不同股市行情下的星期效应研究——基于沪深股市的实证检验

本文基于我国股票市场中一波最长的熊市和牛市行情,运用GARCH模型研究不同行情下的星期效应。研究发现,整个样本区间内星期效应并不显著,但在熊市和牛市子样本中却分别存在明显的星期效应,具体表现：熊市行情下,最高收益率出现在周二,最低收益率出现在周一;牛市行情下,最高收益率出现在周一,最低收益率出现在周四,且结论具有显著性。因此,按行情对股票市场进行星期效应研究,可有效克服总体样本所得结论的模糊性。     

Liquidity Provision of Limit Order Trading in the Futures Market Under Bull and Bear Markets

Abstract:  This study investigates how limit orders affect liquidity in a purely order-driven futures market. Additionally, the possible asymmetric relationship between market depth and transitory volatility in bull and bear markets and the effect of institutional trading on liquidity provision behavior are examined as well. The empirical results demonstrate that subsequent market depth increases as transient volatility increases in bull markets. Market depth exhibits significantly positive relationship to subsequent transient volatility in bull markets. Additionally, although trading volume positively influences transient volatility in bull markets, no such relationship exists in bear markets. Liquidity provision decreases when institutional trading activity intensifies during bear markets. Thus, liquidity provision for limit orders differs between bull and bear markets.     

An Unobserved Components Model that Yields Business and Medium‐Run Cycles

We generalize the unobserved components (UC) model to allow the permanent component to have different dynamics than the transitory components when decomposing U.S. economic activity using a multivariate UC model of (log) output, consumption, and investment. We find that these proposed dynamics in the permanent component are statistically significant and distinct from those of the transitory components. Our approach provides an alternative explanation for the growth cycles identified by Comin and Gertler ( 2006 ) that is related to the cyclical movements in technology, in a framework consistent with the Beveridge and Nelson ( 1981 ) decomposition.     

我国保险资金与资本市场相互关系实证研究

本文选择资本市场和保险资金市场具有代表性的时间序列数据进行实证研究,分析了我国保险资金与资本市场之间的Granger因果关系、长期均衡关系和短期波动情况,并结合实证研究的结果对我国保险资金投资运用和资本市场的发展提出了相关的建议。     

金融机构信贷扩张、资产价格波动与金融危机——基于资产负债表分析模型

金融危机具有周期性特征,通常在金融危机爆发前后会发生显著的资产价格剧烈波动。由于银行中介信贷周期与宏观经济周期的同周期性,金融危机爆发前的信贷扩张与资产价格泡沫积累掩盖了金融机构的系统性风险问题;市场高涨往往伴随着金融自由化思潮、道德风险问题与实质性监管松弛。基于对金融中介机构资产负债表量化模型的构建与分析,应从资本充足率、金融资产计量属性、坏账拔备比率三个维度采取逆周期金融监管策略,以降低金融危机发生的概率。     

Is it one break or ongoing permanent shocks that explains U.S. real GDP?

The relative importance of permanent versus cyclical shocks to GDP has been found to depend on the presence or absence of a single break in mean growth. We estimate unobserved components models conditional on a trend break having occurred in any specified quarter and use the Bayesian model averaging to combine the conditional estimates. We estimate a break occurred around 2006:1. Allowing for a break significantly reduces estimates of trend variance. However, enough spread remains in the posterior distribution to indicate that available data does not definitively settle the question of the relative importance of trend versus cycle.     

Real-time market microstructure analysis: online transaction cost analysis

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [Performance Metrics for Algorithmic Traders, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [J. Risk, 2000, 3(2), 5–39]), a stochastic control (e.g. Guéant et al. [Math. Financ. Econ., 2013, 7(4), 477–507]), an impulse control (see Bouchard et al. [SIAM J. Financ. Math., 2011, 2(1), 404–438]) or a statistical learning (as used in Laruelle et al. [Math. Financ. Econ., 2013, 7(3), 359–403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order (following for instance Cristianini and Shawe-Taylor [An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. have influence, in the sense of predictive power or information defined in Basseville and Nikiforov [Detection of Abrupt Changes: Theory and Application, 1993], Shannon [Bell Syst. Tech. J., 1948, 27, 379–423] and Alkoot and Kittler [Pattern Recogn. Lett., 1999, 20(11), 1361–1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms.     

LME镍、铜期货价格变动的时间序列分析

本文基于2003～2008年伦敦金属交易所（LME）3月镍、铜期货价格的日线数据,运用经典的时间序列R／S分析方法来研究镍、铜期货市场价格的非线性特征。分析结果显示：LME镍、铜期货市场价格波动是典型的有偏随机游动,H值均大于0．5,期货价格时间序列具有持久性趋势;LME镍、铜期货存在大约分别为447天和442天的非周期循环长度。