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。     

The relationship between conflict events and commodity prices in Sudan

Though recent literature uncovers linkages between commodity prices and conflict, the causal direction of the relationship remains ambiguous. We attempt to contribute to this strand of research by studying the dynamic relationship of commodity prices and the onsets of conflict events in Sudan. Using monthly data ranging from January 2001 through December 2012, we identify a structural breakpoint in the multivariate time series model of prices of the three staple foods (sorghum, millet, and wheat) and conflict measure (number of conflict events) in September of 2011. Applying structural vector autoregression (SVAR) and linear non-Gaussian acyclic model (LiNGAM), we find that wheat price fluctuation is a root cause of conflict events in Sudan. We recommend several policy and programmatic suggestions structured toward production, subsidy, price regulation and support for rural farmers and consumers to stabilize commodity prices.     

Multivariate Return Decomposition: Theory and Implications

In this paper, we propose a model based on multivariate decomposition of multiplicative – absolute values and signs – components of asset returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed method.     

Robust block bootstrap panel predictability tests*

This article develops two block bootstrap-based panel predictability test procedures that are valid under very general conditions. Some of the allowable features include cross-sectional dependence, heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. While the first test procedure tests if there is any predictability at all, the second procedure determines the units for which predictability holds in case of a rejection by the first. A weak unit root framework is adopted to allow persistent predictors, and a novel theory is developed to establish asymptotic validity of the proposed bootstrap. Simulations are used to evaluate the performance of our tests in small samples, and their implementation is illustrated through an empirical application to stock returns.     

家族控制程度对上市公司股价情绪效应的影响研究

上市公司家族控制程度的差异是否会引起投资者对其股票估值的不确定性的变化,这一问题在现有家族企业研究文献中鲜有涉及。基于我国A股家族上市公司样本的2009—2017年度数据,从股票超额收益率对投资者情绪敏感度的角度,分析了家族控制程度对股价情绪效应的影响。研究发现,家族控制程度的增加会加剧上市公司股价的情绪效应,股票超额收益率对投资者情绪的波动更为敏感,而且公司规模越小,账面市值比越低,则其股价情绪效应越明显。股权制衡程度更高的公司,家族控制程度对其股价情绪效应的影响会降低,说明有效的治理结构会减缓股价情绪效应。公司主动性信息披露、代际传承进展,以及公司所处行业的市场竞争程度,均显著地负向调节家族控制程度对股价情绪效应的影响,减缓了收益率对情绪的敏感程度。通过改善这些变量的监管状况,可以有效缓解投资者情绪对家族控制公司股价的冲击,促进股票市场的平稳运行。     

基于协高阶矩视角的沪港股市风险传染分析

金融风险传染(financial risk contagion)不仅是资产配置和套期保值决策中应该考虑的一个重要因素,而且直接关系着一国乃至全球金融体系的安全与稳定.运用协高阶矩(协偏度、协波动率、协峰度等)风险传染判定方法,实证检验了沪港通实施前后上海和香港两地股票市场间的风险传染状况.研究结果表明,沪港通政策实施后,上海股市向香港股市的第一类协偏度(均值→波动率)、协波动率、经调整的相关系数、协峰度等的风险传染统计量值依次变大,同样依据风险传染统计量值的大小,香港股市对上海股市的风险传染途径依次为协四阶矩(协波动率、协峰度)、第一类协偏度和经调整的相关系数,协高阶矩成为沪港股市间双向风险传染的主要途径;因资本净流动数额和投资者教育等因素,上海股市对香港市场的风险传染效应更强.     

Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets

This study analyzed the time–frequency relationship between oil price and exchange rate for Pakistan by using measures of continuous wavelet such as wavelet power, cross-wavelet power, and cross-wavelet coherency (WTC). The results of cross-wavelet analysis indicated that covariance between oil price and exchange rate is unable to give clear-cut results, but both variables have been in phase and out phase (i.e. they are anti-cyclical and cyclical in nature) in some or other durations. However, results of squared wavelet coherence disclose that both variables are out of phase and real exchange rate was leading during the entire period studied, corresponding to the 10–15 months’ scale. These results are the unique contribution of the present study, which would have not been drawn if one would have utilized any other time series or frequency domain-based approach. This finding provides evidence of anti-cyclical relationship between oil price and real effective exchange rate; however, in most of the period studied, real exchange rate was leading and passing anti-cycle effects on oil price shocks which is the major contribution of the study.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.