金融资产收益率波动预测——基于我国股票市场跳跃行为研究

金融资产收益率波动是资产定价和金融风险管理的核心部分,而跳跃是收益率波动中的重要组成部分。基于修正Z-检验,本文检测识别我国股市波动中跳跃行为,并且研究了跳跃的时序特征,统计结果表明,在市场大波动时期,和连续成份相比,跳跃对于波动率具有极其重要的贡献。建立包含跳跃的已实现波动率非齐次自回归模型,在波动模型中纳入滞后绝对日收益率和杠杆效应预测股指收益率波动。实证分析结果显示,对于短期的波动预测,包含跳跃和两种影响因素的波动模型表现最好,然而对于提前1月的长期预测,跳跃和连续波动成份分离模型预测明显优于其它模型,这些事实说明跳跃对股指波动率预测具有重要的影响,好坏消息对波动率非对称性具有短期显著影响,而对长期水平的波动率预测影响不显著。     

Forecasting the realized volatility in the Chinese stock market: further evidence

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.     

Analysis of High Frequency Data in Finance: A Survey

This study examines the use of high frequency data in finance, including volatility estimation and jump tests. High frequency data allows the construction of model-free volatility measures for asset returns. Realized variance is a consistent estimator of quadratic variation under mild regularity conditions. Other variation concepts, such as power variation and bipower variation, are useful and important for analyzing high frequency data when jumps are present. High frequency data can also be used to test jumps in asset prices. We discuss three jump tests: bipower variation test, power variation test, and variance swap test in this study. The presence of market microstructure noise complicates the analysis of high frequency data. The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise. Finally, some applications of jump tests in asset pricing are discussed in this article.     

Modeling and forecasting return jumps using realized variation measures

This paper proposes a simple HAR-RV-based model to predict return jumps through a conditional density of jump size with time-varying moments. We model jump occurrences based on a version of the autoregressive conditional hazard model that relies on past continuous realized volatilities. Applying our methodology to seven equity indices on the U.S. and Chinese stock markets, we reach the following key findings: (i) jump occurrence and size are dependent on past realized volatility, (ii) the proposed model yields superior in- and out-of-sample jump size density forecasts compared to an ARMA(1,1)-GARCH(1,1) model, (iii) and the occurrence and sign of return jumps are predictable to some extent.     

Jumps and volatility dynamics in agricultural commodity spot prices

The spot commodities market exhibits both extreme volatility and price spikes, which lead to heavy-tailed distributions of price change and autocorrelation. This article uses various Lévy jump models to capture these features in a panel of agricultural commodities observed between January 1990 and February 2014. The results show that Levy jump models outperform the continuous Gaussian model. Our results prove that assuming a constant volatility or even a deterministic volatility and drift structure of agricultural commodity spot prices is not realistic and is less efficient than the stochastic assumption. The findings demonstrate an interesting correlation between volatility and jumps for a given commodity i, but no relationship between the volatility of commodity i and the probability of jumps of commodity j.     

A correlated bivariate Poisson jump model for foreign exchange

This paper develops a new bivariate jump model to study jump dynamics in foreign exchange returns. The model extends a multivariate GARCH parameterization to include a bivariate correlated jump process. The conditional covariance matrix has the Baba, Engle, Kraft, and Kroner (1989) structure, while the bivariate jumps are governed by a Correlated Bivariate Poisson (CBP) function. Using daily data we find evidence of both independent currency specific jumps, as well as jumps common to both exchange rates of the Canadian dollar and Japanese Yen against the U.S. dollar. The paper concludes by investigating a time-varying structure for the arrival of jumps that relaxes the assumption of constant and bounded jump correlation imposed by the CBP function.I am indebted to two anonymous referees and the editor, Baldev Raj for helpful suggestions. I am also grateful for helpful comments from Adolf Buse, Ramazan Gencay, Rehim Kilic, John Maheu, Alex Maynard, Denis Pelletier, Denise Young, and seminar participants at the Tenth Annual Symposium of the Society for Nonlinear Dynamics and Econometrics (SNDE), Federal Reserve Bank of Atlanta 2002; the Midwest Econometrics Group (MEG) Meetings, Federal Reserve Bank of Kansas City 2001; Canadian Economics Association (CEA) Meetings, McGill University 2001.     

Bootstrap methods for autocorrelation test with uncorrelated but not independent errors

By using bootstrap technique we investigate the properties of the Breusch [Breusch, T.S., 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers 17, 334–355]–Godfrey [Godfrey, L.G., 1978. Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables. Econometrica 46, 1303–1310] autocorrelation tests in dynamic models with uncorrelated but not independent errors. In this paper we show that, under conditions when the errors are uncorrelated but not independent, even the best likelihood ratio test cannot achieve the asymptotic distribution under the null hypothesis of no autocorrelation. Standard bootstrap methods also fail to produce consistent results. To overcome this problem we applied several bootstrap testing methods for the same purpose and found the stationary bootstrap and Wild bootstrap with static model to perform adequately among the other bootstrap methods.     

Do illiquid stocks jump more frequently?

We study the influence of stock liquidity on the stock price jump frequency using intraday data of 175 US stocks during 2007–11. Grouping these stocks according to their average liquidity we find less liquid stocks to jump more often than liquid stocks. Depending on the liquidity measure the least liquid stocks exhibit on average between 10% and 34% more jumps than the most liquid stocks. Our results are robust to different definitions of liquidity and jump measures as well prevail under different time frequencies.     

一种资产定价过程中跳辨识的新方法

文章主要基于AitSahalia(2002)关于金融数据中跳(Jump)的研究,对跳的性质作进一步的探索并加以推论,同时采用IMSE(InferiorMeanSquaredError)作为分离跳的标准,选择出恰当的(λ,α,Δ)仨,达到辨识金融数据中跳的目的。     

Twenty years of jumps in commodity markets

In this article, we provide statistical evidence around jumps affecting commodity returns. Using nearly 20 years of daily data, we use Laurent, Lecourt, and Palm's (2011) methodology to jump extraction, and discuss various aspects of the estimated jump activity. On average across various commodity markets, we find a high number of days for which returns exhibit the presence of jumps, consistently with the intuition that commodities are affected by large price fluctuations. We emphasize that the post-jump average return depends on the commodity sector considered (e.g. agriculture, energy, or metals). We also show evidence of a jump-to-volatility channel for commodities (similar to the effect usually found for equities). Finally, we diagnose around 40 dates during which commodity indices, stocks, bonds and currencies `co-jump’, revealing a tail dependence between standard and alternative assets.     

股票价格服从不对称跳跃-扩散过程的期权定价模型

经典期权定价公式B—S模型假设股票价格服从连续的几何布朗运动,然而,经验研究表明股票价格常常发生跳跃式的变化,这主要是由于股市上常出现一些重大的事件导致的。通过对期权市场出现的“隐含波动率微笑”现象进行观察和研究,可以发现引起股票价格上升或者下跌的跳跃式变化往往是不对称的。为简单起见,在假设跳跃幅度服从均匀分布假设前提下,初步建立起股票价格服从不对称跳跃-扩散过程期权定价模型。     

基于二维跳扩散模型的股市相关性研究

本文分析了在扩散模型中引入跳部分描述资产价格异动的经济逻辑,并进一步细化跳产生的经济原因,引入具有相关性的多维泊松过程描述不同股票市场价格的异常波动次数,推广了现有的多维跳扩散模型。并应用推广后的模型,以中国A股市场和中国香港股票市场指数为样本,分析两个市场异动的相关性,同时结合新闻数据赋予统计结果以经济解释。     

An empirical analysis of the downside risk-return trade-off at daily frequency

This paper considers the downside-risk aversion of investors as an explanation for the risk-return trade-off. We test empirically this hypothesis using intraday data along with the recent measure of downside-risk called realized semivariance developed in Barndorff-Nielsen et al. (2010). The empirical analysis over the period 1996–2008 provides evidence of a significant relation between semivariance and excess returns at the daily frequency. To gain better understanding of the relation between returns and downside-risk, we investigate the statistical relation between a new measure of conditional asymmetry, namely the ratio of the downside realized semivariance over the variance, and obtain a revealing pattern using a rolling window framework able to link asymmetry in the distribution to future returns. In particular, the asymmetry measure becomes significant when the past realized variance is not significant any more thereby providing insights about a possible change in the behavior of investors.     

Testing for parameter restrictions in a stationary VAR model: A bootstrap alternative

This paper proposes the use of the bootstrap when the system Wald test is employed to test for linear restrictions in a stationary vector autoregressive (VAR) model. The bootstrap test is conducted using the estimated generalised least square estimator for VAR parameters, which considers contemporaneous correlations among the error terms. It is found that the bootstrap test shows little size distortion in small samples. In contrast, the asymptotic Wald test exhibits serious size distortion, severely over-rejecting the true null hypothesis in small samples. The bootstrap test also has desirable power properties, with its power particularly high when the model is near non-stationary and the error terms are highly correlated contemporaneously. As an application, the bootstrap Wald test is employed to test for the predictability of stock return from dividend yield using U.S. data.     

中国保险业全要素生产率研究

基于非参数的Malmquist指数方法,本文对我国1999～2006年间保险业的全要素生产率变化状况进行了动态分析,针对众多的研究方法都存在决策单元规模报酬不变假定,以及决策单元向生产前沿面逼近路径只能选择径向,即仅考虑投入或产出变化情况的缺陷,本文提出修正的Malmquist指数模型.实证发现,中国保险业全要素生产率在1999～2006年问有所提高,其中"追赶效应"较为明显而"增长效应"还未完全显现,这说明保险机构更加注重提高自身技术效率水平而非创新能力提高生产的有效性,依然没有处理好效率进步与技术进步的关系,保险机构需要进一步提升技术创新的能力.     

Moran's I test of spatial panel data model — Based on bootstrap method

Under the condition of the finite sample or the unknown distributed error term, testing for spatial dependence in panel data models is an unresolved problem in spatial econometrics. In this paper, a fast double bootstrap (FDB) method is used to construct bootstrap Moran's I tests for Moran's I test in spatial panel data models, and Monte Carlo simulation experiments are used to prove the effectiveness from two aspects including size distortion and power. The experiment results show that, in asymptotic Moran's I test, there is serious size distortion, which could be rectified in bootstrap Moran's I test.     

Foreign equity option pricing under stochastic volatility model with double jumps

This paper considers the challenging problem advocated by Huang and Hung (2005), that is to incorporate the stochastic volatility into the foreign equity option pricing. Foreign equity options (quanto options) are contingent claims where the payoff is determined by an equity in one currency but the actual payoff is done in another currency. Huang and Hung (2005) priced foreign equity options under the Lévy processes. In Huang and Hung's paper, they considered jumps in the foreign asset prices and exchange rates and assumed the volatility as constant. However, many studies showed that constant volatility and jumps in returns are incapable of fully capturing the empirical features of equity returns or option prices. In this paper, the stochastic volatility with simultaneous jumps in prices and volatility is proposed to model foreign asset prices and exchange rates. The foreign equity option pricing formula is given by using the Fourier inverse transformation. The numerical results show that the use of stochastic volatility with simultaneous jumps in prices and volatility proposed to model foreign asset prices and exchange rates is necessary and this approach can help us to capture more accurately the foreign equity option prices.     

NONPARAMETRIC BOOTSTRAP PROCEDURES FOR PREDICTIVE INFERENCE BASED ON RECURSIVE ESTIMATION SCHEMES*

We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out‐of‐sample nonlinear Granger causality, and in the other we outline a test for selecting among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (Journal of Applied Econometrics 14 (1999), 491–510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.     

Correlated jumps in crude oil and gasoline during the Gulf War

This article employs a bivariate poisson jump model to investigate the relationship between the volatility of crude oil and gasoline especially during the period of the Gulf War. We find that greater jumps occurring in crude oil returns will appear in gasoline returns at the same time, but the magnitude of the co-movements in volatility falls. The covariance is relatively smaller in the Second Gulf War vs. the first conflict. The volatility of crude oil is of significantly high levels during periods of the war, yet the volatility of gasoline is not as sensitive as crude oil, particularly in the second conflict. Furthermore, the jump that occurred by the war did not lead both spot prices to a high persistent level for a long period, which fits the feature of the jump models. All these findings are important to market traders and hedging strategies.     

Are EU budget deficits stationary?

In this paper, we test for the stationarity of European Union budget deficits over the period 1971–2006, using a panel of thirteen member countries. Our testing strategy addresses two key concerns with regard to unit root panel data testing, namely (1) the presence of cross-sectional dependence among the countries in the panel and (2) the identification of potential structural breaks that might have occurred at different points in time. To address these concerns, we employ an AR-based bootstrap approach that allows us to test the null hypothesis of joint stationarity with endogenously determined structural breaks. In contrast to the existing literature, we find that the EU countries considered are characterised by fiscal stationarity over the full sample period irrespective of us allowing for structural breaks. This conclusion also holds when analysing sub-periods based on before and after the Maastricht treaty.