国际原油市场极端风险的测度模型及后验分析

采用可以捕捉收益分布尾部极端风险的ES(Excepted Shortfall)指标,同时基于时变高阶矩波动模型和常规GARCH族模型建立风险测度模型,并在多、空头寸共20个分位数水平下,综合对比了不同模型在国际原油市场风险测度中表现出的精确性差异。研究结果表明:时变高阶矩波动模型可以刻画原油市场收益分布中的时变偏度和时变峰度特征,更好地测度原油市场的极端风险,同时GARCHSK-M模型表现出了相对最高的风险测度精确性,可以作为测度原油市场极端风险相对合理的模型选择。     

可变风险溢价结构下跳扩散模型的期权定价

风险溢价结构是真实测度与风险中性测度间的纽带,能够帮助提取投资者的风险偏好特征。本文针对跳扩散模型构建了灵活的风险溢价形式,允许期权市场隐含信息参与校准跳跃风险的市场价格,进而研究存在跳跃情形下的期权定价,并探索市场风险溢价结构。数值分析和实证研究表明,可变风险溢价结构有助于准确刻画市场定价核曲线,且市场风险溢价结构具有明显的时变特征,跳跃风险溢价能够较好解释隐含波动率曲面。此外,跳扩散模型的可变风险溢价结构在样本内外都具有明显的期权定价优势。考虑了不同样本长度、定价方法、定价区间以及期权产品后,以上结论均是稳健的。本研究有助于系统了解不同市场风险溢价结构与定价规律,有利于深入探索跳跃风险溢价补偿机制。     

公司债的非流动性与风险溢价——基于中国的实证研究

本文基于四种非流动性测度,考察了公司债层面和市场层面影响中国公司债非流动性的因素,讨论了公司债非流动性、权益波动率、印花税调整等因素对公司债风险溢价的影响。研究发现,在控制了信用评级和发行人权益波动率后,在截面上只有Amihud(2002)非流动性测度对公司债风险溢价有正的显著影响。此外,公司债发行人权益波动率和2008年的两次印花税调整对公司债风险溢价有正的稳健显著影响。     

典型事实、极值理论与金融市场动态风险测度研究

本文在金融市场典型事实约束下,运用ARFIMA模型对金融市场条件收益率建模,运用GARCH、GJR、FIGARCH、APARCH、FIAPARCH等5种模型对金融波动率进行建模,进而运用极值理论(EVT)对标准收益的极端尾部风险建模来测度各股市的动态风险,并用返回测试(Back-testing)方法检验模型的适应性。实证结果表明,总的来说,FIAPARCH-EVT模型对各个市场具有较强的适应性,风险测度能力较为优越。进一步,本文在ARFIMA-FIAPARCH模型下,假定标准收益分别服从正态分布(N)、学生t分布(st)、有偏学生t分布(skst)、广义误差分布(GED)共4种分布,对各股市的动态风险测度的准确性进行检验,并和EVT方法的测度结果进行对比分析。结果表明,EVT方法风险测度能力优于其他方法,有偏学生t分布假设下的风险测度模型虽然略逊于EVT方法,但也不失为一种较好的方法;ARFIMA-FI-APARCH-EVT不仅在中国大陆沪深股市表现最为可靠,而且在其他市场也表现出同样的可靠性。     

银行间同业拆放利率风险度量:基于EGARCH-SGED模型的实证分析

根据上海银行间同业拆放利率(SHIBOR)数据的基本特性,分析SHIBOR收益率序列呈尖峰厚尾、偏态和波动集聚等特征,利用EGARCH模型来刻画收益率的波动性,同时利用Skew-GED(SGED)分布来描述收益率的概率分布特征,构建EGARCH-SGED模型来测度SHIBOR收益率的风险价值,并与GED和Skew-t分布下的EGARCH模型的风险测度能力进行了比较。研究结果表明,与其他两类模型相比较而言,EGARCH-SGED模型能更好地描述SHIBOR收益率特性,并且能够显著提高风险价值预测的准确性。     

经济政策不确定性对黄金期货市场收益与波动影响的实证研究

错综复杂的国际局势下,经济政策不确定性越来越高,给金融市场带来难以估量的冲击.本文基于高频数据,运用向量自相关(VAR)模型研究经济政策不确定性对美国黄金期货市场收益和波动率的传导路径和影响程度.我们的研究结果表明:(1)经济政策不确定性主要通过供需渠道、金融渠道、市场间波动率溢出效应三种传导路径影响黄金市场.(2)经济政策不确定性对黄金收益有持续平稳的负向作用,对黄金波动率有持续的正向影响但影响较小,且具有滞后性.(3)经济政策不确定性冲击主要是通过跳跃波动影响黄金市场波动率.     

违约预测:市场模型还是会计模型

本文选取沪深A股1463家上市公司,分别运用基于市场信息的Merton模型和基于会计信息的Logistic模型,测算公司的违约风险。相关性分析的研究结果表明,两种模型在违约测度方面的一致性较差,进一步基于ROC曲线以及准确性比率的分析结果显示,Logistic模型的违约预测效果明显优于Merton模型。     

价格跳跃对我国股指期货市场的影响研究

流动性、波动率及交易活跃度是金融市场微观结构研究中的三个热点问题,在实际的金融市场上也得到了极大关注。利用沪深300股指期货的高频数据,检测出股指期货价格发生跳跃的交易日,并运用Granger因果检验方法研究了跳跃发生日和无跳跃发生日中,市场流动性、波动率及交易活跃度这三个指标之间的相互因果关系。实证结果表明,无论价格是否发生跳跃,我国股指期货市场上的流动性与波动率及流动性与成交量指标之间均存在双向的Granger因果关系。而衡量期货市场交易活跃度的另一重要指标——持仓量,在无跳跃发生时可引导流动性和波动率指标,但在有跳跃发生时这些因果关系消失。     

我国保险市场与银行市场间的风险溢出效应研究——基于上市银行和保险公司的实证分析

本文运用我国上市保险公司和上市银行2008年1月至2016年11月的股票收益率数据构建了两市场的二阶段波动率模型。第一阶段,运用一元ARMA-GARCH模型对两个市场的波动性问题进行了测度。结果表明,两个市场的收益率序列都受到前期收益率的影响,存在风险暴露问题。第二阶段,运用二元VAR(2)-GARCH(1,1)-BEKK模型对两市场间的溢出效应进行了测度。实证结果显示,第一,均值溢出方程表明我国银行市场对保险市场存在微弱且短期的均值溢出效应,反之则没有;第二,波动溢出方程及WALD检验的结果证实我国保险市场和银行市场间存在双向波动溢出效应。本文认为,两市场的风险具有明显的关联性,任一市场的风险均可通过资本市场的“二阶效应”而传染给另一市场并形成风险扩散效应。     

卖空交易与股票价格稳定性--来自中国融资融券市场的自然实验北大核心CSSCISSCI

基于2009年4月-2013年12月我国殷票市场的数据,本文研究了融资融券标的股票和非标的股票、以及股票被列入和剔出融资融券标的前后的价格波动特征。结果表明,融资融券交易机制的推出有效提高了我国股票价格的稳定性,融资融券标的股票的价格波动率和振幅均出现了显著性下降。我们还发现,融资融券交易显著降低了股票价格的跳跃风险,有利于防止股票价格的暴涨暴跌和过度投机。此外,融资融券交易在抑制股票价格异质性波动上也起到了实质性作用,从而有助于增加上市公司信息透明度和市场信息效率。     

噪声、跳跃与高频价格波动——基于门限预平均实现波动的分析

高频数据中的噪声和价格跳跃使得波动的估计缺乏一致性,本文提出用门限预平均实现波动的方法估计同时存在市场微观结构噪声和价格跳跃时高频价格波动,该方法是资产价格实际波动的一致估计,并有最优的收敛速度。模拟发现,门限预平均实现波动和常用的高频波动估计方法相比,有更小的均方误差。中国证券市场的实证分析表明,门限预平均实现波动能减少波动预测误差,得到更为精确的风险管理价值。     

Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market

This study examines the Chinese implied volatility index (iVIX) to determine whether jump information from the index is useful for volatility forecasting of the Shanghai Stock Exchange 50ETF. Specifically, we consider the jump sizes and intensities of the 50ETF and iVIX as well as cojumps. The findings show that both the jump size and intensity of the 50ETF can improve the forecasting accuracy of the 50ETF volatility. Moreover, we find that the jump size and intensity of the iVIX provide no significant predictive ability in any forecasting horizon. The cojump intensity of the 50ETF and iVIX is a powerful predictor for volatility forecasting of the 50ETF in all forecasting horizons, and the cojump size is helpful for forecasting in short forecasting horizon. In addition, for a one-day forecasting horizon, the iVIX jump size in the cojump is more predictive of future volatility than that of the 50ETF when simultaneous jumps occur. Our empirical results are robust and consistent. This work provides new insights into predicting asset volatility with greater accuracy.     

The quality of market volatility forecasts implied by S&P 100 index option prices

This study examines the performance of the S&P 100 implied volatility as a forecast of future stock market volatility. The results indicate that the implied volatility is an upward biased forecast, but also that it contains relevant information regarding future volatility. The implied volatility dominates the historical volatility rate in terms of ex ante forecasting power, and its forecast error is orthogonal to parameters frequently linked to conditional volatility, including those employed in various ARCH specifications. These findings suggest that a linear model which corrects for the implied volatility's bias can provide a useful market-based estimator of conditional volatility.     

The Role of Volatility in Forecasting

Theories of underinvestment propose a link between cash flow volatility and investment and subsequent cash flow and earnings levels. Consistent with these theories, our results indicate that forecasting models that include volatility as an explanatory variable have greater accuracy and lower bias than forecasting models that exclude volatility. The improvement in forecast accuracy and bias is greatest when the firm is most likely to experience underinvestment. The profitable implementation of a trading strategy based on these findings, however, suggests that equity market participants do not incorporate fully the information in historical volatility when forecasting future firm performance.     

The information content of option-implied information for volatility forecasting with investor sentiment

This study explores the effect of investor sentiment on the volatility forecasting power of option-implied information. We find that the risk-neutral skewness has the explanatory power regarding future volatility only during high sentiment periods. Furthermore, the implied volatility has varying volatility forecasting ability depending on the level of investor sentiment. Our findings suggest that the effectiveness of volatility forecasting models based on option-implied information varies over time with the level of investor sentiment. We confirm the important role of investor sentiment in volatility forecasting models exploiting option-implied information with strong evidence from in-sample and out-of-sample analyses. We also present improvements in the accuracy of volatility forecasts from volatility forecasting models derived by incorporating investor sentiment in these models.     

Which sentiment index is more informative to forecast stock market volatility? Evidence from China

In this paper, we investigate the predictive ability of three sentiment indices constructed by social media, newspaper, and Internet media news to forecast the realized volatility (RV) of SSEC from in- and out-of-sample perspectives. Our research is based on the heterogeneous autoregressive (HAR) framework. There are several notable findings. First, the in-sample estimation results suggest that the daily social media and Internet media news sentiment indices have significant impact for stock market volatility, while the sentiment index built by traditional newspaper have no impact. Second, the one-day-ahead out-of-sample forecasting results indicate that the two sentiment indices constructed by social media and Internet media news can considerably improve forecast accuracy. In addition, the model incorporating the positive and negative social media sentiment indices exhibits more superior forecasting performance. Third, we find only the sentiment index built by Internet media news can improve the mid- and long-run volatility predictive accuracy. Fourth, the empirical results based on alternative prediction periods and alternative volatility estimator confirm our conclusions are robust. Finally, we examine the predictability of the monthly sentiment indices and find that the two sentiment indices of social media and Internet media news contain more informative to forecast the monthly RV of SSEC, CSI800, and SZCI, however invalid for CSI300.     

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.     

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.     

Forecasting gold volatility with geopolitical risk indices

This paper tries to forecast gold volatility with multiple country-specific (GPR) indices and compares the role of combined prediction models and dimension reduction methods regarding the improvement of gold volatility prediction accuracy. For this purpose, GARCH-MIDAS model’s several extensions are used. We find firstly that most country-specific GPR indices have driving effects on gold volatility, and it makes sense to take forecast information from multiple country-specific GPR indices into account when forecasting gold volatility. The out-of-sample empirical results also indicate that the dimension reduction methods yield better predictions compared to the combined prediction models. In addition, dimension reduction technologies have excellent forecasting performance mainly during low gold volatility periods. Finally, our empirical findings are robust after changing the evaluation method, model settings, in-sample length and gold market.     

The information content of risk-neutral skewness for volatility forecasting

The paper investigates whether risk-neutral skewness has incremental explanatory power for future volatility in the S&P 500 index. While most of previous studies have investigated the usefulness of historical volatility and implied volatility for volatility forecasting, we study the information content of risk-neutral skewness in volatility forecasting model. In particular, we concentrate on Heterogeneous Autoregressive model of Realized Volatility and Implied Volatility (HAR-RV-IV). We find that risk-neutral skewness contains additional information for future volatility, relative to past realized volatilities and implied volatility. Out-of-sample analyses confirm that risk-neutral skewness improves significantly the accuracy of volatility forecasts for future volatility.