最低收购价政策对小麦市场价格波动影响的实证分析

为了估计最低收购价政策对小麦市场价格波动的直接影响,利用2009年1月4日至2016年12月30日小麦现货与期货价格日数据,通过将政策作为虚拟变量引入GARCH模型,实证研究政策对平抑小麦期现货价格波动的作用,并从政策执行区和非执行区的视角比较政策在不同地区维护小麦市场价格稳定方面的效果,对模型结果和价格传导路径进行验证和分析。结果表明,政策对小麦期现货价格波动产生了明显的影响,能够显著降低小麦现货市场的波动程度,并通过期现货市场间的联系对期货市场产生影响,但政策对期货价格波动的作用方向则相反;政策在平抑小麦价格波动方面的效果具有空间上的溢出效应,通过区域间的市场联系从执行区传导到非执行区,导致非执行区价格波动程度的下降。     

上市公司信用风险的KMV模型分析

为研究KMV模型在中国上市公司信用风险定价与度量的适用性问题．选取2004年至2007年260家上市公司的财务指标与股票价格所组成的面板数据,对该模型所揭示的负债资产比、资产波动率和信用风险之间存在的两个内联关系进行实证分析,结果表明：负债资产比与信用风险存在正向关系,资产波动率与信用风险也存在正向关系,得出该模型在对中国上市公司的信用风险定价与度量中具有较高的适用性和精确性的结论。     

时变视角下中美农产品期货波动溢出效应实证

由于金融市场间价格波动溢出效应具有时变性和动态性的特点,采用双变量BEKK模型研究了国内外农产品期货市场间时变波动溢出关系,得出结论：农产品期货的波动均显著地受到自身前期波动的影响,波动具有聚集性和持久性;美国玉米期货与中国玉米期货不存在波动溢出效应,但是美国大豆期货与中国大豆期货、美国豆粕期货与中国豆粕期货之间均存在双向的波动溢出效应。因此,中国大豆期货和豆粕期货在世界农产品期货市场上具有一定的权力,即定价权。     

高频数据瞬时波动率核估计的窗宽选择及算法研究

在瞬时波动率的各种估计量中,非参数估计量因其能准确地度量瞬时波动率,一直是学者们的研究热点。然而,这类估计量在实际应用中都面临着最优窗宽的确定问题。由于最优窗宽中往往携带一些难以估计的未知参数,使得在实际应用过程中确定最优窗宽的具体数值存在困难。本文以瞬时波动率的核估计量为例,借鉴非参数回归分析中窗宽选择的思想,构建了一种能从数据中准确计算出最优窗宽具体值的算法。理论的分析和数值上的验证表明：文中所构建的算法具有良好的稳定性、适应性和收敛速度。算法的提出为瞬时波动率的后续应用研究铺平道路。     

基于Hawkes过程中美股市大幅波动互激效应的研究

近年来,由于中美经济联系日趋紧密,中美股票市场大幅波动的互激效应明显增强。本文考虑中美股市时差和法定节假日差异等因素,运用标值Hawkes过程对2006-2017年CSI300和S&P500大幅波动收益率数据进行建模,结果表明：（1）中美股市大幅波动互激效应存在不对称性,美股市场大幅波动对中国股市的互激效应更强;（2）中美股市大幅波动的幅度对互激效应不存在显著影响;（3）中美股票市场对于大幅波动互激效应的消化速度存在差异,中国股票市场消化美股大幅波动互激效应的速度较快。本研究对金融市场监管者和投资者均有一定意义。     

混频投资者情绪与股票价格行为

采用混频数据抽样模型(MIDAS)研究了混频投资者情绪对中国股市收益率及其波动的影响.通过构建日度、周度及月度这三种不同频率的投资者情绪,实证结果发现,混频情绪对当期收益率及其波动都存在显著的正向影响,并且与传统回归模型相比,MIDAS模型具有更强的解释能力.本文进一步使用GARCH-MIDAS模型研究了混频情绪对收益率波动长期成分的影响,发现混频情绪能够显著影响收益的长期波动.     

Role of stylized features in constructing better estimators

This article discusses the role played by stylized features of financial time series in constructing better estimators for the model parameters. We study in detail one such estimator for the transition probabilities of a simple regime switching model. The estimator is based on the squared autocovariances of the time series, which has been discussed in several empirical studies of economic and financial time series. The effectiveness of this estimator in improving the estimation accuracy is investigated, using both finite sample and asymptotic computations. We also report simulation results to confirm our findings and to extend our conclusions over a bigger region of the parameter space.     

Estimation of structural mean breaks for long-memory data sets

In long-memory data sets such as the realized volatilities of financial assets, a sequential test is developed for the detection of structural mean breaks. The long memory, if any, is adjusted by fitting an HAR (heterogeneous autoregressive) model to the data sets and taking the residuals. Our test consists of applying the sequential test of Bai and Perron [Estimating and testing linear models with multiple structural changes. Econometrica. 1998;66:47–78] to the residuals. The large-sample validity of the proposed test is investigated in terms of the consistency of the estimated number of breaks and the asymptotic null distribution of the proposed test. A finite-sample Monte-Carlo experiment reveals that the proposed test tends to produce an unbiased break time estimate, while the usual sequential test of Bai and Perron tends to produce biased break times in the case of long memory. The experiment also reveals that the proposed test has a more stable size than the Bai and Perron test. The proposed test is applied to two realized volatility data sets of the S&P index and the Korea won-US dollar exchange rate for the past 7 years and finds 2 or 3 breaks, while the Bai and Perron test finds 8 or more breaks.     

Bootstrap forecast intervals for asymmetric volatilities via EGARCH model

Bootstrap forecast intervals are developed for volatilities having asymmetric features, which are accounted for by fitting EGARCH models. A Monte-Carlo simulation compares the proposed forecast intervals with those based on GARCH fittings which ignore asymmetry. The comparison reveals substantial advantage of addressing asymmetry through EGARCH fitting over ignoring it as the conventional GARCH forecast. The EGARCH forecast intervals have empirical coverage probabilities closer to the nominal level and/or have shorter average lengths than the GARCH forecast intervals. The finding is also supported by real dataset analysis of Dow–Jones index and financial times stock exchange (FTSE) 100 index.