中国利率风险溢酬:时变特征与影响因素

利率风险溢酬是长期利率的组成部分,解读它所包含的信息、寻找它的来源有着重要的经济意义。本文先使用利率仿射模型,计算出先验的中国国债利率期限溢酬,然后构建VECM模型,运用脉冲响应、方差分析等技术,分析国债利率的风险溢酬和主要宏观经济变量的动态关系,发现宏观变量对溢酬的影响在当期和滞后几期有明显差异,CPI和GDP是影响最大的两个因素,但信贷供应量和M1的作用也较大。我们同时也发现银行间市场投资者比交易所市场投资者更易受到宏观经济的影响。     

中国债券市场动态利率模型及其实证检验

为了对中国债券市场动态利率期限结构进行深入的研究,本文基于状态空间模型和卡尔曼滤波技术构建了中国债券市场动态利率模型。本文模型根据数据的可观测结构进行建模,通过迭代计算寻找不可观测状态变量的最优估计值和隐含参数,很好地解决了传统计量方法中因为变量不可观测而无法获得真实数据所带来的研究困难。同时通过模型有效性的模拟实验和中国债券市场同业拆借利率的实证研究,证明了模型对利率期限结构在一段时间内的动态变化估计结果准确,在建模样本期内利率的动态变化能够得到有效的分析和预测。本文的研究为中国债券市场动态利率管理和定价问题提供了新的思路和可能的解决渠道。     

关于货币市场结构转换利率期限结构的实证研究

基本的利率期限结构模型均未能将结构转换效应考虑进来,因此为了探讨结构转换架构下利率期限结构模型的特性,本文在中国货币市场利率数据的基础上对基本利率期限结构模型和结构转换利率期限结构模型进行了比较研究,结果发现中国货币市场利率动态中存在明显的结构转换效应,且在结构转换效应中其本身也存在着不稳定性,这充分反映了中国货币市场在发展过程中的不成熟特征.     

基于NS混合模型的中国利率期限结构动态估计比较研究

采用NS混合模型动态估计中国利率期限结构,考察动态NS模型,无套利NS模型及广义无套利NS模型等NS混合模型对我国利率期限结构的动态估计效率,比较NS混合模型的样本外预测能力,检验无套利约束对混合模型动态估计的影响.本文的经验分析结果表明:无套利条件的引入增强了NS混合模型的样本内动态估计能力和样本外预测能力;五因素的广义无套利NS模型(AFGNS)无论在利率期限结构样本内动态估计还是在总体预测效率上都要高于其他模型,可将其作为利率期限结构研究的基础模型:     

短期出口信用保险报损率宏观经济影响因素研究

影响短期出口信用保险报损率的主要因素是海外信用风险水平.选取欧洲地区工业增加值、进口额、货币供应量、利率、汇率等宏观经济指标作为研究欧洲地区的短期险报损率的解释变量,探讨其宏观经济因素同报损率的内在关系.利用偏最小二乘方法建立模型,实证结果表明,工业增加值、进口额、货币供应量、利率与报损率有负相关关系,美元兑欧元汇率与报损率有正相关关系.     

Vasicek利率与Heston模型下的最优投资和再保险

本文主要研究Vasicek随机利率模型下保险公司的最优投资与再保险问题.假设保险公司的盈余过程由带漂移的布朗运动来描述,保险公司通过购买比例再保险来转移索赔风险;同时,将财富投资于由一种无风险资产与一种风险资产组成的金融市场,其中,利率期限结构服从Vasicek利率模型,且风险资产价格过程满足Heston随机波动率模型.利用动态规划原理及变量替换的方法,得到了指数效用下最优投资与再保险策略的显示表达式,并给出数值例子分析了主要模型参数对最优策略的影响.     

基于线性规划和多项式样条函数的利率期限结构模型

针对线形规划利率期限结构模型只能得到离散贴现率,提出利用多项式插值方法拟合出连续光滑的利率期限结构.并依据样本内外国债信息把它与多项式样条函数模型进行了实证比较,前者的价格相对误差分别为0.45%和1.51%,后者分别为4.18%和4.5%.结果表明线性规划利率期限结构模型与多项式插值相结合的方法在拟合我国利率期限结构方面具有一定优势.     

政府支出结构、扭曲性税收及内生贴现因子

在一个无穷期限模型中,假设经济主体具有完全理性,通过将常数贴现因子转换为关于效用水平的内生变量,利用跨期优化方法重新考虑了政府支出结构、扭曲性税收对宏观经济变量的影响.尤其关注这两种变量的长期和短期变化对资本积累路径的影响,理论模型表明,当贴现因子内生化后,政府支出结构和扭曲性税收对宏观经济的影响会更为复杂,尤其体现在生产性支出上.     

考虑流动性的我国国债价格预测研究

随着金融改革的深化和利率市场化脚步的加快,我国的国债交易和国债市场已经得到了高速发展和充分成长.但在国债利率期限结构的研究方面还不够充分,仍有进一步完善的空间,在利率期限结构研究中考虑流动性的影响就是其中之一.从利率期限结构估计入手,将流动性以权重形式加入NSS模型,估计参数并预测国债价格.研究结果表明,加入流动性权重后,利率期限结构的预测性能显著提高,而且随着步长加大,效果更明显.     

基于货币政策传导机制的中国货币政策工具效应分析

建立了一个包含利率、信贷、资产价格及汇率传导的货币政策综合传导机制模型,使用近一个经济周期的货币金融类数据研究价格调控与数量调控对各宏观经济主要变量的冲击反应关系.研究结果显示,利率的冲击响应关系并不明显,近期利率上调对抑制物价的作用可能在未来并不明显.而相较之下利用公开市场直接投放和回笼货币对在抑制通胀的同时保证经济平稳运行更为有效.     

A comparative evaluation of alternative models of the term structure of interest rates

In the paper alternative models of the term structure of interest rates are classified in two different approaches: the no-arbitrage and the general equilibrium approach. It is maintained that the general equilibrium approach is superior on a theoretical ground for two main reasons: first, relevant variables, such as the spot interest rate and the interest risk-premium, are endogenous; second, the relationship between the real and the financial side of the economy becomes a clear and important element in the understanding of the term structure. As regards the applications, however, the advantages of the general equilibrium over the no-arbitrage approach are not so clear: the major role in the empiricil performance of alternative models is played by their ability to capture volatility. At the current state of the literature, there is no model that outperforms others, in particular on the empirical side.     

我国股市网络稳定性及其宏观影响因素——基于三种网络拓扑结构、鲁棒性的实证研究

基于金融时间序列的多重分形特征及衡量市场风险的VaR模型,建立我国沪深股市的股票关联网络,实证研究三种网络拓扑结构特征,并使用协整检验方法分析网络稳定性和宏观经济变量间的长期均衡关系。结果表明:股票价格网络不具有无标度性,多标度网络和风险网络都具有无标度性;在三种网络中,风险网络具有更强的鲁棒性。此外,股市波动率和网络稳定系数间互为格兰杰因果关系,股市波动的前期变化能有效解释网络稳定性系数的变化;网络稳定性与宏观经济变量间具有长期的均衡关系,GDP增长率、消费者物价水平CPI对网络稳定性具有正向效应,利率对网络稳定性具有负向效应。风险网络的提出有助于分析我国股市的短期风险及稳定性,并为制定系统风险防御策略提供参考。     

中国银行间拆借利率扩散模型的极大拟似然估计

本文用极大拟似然估计法估计了中国银行间市场七天拆借利率扩散模型的参数。并用自助法对众多不同的模型进行了广义拟似然比检验。结论表明:中国货币市场利率具有均值回复效应:利率敏感系数γ值为1.421265,对利率水平具有较高敏感性。     

Shibor期限结构动态研究

近年来央行一直推动利率市场化进程,央行将把上海银行间同业拆放利率(Shibor)培育成基准利率。为了推动利率市场化进程,本文以Shibor作为研究对象,构造了Shibor期限结构的基础模型。为了进一步找出Shibor的变化规律,对于基础模型我们做了不同的扩展,并应用时间序列模型对我国的Shibor期限结构进行了实证分析,发现:隔夜、1周、2周、1个月Shibor具有很强的均值回复特性,3个月、6个月、9个月及1年Shibor不具有显著的线性均值回复特征;1个月、3个月、6个月、9个月及1年Shibor期限结构的扩散项部分是对称的,而隔夜、1周、2周Shibor期限结构的扩散项部分存在明显的不对称性。     

The term structure of interest rates in the economic and monetary union

The main purpose of this article is to present a new numerical procedure that can be used to implement a variety of different interest rate models. The new approach allows to construct no-arbitrage models for the term structure, where the stochastic process driving the rates is infinitely divisible, as in the cases of pure-diffusion and jump-diffusion mean reverting models. The new method determines a unique fully specified hexanomial tree, consistent with risk neutral probabilities. A simple forward recursive procedure solves for the entire tree. The proposed lattice model, which generalized the Hull and White [37] single-factor model, is relatively simple, computational efficient and can fit any initial term structure observed in the market. Numerical experiments demonstrate how the jump-diffusion mean reverting model is particularly suited to describe the European money market rates behavior. Interest rates controlled by the monetary authorities behave as if they are jump processes and the term structure, at short maturity, is contingent upon the levels of these official rates.     

非均衡宏观调控最优控制模型及应用

本文基于最优控制理论原理 ,从理论上给出宏观调控的最优数学模型 ,为国民经济宏观调控的定量评价提供了理论依据和实证分析 .     

Development and calibration of a currency trading strategy using global optimization

We have developed a new financial indicator—called the Interest Rate Differentials Adjusted for Volatility (IRDAV) measure—to assist investors in currency markets. On a monthly basis, we rank currency pairs according to this measure and then select a basket of pairs with the highest IRDAV values. Under positive market conditions, an IRDAV based investment strategy (buying a currency with high interest rate and simultaneously selling a currency with low interest rate, after adjusting for volatility of the currency pairs in question) can generate significant returns. However, when the markets turn for the worse and crisis situations evolve, investors exit such money-making strategies suddenly, and—as a result—significant losses can occur. In an effort to minimize these potential losses, we also propose an aggregated Risk Metric that estimates the total risk by looking at various financial indicators across different markets. These risk indicators are used to get timely signals of evolving crises and to flip the strategy from long to short in a timely fashion, to prevent losses and make further gains even during crisis periods. Since our proprietary model is implemented in Excel as a highly nonlinear “black box” computational procedure, we use suitable global optimization methodology and software—the Lipschitz Global Optimizer solver suite linked to Excel—to maximize the performance of the currency basket, based on our selection of key decision variables. After the introduction of the new currency trading model and its implementation, we present numerical results based on actual market data. Our results clearly show the advantages of using global optimization based parameter settings, compared to the typically used “expert estimates” of the key model parameters.     

Stochastic modelling of long-term investment risks

There are many risks that individuals, firms, and societieshave to face, and among them are the uncertainties of futureinvestment variables, which include inflation (both of pricesand earnings), interest rates,exchange rates, and returns onordinary shares (including both dividend income and changesin capital values).These investment risks affect inviduals intheir own financial planning; affect companies in planning investmentprojects and in arrangements for raising capital;affect governmentsand government institutions that have to borrow in the capitalmarkets; and especially affect investment institutions and intermediarieswho take on borrowings, deposits, insurance contracts, or pensionfund liabilities on the one hand and invest assets in loans,ordinary shares, property, or other investments on the other. A great deal of work done by financial economists in recentdecades has established reasonable models for describing movementsof many investment variables in the short run. Typically thesemodels are based on a 'random walk' or Gauss-Wiener continuousdiffusion process. This sort of model has been particularlyvaluable to market-makers and other investment participantswhose time horizon is short. But these short-term models oftendo not provide a satisfactory structure for the long term. Thispresentation will describe some of the author's work in thestatistical analysis of long-term investment series, both inthe United Kingdom and in other countries, based on statisticaltime-series analysis of historical data. Although many of the series could be valued using multivariatemethods, such as vector autoregressive (VAR) models, preliminaryinvestigation showed that many of the series could be investigatedin a 'cascade' fashion, with price inflation being put as theinitial 'driver'. A very long historic series shows long periodswhen changes in prices in successive years could be taken asrandom, with zero drift, and other periods (including most ofthis century) when inflation rates in successive years werecorrelated. A similar pattern has applied in recent years inmany other countries. It is postulated that the prices of ordinary shares in aggregateare closely related to the dividends paid on them, so that theratio between dividend and price, i.e. the dividend yield, isstationary—fluctuating around a constant mean. The dividend-yieldseries can be described by means of a first-order autoregressivetime-series model,while the dividend series can be describedby a model that depends on inflation in the current and precedingyears, with an appropriate time lag. Interest rates, both long-term and short-term, are first decomposedinto an allowance for prospective future inflation and a 'real'rate of interest, comparable to the yield on index-linked stocks.The real rate of interest can also be modelled as a mean-revertingautoregressive model. The allowance for future inflationcanbe derived as a moving average of past inflation rates.In orderto link models for different countries, it is necessary to havea model for currency exchange rates. This can be done by postulatinga hypothetical 'purchasingpower parity' exchange rate, whichexactly allows for changes in inflation, and then by modellingthe deviation of the actual rate from the hypothetical rateby means of yet another autoregressive model. It is necessaryalso to keep an appropriate structure for cross rates betweenany pair of currencies. This series of stochastic models is particularly useful forestimating future scenarios of all the variables in a consistentmanner, and for estimating their likely variability. In some cases this can be done analytically, but in generalit requires ’Monte Carlo‘ simulations. Various possibleapplications of the model in different fields will be described.