroeconomic fundamentals. The model specifications include three different dynamic probit models and the standard static model. The out-of-sample analysis suggests that the four probit models with the proposed risk factors can generate more accurate forecasts for the duration of recessions than the conventional static models with only yield spread and equity price index as the predictors. Among the four probit models, the dynamic and dynamic autoregressive probit models outperform the static and autoregressive models in terms of predicting the recession duration. With respect to forecasting the business cycle turning points, the static probit model is as good as the dynamic probit models by being able to flag an early warning signal of a recession.     

Forecasting with large datasets: compressing information before,during or after the estimation?

We study the forecasting performance of three alternative large data forecasting approaches. These three approaches handle the dimensionality problem evoked by a large dataset by compressing its informational content, yet at different stages of the forecasting process. We consider different factor models, a large Bayesian vector autoregression and model averaging techniques, where the data compression takes place before, during and after the estimation of the respective forecasting models. We use a quarterly dataset for Germany that consists of 123 variables and find that overall the large Bayesian vector autoregression and the Bayesian factor augmented vector autoregression provide the most precise forecasts for a set of 11 core macroeconomic variables. Further, we find that the performance of these two models is very robust to the exact specification of the forecasting model.     

Forecasting the yield curve and the role of macroeconomic information in Turkey

In this study we investigate the yield curve forecasting performance of Dynamic Nelson–Siegel Model (DNS), affine term structure VAR model (ATSM VAR) and principal component model (PC) in Turkey. We also investigate the role of macroeconomic variables in forecasting the yield curve. We have reached numbers of important results: 1—Macroeconomic variables are very useful in forecasting the yield curve. 2—The forecasting performances of the models depend on the period under review. 3—Considering the structural break which associates with change in monetary policy leads models to produce better forecasts than the random walk. 4—The role of exchange rate should not be ruled out in forecasting the yield curve in an emerging market like Turkey.     

Forecasting Time Series Subject to Multiple Structural Breaks

This paper provides a new approach to forecasting time series that are subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks occurring over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the parameters from the meta-distribution that characterizes the stochastic break-point process. In an application to U.S. Treasury bill rates, we find that the method leads to better out-of-sample forecasts than a range of alternative methods.