Nonparametric Estimation of a Multifactor Heath-Jarrow-Morton Model: An Integrated Approach

We propose a new nonparametric estimator for the volatilitystructure of the zero-coupon yield curve inside the Heath-Jarrow-Mortonframework. The estimator incorporates cross-sectional restrictionsalong the maturity dimension, and also allows for measurementerrors, which can arise from estimation of the yield curve fromnoisy data. The estimates are implemented with daily CRSP bonddata.     

中国A股量价之间动态关系的实证研究

本文采用了VAR模型以及Granger因果检验的方法来考察量价之间的动态相关关系。我们选定1996年12月16日至2008年12月31日作为样本研究区间,实证发现滞后期的交易量和收益对当前期交易量与收益的解释力度存在下降趋势;同时,交易量同收益之间由收益对交易量的单向引导发展为双方互为Granger原因。本文得到的交易模式的动态演变轨迹反映了我国投资者式逐步趋于理性成熟。     

中国A股量价之间动态关系的实证研究

本文采用了VAR模型以及Granger因果检验的方法来考察量价之间的动态相关关系.我们选定1996年12月16日至2008年12月31日作为样本研究区间,实证发现滞后期的交易量和收益对当前期交易量与收益的解释力度存在下降趋势;同时,交易量同收益之间由收益对交易量的单向引导发展为双方互为Granger原因.本文得到的交易模式的动态演变轨迹反映了我国投资者式逐步趋于理性成熟.     

Nonparametric Estimation of State-Price Densities Implicit in Financial Asset Prices

Implicit in the prices of traded financial assets are Arrow–Debreu prices or, with continuous states, the state-price density (SPD). We construct a nonparametric estimator for the SPD implicit in option prices and we derive its asymptotic sampling theory. This estimator provides an arbitrage-free method of pricing new, complex, or illiquid securities while capturing those features of the data that are most relevant from an asset-pricing perspective, for example, negative skewness and excess kurtosis for asset returns, and volatility "smiles" for option prices. We perform Monte Carlo experiments and extract the SPD from actual S&P 500 option prices.     

股票价格"虚拟"问题的研究

从现代市场经济的视角看,股票作为一种能够为投资者带来一定收入的资本所有权证书,是最典型的虚拟资本形式。作为虚拟资本的股票价格不外乎是一种与利息率相关的股息收入的资本化。由于利息率是股票价格一个决定性因素,致使股票价格与利息率相关性极强,而与其净资产的高低相关性较弱。因此,在证券市场上,股票价格往往高于其真实价格(每股净资产价值),从而具有了“虚拟”成分。这样,股票价格虚拟运动便成为一种独特的经济范畴。为此,只有注重对股票价格虚拟的合理范围的判断,确定出股票价格可投资的界限,才能以价值投资的理念,引导市场的投资行为,促进中国股市健康发展。     

The Impact of Information Release on Stock Price Volatility and Trading Volume: The Rights Offering Case

This paper examines whether information released via rights offering announcements induces changes in price volatility and trading volume of underlying stock. The results of this paper provide support for the release of new information via offering announcements and evidence of its effects on price volatility and volume of underlying stock. Specifically, utilization of the announced information by investors is evidenced by greater trading volume following the announcement date than during the pre-announcement period. We interpret this result to mean that informedness dominates consensus. However, stock price volatility decreased from the pre-announcement period to the post-expiration period of rights offerings.     

Drift Estimation of Generalized Security Price Processes from High Frequency Derivative Prices

This paper presents a framework for using high frequency derivative prices to estimate the drift of generalized security price processes. This work may be seen more generally as a quasi-likelihood approach to estimating continuous-time parameters of derivative pricing models using discrete option data. We develop a generalized derivative-based estimator for the drift where the underlying security price process follows any arbitrary state-time separable diffusion process (including arithmetic and geometric Brownian motion as special cases). The framework provides a method to measure premia in derivative prices, test for risk-neutral pricing and leads to a new empirical approach to pricing derivative contingent claims. A sufficient condition for the asymptotic consistency of the generalized estimator is also obtained. A study based on generating the S&P500 index and calls shows that the estimator can correctly estimate the drift parameter. This revised version was published online in November 2006 with corrections to the Cover Date.     

Aggregation of Nonparametric Estimators for Volatility Matrix

An aggregated method of nonparametric estimators based on time-domainand state-domain estimators is proposed and studied. To attenuatethe curse of dimensionality, we propose a factor modeling strategy.We first investigate the asymptotic behavior of nonparametricestimators of the volatility matrix in the time domain and inthe state domain. Asymptotic normality is separately establishedfor nonparametric estimators in the time domain and state domain.These two estimators are asymptotically independent. Hence,they can be combined, through a dynamic weighting scheme, toimprove the efficiency of volatility matrix estimation. Theoptimal dynamic weights are derived, and it is shown that theaggregated estimator uniformly dominates volatility matrix estimatorsusing time-domain or state-domain smoothing alone. A simulationstudy, based on an essentially affine model for the term structure,is conducted, and it demonstrates convincingly that the newlyproposed procedure outperforms both time- and state-domain estimators.Empirical studies further endorse the advantages of our aggregatedmethod.     

Option Volume and Stock Prices: Evidence on Where Informed Traders Trade

This paper investigates the informational role of transactions volume in options markets. We develop an asymmetric information model in which informed traders may trade in option or equity markets. We show conditions under which informed traders trade options, and we investigate the implications of this for the linkage between markets. Our model predicts an important informational role for the volume of particular types of option trades. We empirically test our model's hypotheses with intraday option data. Our main empirical result is that negative and positive option volumes contain information about future stock prices.     

IFRS Adoption and Stock Prices of Japanese Firms in Governance System Transition

This study examines firms' decision to voluntarily adopt IFRS in a setting where there are changes to the governance system in a traditionally code law country, as well as how the market responds to such decisions. We find the probability of voluntary IFRS adoption to be higher among firms that have a high proportion of foreign shareholders, undertake quality audits, have low levels of leverage, feature a nominating committee, and are included in the new market index. In addition, the stock prices of IFRS adopters tend to increase around the announcement date of IFRS adoption, compared to those of non-adopters. Finally, market reactions are smaller for firms that feature a nominating committee, and are included in the new market index—perhaps because IFRS adoption by these firms is less surprising to market participants, and because IFRS adoption is not expected to add large incremental value to these firms.     

The Effect of Tick Size on Price Clustering and Trading Volume

Proposals have been made for some stock exchanges to reduce the size of their trading tick in order to lower transactions costs and, as a result, attract more trading volume and firm listings. We investigate the impact of tick size on price clustering and trading volume when the minimum price change varies with price level. Controlling the firm specific variables, we find that a smaller trading tick tends to exacerbate price clustering. Furthermore, a reduction in tick size is more likely to increase trading volume if the shares are heavily traded. These results suggest that previous studies on other stock markets may have overstated the benefits of a smaller trading tick to traders.     

利率与股价之间的价格溢出效应与波动溢出效应

基于具有外生变量的二元VAR-MGARCH模型对中国货币市场利率和股价之间的关联进行了理论分析和实证研究。结果表明,利率和股价之间基本不存在价格溢出效应;货币市场利率和股价序列均表现出时变方差的特征和波动的持久性特征,货币市场和股市之间存在双向波动溢出效应;货币供给的正向冲击对利率的影响是正向的。     

Fundamental Real Estate Prices: An Empirical Estimation with International Data

We propose two alternative models to estimate fundamental prices on real estate markets. The first model is based on a no-arbitrage condition between renting and buying. The second model interprets the period costs as the result of market equilibrium between housing demand and supply. We estimate both models for the USA, the UK, Japan, Switzerland, and the Netherlands. We find that observed prices deviate substantially and for long periods from their estimated fundamental values. However, we find some evidence that, in the long-run, actual prices tend to return to their fundamental values progressively. This result is due to both impulse–response functions and forecast analyses. In particular, we find that using the fundamental price significantly increases the accuracy of out-of-sample long-term forecasts of the price.

投资者分歧、异常交易量和股票横截面收益率预测——基于中国股票市场的经验证据

本文定义月度异常交易量为本月与上个月交易金额的比值,发现中国市场月度收益率与滞后一个月的异常交易量显著负相关。在控制了公司规模、账面市值比、流动性以及动量效应等指标后仍然具有显著的解释作用。进一步研究表明,在出现高异常交易量后的12个月内,换手率和特质性波动率都有大幅上升。本文认为,交易量上升代表着市场分歧程度和受关注程度的增加,在卖空约束下会使得股票价值高估,从而造成未来收益率下降。     

Testing the bivariate mixture hypothesis using German Stock market data

According to the bivariate mixture hypothesis (BMH) as proposed by Tauchen and Pitts (1983) and Harris (1986, 1987) the daily price changes and the corresponding trading volume on speculative markets follow a joint mixture of distributions with the unobservable number of daily information events serving as the mixing variable. Using German stock market data of 15 major companies the distributional properties of the BMH is tested employing maximum-likelihood as well as generalised method of moments estimation techniques. In addition to providing a new approach for the pointwise estimation of the latent information arrival rate based on the maximum-likelihood method, we investigate the time-series properties of the BMH. the major results can be summarised as follows: (i) the distributional characteristics of the data (especially leptokurtosis and skewness in the distribution of price changes and volume respectively) cannot be explained satisfactorily by the BMH; univariate mixture models for price changes and trading volume separately reveal a possible specification error in the model; (ii) a univariate normal mixture model can account for the observed distributional characteristics of price changes; (iii) the estimated process of the latent information rate cannot fully explain the time-series characteristics of the data (especially the volatility clustering or ARCH-effects).     

Robust Estimation of Beta Coefficients: Evidence from a Small Stock Market

In this paper we demonstrate that robust estimators improve the reliability of estimates of beta coefficients on small, thinly traded stock markets. We outline several different types of robust and bounded influence regression estimators and assess them using a jackknife methodology on data from the Johannesburg Stock Exchange. The empirical evidence confirms the hypothesis that robust estimators are more efficient than least squares estimators and indicates that least squares estimators may over-estimate systematic risk in some cases.     

论《非参数统计》精品课程建设

非参数统计是应用统计学的重要分支、统计学专业的主干课程。1994年在内蒙古财经学院开课以来,围绕着学科建设、专业建设而展开的课程建设已持续多年,将该课程建设成为内蒙古自治区级精品课程是其奋斗目标。从教学队伍、教学内容、教材、实验、机制以及教学方法和手段等方面出发,总结了统计学系非参数统计课程多年的建设经验,提出了精品课程建设的核心是解决好课程内容建设问题、而课程资源建成后的共享与应用是关键点和落脚点的观点。     

Nonparametric Estimation of Expected Shortfall

The expected shortfall is an increasingly popular risk measurein financial risk management and it possesses the desired sub-additivityproperty, which is lacking for the value at risk (VaR). We considertwo nonparametric expected shortfall estimators for dependentfinancial losses. One is a sample average of excessive losseslarger than a VaR. The other is a kernel smoothed version ofthe first estimator (Scaillet, 2004 Mathematical Finance), hopingthat more accurate estimation can be achieved by smoothing.Our analysis reveals that the extra kernel smoothing does notproduce more accurate estimation of the shortfall. This is differentfrom the estimation of the VaR where smoothing has been shownto produce reduction in both the variance and the mean squareerror of estimation. Therefore, the simpler ES estimator basedon the sample average of excessive losses is attractive forthe shortfall estimation.     

Interest Rate Surprises and Stock Prices

This paper examines the impact of unexpected changes in the federal funds target on stock prices from 1988 to 2001. Measures of interest rate surprises are constructed from survey data and changes in the 3-month T-bill yield. I find that surprises associated with decreases in the target cause stock prices to rise significantly. Surprises associated with increases in the target increase stock market volatility on the announcement day, with volatility reverting to pre-surprise levels on the day after the announcement. This volatility pattern is only evident since 1994. An implication is that concerns about immediate disclosure causing persistent and heightened stock market volatility might be misplaced.