基于高频数据的波动率预测研究

基于高频数据的金融分析与建模研究目前已成为金融工程研究领域的一大热点.在金融资产价格波动率的刻画上,金融高频波动率有着低频波动率无法比拟的信息优势,能够较为准确地刻画金融市场波动率的相关特征,并对金融市场波动率的变化做出较为精确的预测.本文选择基于高频数据的沪深300指数为样本,通过构建已实现波动率和已实现极差的长记忆性模型去研究高频数据建模预测中的方法,以对比研究的形式分析了已实现波动率和已实现极差在波动率预测中的能力大小,为高频数据波动率预测研究提供了参考和借鉴.     

基于高频数据的波动率预测研究

基于高频数据的金融分析与建模研究目前已成为金融工程研究领域的一大热点。在金融资产价格波动率的刻画上,金融高频波动率有着低频波动率无法比拟的信息优势,能够较为准确地刻画金融市场波动率的相关特征,并对金融市场波动率的变化做出较为精确的预测。本文选择基于高频数据的沪深300指数为样本,通过构建已实现波动率和已实现极差的长记忆性模型去研究高频数据建模预测中的方法,以对比研究的形式分析了已实现波动率和已实现极差在波动率预测中的能力大小,为高频数据波动率预测研究提供了参考和借鉴。     

权证隐含波动率在标的证券波动率预测中的作用研究

资产收益的波动是投资者投资决策的主要依据.本文选取了葛州和长虹等七只权证作为样本.首先应用单位根检验,验证各样本历史波动率和隐含波动率序列的平稳性,在此基础上检验各样本两种波动率序列的协整关系.最后,对隐含波动率所包含的额外信息进行探讨.结果表明,已实现波动率和隐含波动率基本上呈现单位根状态,并且两者之问基本不存在协整关系,权证的隐含波动率确实拥有额外的信息.投资者在实际运作中,可以加入隐含波动率来提高对实际波动率预测的准确性.     

基于高频数据的金融市场波动溢出分析

在讨论"已实现"波动率、"已实现"协方差基础上,针对金融市场的高频数据,引入"已实现"波动变结构,分阶段计算"已实现"波动率的相关系数,检验"已实现"波动率相关系数,判断在变结构点前后是否发生显著变化,从而分析金融市场之间的波动溢出效应,并进行实证分析。     

基于SEMIFAR模型的我国股市波动率的长记忆性研究

以深圳股票市场1997年1月1日至2011年10月10日深证成分指数行情数据为样本,采用SEMIFAR模型,研究中国股票市场波动率的长记忆特性。首先,对长记忆的统计检验进行计量分析,研究发现对数日波动率序列衰减缓慢并在滞后200阶的情况下依然显著,这表明我国股票市场波动率序列具有长记忆性。紧接着,尝试使用SEMIFAR模型对日波动率序列进行建模和预测,结果发现SEMIFAR模型在对数日波动率序列长记忆建模中效果很好。     

高频环境下金融资产收益波动率研究的新进展

金融资产收益的波动率估计和预测是金融风险管理、金融衍生品定价以及投资组合选择中一个非常重要的核心环节,随着高频金融分时数据的广泛采集,高频已实现波动率的方法开始流行,以此为基础,金融资产收益波动率的估计、建模和预测研究大为拓展。从高频已实现波动率的估计、特征、预测模型这几个方面对国内外主要学术文献的研究成果进行综述,期望为该主题的深入研究提供一定的线索。     

带高频信息及交互效应的波动率模型

本文利用股票市场的高频数据波动率预测,采用隔夜波动率和交易时段波动率预测模型,其中,隔夜波动率模型考虑了周末效应对波动率的影响,在交易时段波动率模型中,"已实现波动率"采用基于周平均收益率的函数系数形式,以考察短期收益与高频信息的交互影响,建立了函数系数GARCH模型。基于上证综指的实证分析显示,隔夜波动率存在明显的周末效应,交易时段波动率"杠杆效应"显著,短期收益与高频信息存在显著的非线性交互作用。     

EGARCH-SN模型及其实证研究

首先研究了以往GARCH模型对误差项的各种选择方法,并基于Sahu等(2003)和Branco、Dey(2001)等对偏正态分布的研究,提出了EGARCH(1,1)-SN模型;该模型能同时考虑收益序列的"有偏、尖峰和肥尾"特性以及正负新息非对称冲击的杠杆效应,是理论上较为理想的波动模型。选用沪深A股1996-2005年日收益率数据对模型进行了检验,结果发现:EGARCH(1,1)-SN模型对沪深两市收益波动的拟合效果很好;同时",波动序列非对称性"比"收益序列非对称性"更为重要;正负新息均具有增大后期波动之趋势,但负新息对后期波动的影响更大。     

投资者情绪、期权隐含信息与股市波动率预测——基于上证50ETF期权的经验研究

在异质自回归模型(HAR-RV)中引入中国上证50ETF期权隐含信息和投资者情绪,本文分别对中国股票市场未来日、周和月波动率进行预测。研究发现,期权隐含信息和投资者情绪能够提高HAR-RV模型对股票市场未来波动率的预测效果。投资者情绪对未来波动率的影响存在两种机制:在情绪高涨期间,月已实现波动率与未来波动率正相关,说明以个人投资者占主体所引起的价格信息机制,在中国股票市场交易中占主导作用;风险中性偏度与未来波动率负相关,说明以个人投资者占主体所引起的噪声交易机制占主导作用。     

基于GARCH模型的欧元／美元汇率的预测

本文对今年来欧元兑美元的日收盘价进行数据分析．发现欧元／美元汇率日波动不服从正态分布。而且汇率的时间序列有波动异方差性,根据近年来欧元／美元的汇率数据特征．建立欧元／美元汇率的GAR．CH（1,1）预测模型,实证分析所建模型的拟合度较高,适应做短期预测。     

Implications of market microstructure for realized variance measurement

Volatility measuring and estimation based on intra-day high-frequency data has grown in popularity during the last few years. A significant part of the research uses volatility and variance measures based on the sum of squared high-frequency returns. These volatility measures, introduced and mathematically justified in a series of papers by Andersen et al. [1999. (Understanding, optimizing, using and forecasting) realized volatility and correlation. Leonard N. Stern School Finance Department Working Paper Series, 99-061, New York University; 2000a. The distribution of realized exchange rate volatility. Journal of the American Statistical Association 96, no. 453: 42–55; 2000b. Exchange rate returns standardized by realized volatility are (nearly) Gaussian. Multinational Finance Journal 4, no. 3/4: 159–179; 2003. Modeling and forecasting realized volatility. NBER Working Paper Series 8160.] and Andersen et al. 2001a. Modeling and forecasting realized volatility. NBER Working Paper Series 8160., are referred to as ‘realized variance’. From the theory of quadratic variations of diffusions, it is possible to show that realized variance measures, based on sufficiently frequently sampled returns, are error-free volatility estimates. Our objective here is to examine realized variance measures, where well-documented market microstructure effects, such as return autocorrelation and volatility clustering, are included in the return generating process. Our findings are that the use of squared returns as a measure for realized variance will lead to estimation errors on sampling frequencies adopted in the literature. In the case of return autocorrelation, there will be systematic biases. Further, we establish increased standard deviation in the error between measured and real variance as sampling frequency decreases and when volatility is non-constant.     

Comovements in volatility in the euro money market

This paper assesses the sources of volatility persistence in Euro Area money market interest rates and the existence of linkages relating volatility dynamics. The main findings of the study are as follows. Firstly, there is evidence of stationary long memory, of similar degree, in all series. Secondly, there is evidence of fractional cointegration relationships relating all series, except the overnight rate. The common long memory factor analysis points to a two-factor volatility curve. The most important factor, in terms of proportion of total variance explained, can be interpreted as a level factor (64% of total variance), while the other as a slope factor (13% of total variance). Impulse response analysis and forecast error variance decomposition finally point to non significant forward transmission of liquidity shocks.     

On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting

This article documents the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European climate exchange (ECX), which is valid under the EU emissions trading scheme (EU ETS). Realized volatility measures from naive, kernel-based and subsampling estimators are used to obtain inferences about the distributional and dynamic properties of the ECX emissions futures volatility. The distribution of the daily realized volatility in logarithmic form is shown to be close to normal. The mixture-of-normals hypothesis is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi in J Financ Econ 7:174–196, 2009) with only a weekly component, which reproduces long memory properties of the series, is then used to model the volatility dynamics. Finally, the predictive accuracy of the HAR-RV model is tested against GARCH specifications using one-step-ahead forecasts, which confirms the HAR-RV superior ability.     

The one-trading-day-ahead forecast errors of intra-day realized volatility

Two volatility forecasting evaluation measures are considered; the squared one-day-ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts.We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models.     

Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility

Realized measures employing intra-day sources of data have proven effective for dynamic volatility and tail-risk estimation and forecasting. Expected shortfall (ES) is a tail risk measure, now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to implicitly model ES, has been extended to allow the intra-day range, not just the daily return, as an input. This model class is here further extended to incorporate information on realized measures of volatility, including realized variance and realized range (RR), as well as scaled and smoothed versions of these. An asymmetric Gaussian density error formulation allows a likelihood that leads to direct estimation and one-step-ahead forecasts of quantiles and expectiles, and subsequently of ES. A Bayesian adaptive Markov chain Monte Carlo method is developed and employed for estimation and forecasting. In an empirical study forecasting daily tail risk measures in six financial market return series, over a seven-year period, models employing the RR generate the most accurate tail risk forecasts, compared to models employing other realized measures as well as to a range of well-known competitors.     

Bond risk premia and realized jump risk

We find that augmenting a regression of excess bond returns on the term structure of forward rates with an estimate of the mean realized jump size almost doubles the R² of the forecasting regression. The return predictability from augmenting with the jump mean easily dominates that offered by augmenting with options-implied volatility and realized volatility from high-frequency data. In out-of-sample forecasting exercises, inclusion of the jump mean can reduce the root mean square prediction error by up to 40%. The incremental return predictability captured by the realized jump mean largely accounts for the countercyclical movements in bond risk premia. This result is consistent with the setting of an incomplete market in which the conditional distribution of excess bond returns is affected by a jump risk factor that does not lie in the span of the term structure of yields.     

Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution

The realized-GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed as the realized measure in the realized-GARCH framework. Sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with inherent micro-structure noise and inefficiency. A Bayesian Markov Chain Monte Carlo (MCMC) method is adapted and employed for estimation and forecasting, while various MCMC efficiency and convergence measures are employed to assess the validity of the method. In addition, the properties of the MCMC estimator are assessed and compared with maximum likelihood, via a simulation study. Compared to a range of well-known parametric GARCH and realized-GARCH models, tail risk forecasting results across seven market indices, as well as two individual assets, clearly favour the proposed realized-GARCH model incorporating the two-sided Weibull distribution; especially those employing the sub-sampled realized variance and sub-sampled realized range.     

Asymmetric volatility in the foreign exchange markets

We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous component of realized volatility rather than the jump component.     

The Predictive Power of the French Market Volatility Index: A Multi Horizons Study

The main purpose of this paper is to examine empirically the time series properties of the French Market Volatility Index (VX1). We also examine the VX1's ability to forecast future realized market volatility and finds a strong relationship. More importantly, we show how the index can be used to generate volatility forecasts over different horizons and that these forecasts are reasonably accurate predictors of future realized volatility.     

Forecasting Financial Market Volatility Using a Dynamic Topic Model

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.