TESTING FOR CONTINUOUS LOCAL MARTINGALES USING THE CROSSING TREE

We present statistical tests for the continuous martingale hypothesis; that is, for whether an observed process is a continuous local martingale, or equivalently a continuous time‐changed Brownian motion. Our technique is based on the concept of the crossing tree. Simulation experiments are used to assess the power of the tests, which is generally higher than that of recently proposed tests using the estimated quadratic variation (i.e. realized volatility). In particular, the crossing tree shows significantly higher power with shorter data sets. We then show results from applying the methodology to five high‐frequency currency exchange rate data sets from 2003. For four of them we show that at small time‐scales (less than 15 minutes or so) the continuous martingale hypothesis is rejected, but not so at larger time‐scales. For the fifth, the hypothesis is rejected at small time‐scales and at some moderate time‐scales, but not all.     

Who is on the rise in Austria: Wage mobility and mobility risk

In this paper we investigate earnings mobility in Austria from the angle of individual persons. Earnings mobility over time has two aspects: Positional changes and the volatility of earnings over time. Whereas the further is a positive outcome, more volatility as such can be seen as negative. We use Austrian data from tax authorities to find out how population characteristics are related to these two concepts of earnings mobility. Our main results concern initial positions: The higher up you are at the beginning, the more difficult is further positional advancement, moreover, in terms of volatility we see that those in the lowest quantiles of the earnings distribution face the highest volatility of earnings positions over time. *Rudolf Winter-Ebmer is also associated with CEPR, London and IZA, Bonn.     

Regression-based,regression-free and model-free approaches for robust online scale estimation

This paper compares the methods for variability extraction from a univariate time series in real time. The online scale estimation is achieved by applying a robust scale functional to a moving time window. Scale estimators based on the residuals of a preceding regression step are compared with regression-free and model-free techniques in a simulation study and in an application to a real time series. In the presence of level shifts or strong non-linear trends in the signal level, the model-free scale estimators perform especially well. However, the investigated regression-free and regression-based methods have higher breakdown points, they are applicable to data containing temporal correlations, and they are much more efficient.     

More on the volatility-trading volume relationship in emerging markets: The Chinese stock market

This paper empirically investigates the characteristics in terms of volatility and trading volume relationships of the Chinese stock markets, and specifically of the stocks comprising the SSE180 index. Our results show that, contrary to previous evidence, both volatility and trading volume appear to be multi-fractal and highly intermittent, suggesting a common long-run behaviour in addition to the common short-term behaviour underlined by former studies. Moreover, the trading volume seems to have no explanatory power for volatility persistence when introduced in the conditional variance equation. Finally, the sign of the trading volume coefficients is mainly negative, hence showing a negative correlation between the two variables.     

Wavelet semi-parametric inference for long memory in volatility in the presence of a trend

Risk of investing in a financial asset is quantified by functionals of squared returns. Discrete time stochastic volatility (SV) models impose a convenient and practically relevant time series dependence structure on the log-squared returns. Different long-term risk characteristics are postulated by short-memory SV and long-memory SV models. It is therefore important to test which of these two alternatives is suitable for a specific asset. Most standard tests are confounded by deterministic trends. This paper introduces a new, wavelet-based, test of the null hypothesis of short versus long memory in volatility which is robust to deterministic trends. In finite samples, the test performs better than currently available tests which are based on the Fourier transform.     

Value at risk estimation under stochastic volatility models using adaptive PMCMC methods

In this paper, we propose a value-at-risk (VaR) estimation technique based on a new stochastic volatility model with leverage effect, nonconstant conditional mean and jump. In order to estimate the model parameters and latent state variables, we integrate the particle filter and adaptive Markov Chain Monte Carlo (MCMC) algorithms to develop a novel adaptive particle MCMC (A-PMCMC) algorithm. Comprehensive simulation experiments based on three stock indices and two foreign exchange time series show effectiveness of the proposed A-PMCMC algorithm and the VaR estimation technique.     

The Econometrics of Ultra‐high‐frequency Data

Ultra‐high‐frequency data is defined to be a full record of transactions and their associated characteristics. The transaction arrival times and accompanying measures can be analyzed as marked point processes. The ACD point process developed by Engle and Russell (1998) is applied to IBM transactions arrival times to develop semiparametric hazard estimates and conditional intensities. Combining these intensities with a GARCH model of prices produces ultra‐high‐frequency measures of volatility. Both returns and variances are found to be negatively influenced by long durations as suggested by asymmetric information models of market micro‐structure.     

基于四次幂差修正HAR模型的股指期货波动率预测

股指期货波动率建模与预测是揭示其波动运行规律和市场风险是重要途径。本文基于跳跃、好坏波动率与符号跳跃建立四组HAR模型,提出单级纠偏HARQ类模型和多级纠偏HARQF类模型,实证研究揭示股指期货波动运行规律,并采用MCS检验来评估模型优劣。HAR建模考察连续与跳跃波动、好与坏波动率的两种已实现波动分解。为了降低波动率估计偏差,基于最小化MSE准则确定最优抽样频率,利用已实现核修正的ADS检测法识别跳跃,采用已实现核估计修正好坏波动率与符号跳跃。基于沪深300股指期货的实证研究表明：连续波动比跳跃波动对未来已实现波动贡献更大;好坏波动率具有不对称波动冲击,而符号跳跃对未来波动具有负向冲击;好坏波动率分解优于连续与跳跃波动分解;中位数已实现四次幂差能够显著提升HAR类模型的样本内外预测能力;与样本内预测相反,样本外预测中单级纠偏HARQ类模型优于多级纠偏HARQF类模型;MCS检验得出HARQ-RV-SJ模型表现最佳。研究结论与启示对认识股指期货波动规律和市场风险具有意义。     

嵌入GARCH波动率估计的B1ack-Litterman投资组合模型

在B1ack-Litterman投资组合模型中,为了更有效地估计风险资产的期望收益和波动率,引入了投资者的主观观点,这种处理确实能提高均值-方差投资组合模型的性能。但是在实践中,如何度量投资者观点成了另一个难题。为了克服这一困难,我们将GARCH波动率估计嵌入到B1ack-Litterman模型中,通过使用GARCH模型的预测能力来替代投资者主观观点,从而获得一个新的投资决策模型。作为应用,分别考虑了国内外真实市场数据测试情形,通过实证结果发现,嵌入了GARCH波动率估计后,Black-Litterman模型的性能可进一步得到很好提高,样本外平均收益率、波动率和夏普比等指标,均要好于一些传统模型。     

情绪指数与市场收益: 纳入中国波指( iVX) 的分析

本文在 Baker 和 Wurgler( 2006, 2007) 研究框架的基础上, 将中国波动率指数( iVX) 作为一个新的情绪代理变量,结合传统的封闭式基金折价率、股票换手率和 IPOs 的数量等变量,运用主成分分析法构建了中国 A 股市场的情绪指数, 并分析了情绪指数与市场收益之间的依赖关系和预测效果． 研究发现,情绪指数与市场收益呈负向关系． 然而,其当期依赖关系并不显著,而情绪指数对其后第三周的市场收益有较显著的负向预测关系． 中国波指的加入能够明显提高这种预测效果; 相反,IPOs 的数量则并不是一个有效的情绪代理变量． 此外, 采用前两个主成分的加权并不比仅采用第一主成分构建情绪指数在市场收益预测方面表现得更好, 甚至表现得更差． 最后,分析了情绪效应的不对称性,发现正情绪指数对未来收益的影响要远远大于负情绪指数．