我国CPI季节调整模型预测

文章基于考虑春节效应的X-12-ARIMA季节调整模型,对我国2002年1月至2013年12月的CPI序列月度数据进行季节调整,并进行季节波动性分析及短期预测.实证结果表明:我国的CPI变动存在明显的季节性特征,春节效应对其有显著影响;CPI序列的短期波动主要是受季节性成分影响,而长期波动主要受趋势-循环成分影响;利用该模型进行短期预测效果较好,预测误差绝对值控制在1.5％之内.     

交易间隔、超高频波动率与VaR ——利用日内信息预测金融市场风险

 金融市场风险价值研究一般采用日收益数据,并基于GARCH类模型进行估计和预测,这必然会损失部分日内信息。本文尝试使用中国股市日内分笔超高频数据,在分析日内波动特性的基础上,通过UHF-GARCH模型对交易间隔等日内信息建模,得到超高频波动率UHFV。本文用ARFIMA模型对超高频波动率UHFV建模,应用到风险价值VaR的预测中,并同基于日数据的GARCH类模型的VaR预测能力进行比较。VaR似然比和动态分位数等回测检验的结果显示,超高频数据波动率UHFV模型的预测能力强于采用日数据的GARCH类模型。     

金融市场波动对宏观经济的预测信息研究

文章分析了金融市场波动性中隐含的关于未来宏观经济走势的信息。通过构建线性模型进行样本内回归分析和样本外预测精度的比较,实证研究结果表明,我国股票市场波动性蕴含着对未来两年的经济增长和价格水平波动的预测信息;外汇市场对一年内的经济变动的解释能力较强,而债券市场波动和同业拆借市场波动暂未发现关于未来经济的走势明朗的前瞻性信息。     

基于半参数混频误差修正模型的中国CPI预测研究

考虑宏观经济变量具有明显的非线性特征,将非线性误差修正项引入存在协整关系的非平稳混频数据抽样（MIDAS）模型中,构建半参数混频数据抽样误差修正（SEMI-ECM-MIDAS）模型。使用广义似然比（GLR）检验,拓展了混频数据下模型函数形式的一致性检验问题。模拟结果表明SEMI-ECM-MIDAS模型对存在非线性误差修正机制的数据具有显著的预测优势。最后使用该模型研究中国股票市场周度数据、广义货币发行量月度数据和国际原油市场月度数据对中国CPI的短期预测效果。基于AIC准则,对包含半参数模型在内的4种混频数据抽样模型和2种同频模型的连续预测效果进行了全面的比较。研究结果发现：GLR检验表明误差修正项具有明显的非线性特征且在回归中具有显著的反向修正机制,无论采用递归样本、滚动样本还是固定样本,本文提出的SEMI-ECM-MIDAS模型在进行连续预测时均具有最优的预测精度,且预测结果不受混频动态协整关系选择的影响。     

居民消费价格指数的非对称性波动分析及短期预测

居民消费价格指数(CPI)在一定程度上反映了通货膨胀抑或紧缩的程度,受到社会普遍关注.文章基于我国1990年1月～2011年6月的月度数据进行探索性建模分析,通过模型比较发现对D_CPI序列建立AR(2)-ACGARCH(1,1)组合模型最合理,该模型很好的刻画了CPI的非对称性波动特征.研究结果表明D_CPI具有明显的群集效应和逆杠杆效应,即正的外部冲击对价格水平的影响大于负的外部冲击；另外短期预测结果显示2011年第三季度我国CPI月同比增长都将超过6％,预测效果比较理想,较为符合实际情形.     

基于先行指数对我国通货膨胀率的预测

在当前国际经济不确定加大的背景下,物价波动态势和未来走势再度吸引了人们的视线,本文基于景气指数的方法,构建了一致物价指数反映价格波动态势,并且,构建了先行物价指数,反映物价的未来走势。不仅如此,文章还在模型中引入先行物价指数作为解释变量,对我国通货膨胀的波动进行模拟和短期预测,结果表明,在对我国通货膨胀进行预测的模型中加入先行物价指数这一解释变量可以显著地提高预测精度,基于此模型外推我国CPI走势,认为我国通货膨胀在2010年将呈现先增加后降低的波动态势。     

中国宏观经济与股市动态关系研究:1998-2007

文章采用贝叶斯向量自回归计量经济模型,检验了1998年1月-2007年12月的上证A股指数月度收益与6个主要宏观经济变量之间的相关关系.结果显示股指收益与工业增加值正相关,与CPI以及长期利率负相关.但与货币供应量、出口额、投资等变量相关性不明显.根据估计得到的贝叶斯向量自回归模型进行预测,进一步显示了CPI对股市的影响关系.     

基于混频数据的日度经济不确定性测度及其应用

经济不确定性主要反映经济系统的不可预测程度，可以通过经济指标的实际值和预期值之间的偏差进行测度。为及时测度我国的经济不确定性，本文提出一种同比形式的日度混频动态因子模型，将金融市场的日度数据和传统的宏观低频数据信息相结合，构建我国日度经济意外指数和经济不确定性指数，用于反映我国宏观经济运行的非预期成分和不确定性程度。进一步地，本文基于日度数据分别讨论经济意外指数对人民币汇率的影响，以及经济不确定性对我国股市波动率的影响，研究结果表明经济意外指数的正向变化使人民币对美元升值，经济不确定性的增加将加剧股市波动。     

基于ARIMA模型的消费者价格指数预测

科学准确地预测CPI将为宏观经济政策的制定提供合理的数据支持.文章根据我国2010年1月至2015年6月CPI月度数据建立ARIMA模型,对2015年下半年我国的CPI数据进行预测.实证结果表明:ARIMA(12,1,2)模型的预测效果良好,可以作为我国CPI走势判断的有效依据.     

中国金融压力指数的构建及动态传导效应研究

本文选取2007年1月4日至2015年9月30日银行、股票、债券和外汇市场相关指标的日度数据,采用因子分析法首次构建了日度的中国金融压力指数(CFSI),该指数不仅可以准确测度我国金融系统的风险压力情况,为实时监测金融风险提供量化工具,还能用于研究金融压力对宏观经济的动态传导效应,为政策制定者有针对性地制定不同金融压力时期的宏观经济政策提供参考.通过对CFSI建立MS-VAR模型发现,我国金融压力有明显的两区制特征,样本区间内CFSI大多时间处于平稳下降区制,而处于上升区制的时间段则与国内外的一些重大金融压力事件相吻合,说明CFSI的走势能够准确地反映我国的金融压力情况.随后本文选取2007年1月至2015年9月CFSI、CPI、工业增加值增长率和银行间同业拆借利率的月度数据,首次建立TVP-VAR模型研究了CFSI对物价水平、经济增长和利率水平的动态传导效应.模型通过了稳定性检验,结果表明:①CFSI对物价水平的影响以负向为主,并且不同区制下的影响程度有所不同,CFSI在上升区制内对CPI负向影响的强度较高,在平稳下降区制内负向影响的强度较低.②CFSI在不同区制内对经济增长均有明显的负向影响,但影响程度有所不同,CFSI在上升区制内对经济增长负向影响的强度较高,在平稳下降区制内负向影响的强度较低.③CFSI在不同区制内对利率水平会产生不同的影响,CFSI在平稳下降区制内对利率会产生持续的正向影响,短期内影响较强,中长期内影响不断减弱;而在上升区制内,CFSI短期会对利率产生正向影响,中长期内则转变为持续的负向影响.     

Nonparametric testing for long-horizon predictability with persistent covariates

We propose a testing procedure for long-horizon predictability via kernel-based nonparametric estimators of long-run covariances between multiperiod returns and persistent covariates. Asymptotic properties of the proposed tests are studied. As for implementation of the test, sieve bootstrap methods are employed to obtain reasonable approximation to the sample distribution of the test statistics. Monte Carlo simulations are conducted to verify the theoretical conjecture. Empirical analysis, using US monthly data from 1929 to 2011, are presented for testing stock return predictability of some forecasting financial variables. Long-term interest rates, unlike default spreads or price-earning ration, are found to show some forecasting power.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models

This article introduces a new specification for the heterogenous autoregressive (HAR) model for the realized volatility of S&P 500 index returns. In this modeling framework, the coefficients of the HAR are allowed to be time-varying with unspecified functional forms. The local linear method with the cross-validation (CV) bandwidth selection is applied to estimate the time-varying coefficient HAR (TVC-HAR) model, and a bootstrap method is used to construct the point-wise confidence bands for the coefficient functions. Furthermore, the asymptotic distribution of the proposed local linear estimators of the TVC-HAR model is established under some mild conditions. The results of the simulation study show that the local linear estimator with CV bandwidth selection has favorable finite sample properties. The outcomes of the conditional predictive ability test indicate that the proposed nonparametric TVC-HAR model outperforms the parametric HAR and its extension to HAR with jumps and/or GARCH in terms of multi-step out-of-sample forecasting, in particular in the post-2003 crisis and 2007 global financial crisis (GFC) periods, during which financial market volatilities were unduly high.     

Semi-parametric forecasts of the implied volatility surface using regression trees

We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model.     

Modeling Financial Return Dynamics via Decomposition

While the predictability of excess stock returns is detected by traditional predictive regressions as statistically small, the direction-of-change and volatility of returns exhibit a substantially larger degree of dependence over time. We capitalize on this observation and decompose the returns into a product of sign and absolute value components whose joint distribution is obtained by combining a multiplicative error model for absolute values, a dynamic binary choice model for signs, and a copula for their interaction. Our decomposition model is able to incorporate important nonlinearities in excess return dynamics that cannot be captured in the standard predictive regression setup. The empirical analysis of U.S. stock return data shows statistically and economically significant forecasting gains of the decomposition model over the conventional predictive regression.     

Robust bootstrap densities for dynamic conditional correlations: implications for portfolio selection and Value-at-Risk

ABSTRACT

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions depend on the forecasts covariance matrix little is known about effects of outliers on the uncertainty associated with these forecasts. In this paper we analyse these effects on the context of dynamic conditional correlation models when the uncertainty is measured using bootstrap methods. We also propose a bootstrap procedure to obtain forecast densities for return, volatilities, conditional correlation and Value-at-Risk that is robust to outliers. The results are illustrated with simulated and real data.     

Forecasting interest rates: a comparative assessment of some second-generation nonlinear models

Modeling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary methods such as ARMA and VAR, but only with moderate success. We examine here three methods, which account for several specific features of the real world asset prices such as nonstationarity and nonlinearity. Our three candidate methods are based, respectively, on a combined wavelet artificial neural network (WANN) analysis, a mixed spectrum (MS) analysis and nonlinear ARMA models with Fourier coefficients (FNLARMA). These models are applied to weekly data on interest rates in India and their forecasting performance is evaluated vis-à-vis three GARCH models [GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)] as well as the random walk model. Both the WANN and MS methods show marked improvement over other benchmark models, and may thus hold out several potentials for real world modeling and forecasting of financial data.     

Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo (MCMC) sampling algorithm. Candidate draws for the unobserved volatilities are obtained in blocks by applying the Kalman filter and simulation smoother to a linearization of a nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws via the specification of a bivariate measurement equation, with implied Black–Scholes volatilities used to proxy observed option prices in the candidate model. Alternative models nested within the Heston (1993) framework are ranked via posterior odds ratios, as well as via fit, predictive and hedging performance. The method is illustrated using Australian News Corporation spot and option price data.     

Robust bootstrap forecast densities for GARCH returns and volatilities

Bootstrap procedures are useful to obtain forecast densities for both returns and volatilities in the context of generalized autoregressive conditional heteroscedasticity models. In this paper, we analyse the effect of additive outliers on the finite sample properties of these bootstrap densities and show that, when obtained using maximum likelihood estimates of the parameters and standard filters for the volatilities, they are badly affected with dramatic consequences on the estimation of Value-at-Risk. We propose constructing bootstrap densities for returns and volatilities using a robust parameter estimator based on variance targeting implemented together with an adequate modification of the volatility filter. We show that the performance of the proposed procedure is adequate when compared with available robust alternatives. The results are illustrated with both simulated and real data.     

The coefficient of variation asymptotic distribution in the case of non-iid random variables

Due to the widespread use of the coefficient of variation in empirical finance, we derive its asymptotic sampling distribution in the case of non-iid random variables to deal with autocorrelation and/or conditional heteroskedasticity stylized facts of financial returns. We also propose statistical tests for the comparison of two coefficients of variation based on asymptotic normality and studentized time-series bootstrap. In an illustrative example, we analyze the monthly return volatility of six stock market indexes during the years 1990–2007.