GARCH-M框架下权证定价研究

利用标的股票的日收盘价格数据进行GARCH-M参数估计,然后利用物理测度和风险中性测度之间的局部风险中性定价关系,为目前国内的备兑权证进行定价。定价结果表明GARCH框架下的权证定价模型比历史波动率模型能够更好地吻合市场数据,但和市场价格之间仍有差距。分析了这种差异的原因,并给出相应的对策建议。     

GARCH-M框架下权证定价研究

利用标的股票的日收盘价格数据进行GARCH-M参数估计，然后利用物理测度和风险中性测度之间的局部风险中性定价关系，为目前国内的备兑权证进行定价。定价结果表明GARCH框架下的权证定价模型比历史波动率模型能够更好地吻合市场数据，但和市场价格之间仍有差距。分析了这种差异的原因，并给出相应的对策建议。     

B-S模型应用于中国备兑权证定价的实证考察——基于调整的EWMA模型

本文采用B-S期权定价模型,对我国备兑权证进行了理论定价并考察了市场价格与理论价格之间的偏差.在经典的B-S模型基础上,本文还比较了EWMA模型与调整的EWMA模型在对历史波动率进行估算时的有效性.     

证券投资者情绪对市场收益及波动率影响的实证研究

证券市场中的投资者依据他们对上市公司未来盈利的预期来对证券进行心理定价.作为投资者对未来市场预期的一种表征,情绪是心理学及行为金融学的一个重要概念,是反映投资者心理和行为特征的一个重要指标.本文运用非对称的GARCH类模型考察了中国证券投资者的情绪指数与未来市场收益率及波动率之间是否存在显著的统计相关关系.     

碳排放期权定价及实证研究——以湖北碳排放交易中心为例

碳期权作为碳金融市场上重要的交易产品,合理定价有利于投资者做出理性的碳期权套期保值决策,降低碳交易市场风险。因此,本文基于湖北碳排放交易中心碳配额数据构建定价模型,以此对我国八大碳交易市场价格提供一定的借鉴。本文将GARCH模型和期权定价模型B-S引入碳排放交易期权的定价研究中。通过碳排放交易中心配额期货收盘价的数据检验,发现价格波动情况具有非正态性和尖峰厚尾的特征,并采用GARCH模型拟合预测碳价收益率波动率,将预测的数据求取标准差后得到最终日波动率,从而带入B-S定价模型中进行价值估值。结果表明,GARCH模型具有良好的拟合性,有利于提高定价的精准度。     

国内权证市场的投资策略

权证实质是一种看涨、看跌期权。权证的定价比较复杂,有多个因素可决定权证的价格,其中正股价格、执行价格、到期日、股价波动率等几项是最主要的。在国内投资权证,可以选择利用波动率构建无风险套利组合;根据国内权证收益率特点确定在折价时买入权证而在溢价比率非常高时卖出权证;针对不同的市场预期,选择投资不同的权证类型等投资策略。     

基于GARCH模型的股票收益率波动的实证分析

以上证指数为例,利用GARCH模型对我国股票收益率波动进行研究。在建模中,主要进行了平稳性检验;参数估计和检验;ARCH效应检验并拟合GARCH模型。结果证实了股票收益率的波动存在ARCH效应,且GARCH模型能较好的拟合股票收益率序列数据。     

上证50ETF期权定价实证研究——基于B-S期权定价公式

2015年2月9日,我国境内首只股票期权——上证50ETF期权正式上市交易,打开了我国证券市场权益期权时代的大门,将对我国整个金融市场的发展产生深远且持久的影响,因此对研究该期权的定价问题有着深刻的意义。本文主要用基于历史波动率和基于GARCH模型预测的波动率的B-S模型为期权定价,并用平均偏离度量化结果进行分析,得出基于GARCH模型的B-S期权定价方法预测的效果好于其历史波动率的B-S期权定价效果的结论。     

基于B-S定价模型下的香港认购权证价格偏差分析

主要以Black-Scholes定价模型为主要的理论工具,选取香港恒生指数具有代表性的认购权证5577和4504及其正股的数据作样本,应用历史波动率计算了权证的理论价格;通过格兰因因果检验和偏离度分析,检验了市场定价的合理性和有效性。     

大连期货市场大豆量价分析

本文运用GARCH模型和多元的GARCH模型对大连期货市场大豆的量价关系进行了分析,发现收益的绝对值较收益率本身与交易量有更大的相关性,收益率的绝对值对交易量的条件方差具有显著的影响,且它们的波动都有很强的持续性。运用多元的GARCH模型发现交易量的条件方差与收益率的条件方差有显著的相关性,但是条件方差的滞后项却对对方的条件方差的解释作用较小。     

The realized volatility of FTSE‐100 futures prices

Five‐minute returns from FTSE‐100 index futures contracts are used to obtain accurate estimates of daily index volatility from January 1986 to December 1998. These realized volatility measures are used to obtain inferences about the distributional and autocorrelation properties of FTSE‐100 volatility. The distribution of volatility measured daily is similar to lognormal while the volatility time series has persistent positive autocorrelation that displays long‐memory effects. The distribution of daily returns standardized using the measures of realized volatility is shown to be close to normal, unlike the unconditional distribution. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:627–648, 2002     

Return distributions and volatility forecasting in metal futures markets: Evidence from gold,silver, and copper

The characterization of return distributions and forecast of asset‐price variability play a critical role in the study of financial markets. This study estimates four measures of integrated volatility—daily absolute returns, realized volatility, realized bipower volatility, and integrated volatility via Fourier transformation (IVFT)—for gold, silver, and copper by using high‐frequency data for the period 1999 through 2008. The distributional properties are investigated by applying recently developed jump detection procedures and by constructing financial‐time return series. The predictive ability of a GARCH (1,1) forecasting model that uses various volatility measures is also examined. Three important findings are reported. First, the magnitude of the IVFT volatility estimate is the greatest among the four volatility measures. Second, the return distributions of the three markets are not normal. However, when returns are standardized by IVFT and realized volatility, the corresponding return distributions bear closer resemblance to a normal distribution. Notably, the application of financial‐time sampling technique is helpful in obtaining a normal distribution. Finally, the IVFT and realized volatility proxies produce the smallest forecasting errors, and increasing the time frequency of estimating integrated volatility does not necessarily improve forecast accuracy. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:55–80, 2011     

Volatility and jump risk in option returns

We examine the importance of volatility and jump risk in the time-series prediction of S&P 500 index option returns. The empirical analysis provides a different result between call and put option returns. Both volatility and jump risk are important predictors of put option returns. In contrast, only volatility risk is consistently significant in the prediction of call option returns over the sample period. The empirical results support the theory that there is option risk premium associated with volatility and jump risk, and reflect the asymmetry property of S&P 500 index distribution.     

Information content of volatility spreads

This study reexamines the determinants of volatility spreads and suggests a new forecast of future volatilities. Contrary to earlier volatility forecasts, the newly introduced forecast is applicable when investors are not risk‐neutral or when underlying returns do not follow a Gaussian probability distribution. This implies that the method is consistent with the presence of risk premia for other risks such as volatility risk. Using S&P 500 index options, we show that the new volatility forecast outperforms other volatility forecasts including risk‐neutral implied volatility and historical volatility in two aspects. First, the new forecast is superior to other estimates in terms of forecasting errors for future realized volatilities. Second, it is an unbiased estimator of future realized volatilities. This is shown using an encompassing regression analysis. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:533–558, 2010     

Quantile Regression Analysis of the Asymmetric Return‐Volatility Relation

We use quantile regression to investigate the short‐term return‐volatility relation between stock index returns and changes in implied volatility index. Neither the leverage hypothesis nor the volatility feedback hypothesis effectively explains the asymmetric return‐volatility relation. Instead, behavioral explanations, such as the affect and representativeness heuristics, are supported by our results, particularly in the short‐term; the affect heuristic plays an important role. Moreover, in the context of an extreme volatility change distribution, the affect heuristic and time‐pressure dominate. Thus, we observe strong negative and asymmetric relations between each volatility index and its corresponding stock market index. The asymmetry increases monotonically from the median quantile to the uppermost quantile (i.e., 95%); therefore, ordinary least squares (OLS) regression underestimates this relation at upper quantiles. Additionally, the VIX presents the highest asymmetric return‐volatility relation, followed by the VSTOXX, VDAX, and VXN. Finally, the observed asymmetry is more pronounced with the new volatility index measure than with the old, at‐the‐money volatility index measure. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 33:235–265, 2013     

Examining the Return–Volatility Relation for Foreign Exchange: Evidence from the Euro VIX

We compare the return–volatility relation for the euro currency to the equivalent relation for the equity market, examining the sign, symmetry, and strength of the relation. We employ the euro‐currency exchange‐traded fund (FXE) and its associated option implied volatility index (the EVZ), whereas previous studies only employ equities and/or realized volatility. The equity studies find a negative asymmetric return–volatility relation for implied volatility, with a strong relation when large market movements occur. We find that the euro return–volatility relation can possess either a positive or negative sign, is asymmetric, and has a weaker relation. Thus, the sign and strength of the euro relation differs from the equivalent equity relation. Our quantile regressions show that both the positive and negative contemporaneous returns of the euro result in increased volatility in the extreme quantiles of the conditional distribution, with the contemporaneous effect showing a stronger relation when the euro depreciates. We also find that the volume of the euro‐currency ETF options affects the return–volatility relation for the euro ETF. Overall, the results here expand the concept originally restricted to equities, with the surprising results that the return‐implied volatility relation is weaker and the asymmetric return sometimes is positive for the euro currency. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 34:74–92, 2014     

ARCH族模型在上证收益率变动中的实证研究

股票价格的频繁波动是股票市场最明显的特征之一。利用ARCH模型及其扩展模型对上证综合指数的波动性进行实证研究,结果发现,我国上海股票市场收益率序列有明显的尖峰厚尾性、波动聚类性、非正态性以及存在条件异方差特性,波动的信息不对称性等特点。     

Fractals or I.I.D.: Evidence of long-range dependence and heavy tailedness from modeling German equity market returns

Several studies find that the return volatility of stocks tends to exhibit long-range dependence, heavy tails, and clustering. Because stochastic processes with self-similarity possess long-range dependence and heavy tails, it has been suggested that self-similar processes be employed to capture these characteristics in return volatility modeling. In this paper, we find using high-frequency data that German stocks do exhibit these stylized facts. Using one of the typical self-similar processes, fractional stable noise, we empirically compare this process with several alternative distributional assumptions in either fractal form or I.I.D. form (i.e., normal distribution, fractional Gaussian noise, generalized extreme value distribution, generalized Pareto distribution, and stable distribution) for modeling German equity market volatility. The empirical results suggest that fractional stable noise dominates these alternative distributional assumptions both in in-sample modeling and out-of-sample forecasting. Our findings suggest that models based on fractional stable noise perform better than models based on the Gaussian random walk, the fractional Gaussian noise, and the non-Gaussian stable random walk.     

Pricing Stock Options in a Jump‐Diffusion Model with Stochastic Volatility and Interest Rates: Applications of Fourier Inversion Methods

Fast closed form solutions for prices on European stock options are developed in a jump‐diffusion model with stochastic volatility and stochastic interest rates. The probability functions in the solutions are computed by using the Fourier inversion formula for distribution functions. The model is calibrated for the S and P 500 and is used to analyze several effects on option prices, including interest rate variability, the negative correlation between stock returns and volatility, and the negative correlation between stock returns and interest rates.     

The intraday distribution of volatility and the value of wildcard options

This study investigates the value of the wildcard option embedded in the American FT‐SE 100 index (SEI) options. Model‐based studies of S&P 100 index options show the embedded wildcard option to have significant value. By contrast, nonparametric tests on SEI options indicate that the wildcard has very little value. The contrasting results arise because U.S. studies observe a high level of volatility during the 15‐minute wildcard period, whereas the 21‐minute wildcard period in London is relatively quiet. The present study highlights the sensitivity of the wildcard value to the intraday distribution of volatility and indicates the difficulty in estimating the wildcard period volatility, since it is itself volatile. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20: 307–320, 2000