期权定价模型中的波动率分析

期权定价模型是布莱克的一种典型的相对经济理论,它在金融实践过程中产生巨大的经济影响,这种期权的模型需要输入参数中在市场中无法直接观察取得的重要变量,即波动率数值,也就是说,基于历史数据来计量历史波动。人们通常在期权经济定价中,结合期权的价格,采用定量模型倒推出隐含的波动率,这种隐含的波动率对于投资者未来市场的预期有很重要的作用,对于期权市场和经济市场的避险和套期保值业务来说,为了能够进行更好的风险规避和管理,就必须要了解隐含波动率的波动规律。     

在波动率微笑中静态对冲奇异期权

世界上或许还没有一个期权市场上的隐含波动率不出现某种倾斜(如利率和股票)或者微笑(如汇率和商品)。从模型的角度来说就是大家最熟悉的对数正态分布只是一个非常大胆的近似。奇异型期权常出现的不连续盈亏函数(Payoff)对价格波动率的形状却是比较敏感。在这里我们先提出一个具有波动率倾斜或者微笑的模型族群,然后首次证明该模型族群具有期权对称性,从而导出奇异型期权可以分解为一组常规香草型期权,也就是说即使有波动率微笑时,奇异型期权也可以用一组常规香草型欧式期权来静态复制。我们只需要管理一组常规欧式期权来管理奇异型期权的市场风险。同时因为复制也自然得到在波动率微笑的市场中的奇异期权的定价。除了期权的理论意义外,更重要的是对实际市场参与者来说,提供了一个非常简便实用和可操作的复杂型期权的定价、风险分解和对冲管理手段。     

运用Matlab基于LSM方法对美式期权定价的新探究

传统期权定价方法是通过主观假定初始价格、执行价格、期限、波动率、无风险利率等条件来对期权进行定价,很少联系实际的期权市场报价对期权进行定价。本文根据股票期权市场报价,通过Matlab快速方便地求解出隐含的波动率和无风险利率,并在此基础上运用Matlab基于最小二乘蒙特卡洛模拟(LSM)方法对该股票的美式期权进行定价。本文揭示了如何根据期权市场报价实现隐含波动率和无风险利率的求解,进而结合LSM方法对美式期权进行定价的一种新方法。此外,本文对LSM方法的改进技术也进行了探讨。     

运用Matlab基于LSM方法对羹式期权定价的新探究

传统期权定价方法是通过主观假定初始价格、执行价格、期限、波动率、无风险利率等条件来对期权进行定价,很少联系实际的期权市场报价对期权进行定价。本文根据股票期权市场报价,通过Matlab快速方便地求解出隐含的波动率和无风险利率,并在此基础上运用Matlab基于最／bZ．乘蒙特卡洛模拟（LSM）方法对该股票的美式期权进行定价。本文揭示了如何根据期权市场报价实现隐含波动率和无风险利率的求解,进而结合LSM方法对美式期权进行定价的一种新方法。此外,本文对LSM方法的改进技术也进行了探讨。     

基于受约束正则方法的奇异期权定价

本文研究一种对奇异期权定价的非参数方法,避开了传统期权定价方法对资产价格分布假设和波动率假设等难题,并且不同于其他非参数方法从期权历史交易价格出发为新期权定价,本文直接用标的资产的价格为期权定价。因此,即使在期权市场不完善,期权价格不可靠、不可得,甚至不存在的情况下,也能为期权有效定价。此外,本文将正则定价方法和隐含二叉树方法有效结合,扩展到为奇异期权的定价问题上,并在传统的正则定价方法中加入了价格敏感因素作为约束条件,以提高该方法的定价精度。     

期权定价模型中的波动率分析

布莱克-斯科尔斯的期权定价模型是一个对经济理论、金融实践产生巨大影响的模型。该模型需要输入的参数中唯一无法在市场中直接观察到的重要变量是基础资产的波动率。基于历史数据来计量的历史波动率有严重缺陷,于是人们根据期权的市场价格,利用Black-Scholes定价模型倒推出隐含波动率。隐含波动率反映投资者对未来市场的共同预期;对于避险者的套期保值业务来说,这是进行风险管理的一项重要指标。然而,隐含波动率在使用过程中也存在着＂波动率微笑＂、＂波动率偏斜＂及＂波动率期限结构＂等现象。究其根源,皆源自于Black-Scholes模型所依据的某些假设条件与实际情况不相符合。     

基于GARCH模型的股票期权定价方法研究

本文应用GARCH模型估计股票期权标的股票的收益波动率,并将估计出的收益波动率代入Black-Schole期权定价公式,以期提高Black-Scholes期权定价公式的精确度.为验证该方法的有效性,本文以首创JBTI为例进行了实证研究,结果表明在期权交易价格上升的期间内,基于GARCH模型的期权定价方法可以提高Black-Scholes期权定价公式的精确度.     

确定波动率的欧式期权鞅定价与利率期限结构

把merton随机利率期权模型扩展到允许基础资产支付红利情形,在一系列假设前提基础上重新运用鞅测度方法可以得到无套利时随机利率下欧式未定权益的一般定价公式,进而得出欧式期权定价的解析表达式。通过对债券价格过程的假设,构造出一个关于确定波动率的债券价格过程、单因素利率期限结构模型和债券价格之间的对应关系的命题,并由此得出了债券期权定价的解析公式。     

我国期权市场的定价核研究 ——基于马尔可夫状态转换的分析

基于我国上证50ETF期权市场,利用MSGARCH模型估计考虑状态转换后的经验定价核,分析不同市场阶段下经验定价核的形状及其成因,并进一步研究经验定价核的定价效果、期限结构以及对市场收益率的预测作用.结果发现:通过划分波动率状态,MSGARCH模型有效减少了周期性偏差.不同市场阶段下投资者情绪的变化使风险中性分布出现差异,进而导致经验定价核在不同的波动率时期存在明显的差异.进一步考虑状态转换后的经验定价核在不同期限下具有稳定性,且定价效果更加精确.     

股价波动源模型下可转换债券的鞅定价

衍生证券是指其价格或投资回报最终取决于另一种资产(即标的资产)的价格的一类新型的金融工具,从1973年出现至今已有30多个年头了。其间,期权市场得到了迅猛发展,在标准期权的基础上,衍生出了各种各样的奇异期权,障碍期权就是其中的一种。对其合理适当地定价是金融数学中一个既具有理论意义又具有实际应用价值的重要问题。期权的价格与其标的物——股票价格之间有着密切的联系。事实上,股票价格的形成机制的理论研究一直影响着证券市场的发展。目前,期权定价中通常采用的描述股票价格的波动模型主要有:随机游走模型(Random Walk)(文[3]),对数正态分布模型。文[1]指出了上述两种模型与现实市场的差距,并结合大量的实证分析,提出了一种由随机波动源和异常波动源共同作用的股票价格波动模型——波动源模型。它既考虑了大量的散户交易者的不相关交易对股票价格造成的随机波动,又考虑到了宏观因素、上市公司背景因素和主力交易者的交易行为对股票价格造成的异常波动,因此,它能更精确地描述股票价格的波动现象,更贴近现实市场。本文利用鞅方法定价,在股价波动源模型下,研究了一种企业债券和股票期权相结合的混合证券——可转换债券的定价公式。可转换债券是一种...     

VIX option pricing and CBOE VIX Term Structure: A new methodology for volatility derivatives valuation

This study integrates CBOE VIX Term Structure and VIX futures to simplify VIX option pricing in multifactor models. Exponential and hump volatility functions with one- to three-factor models of the VIX evolution are used to examine their pricing for VIX options across strikes and maturities. The results show that using exponential volatility functions presents an effective choice as pricing models for VIX calls, whereas hump volatility functions provide efficient out-of-sample valuation for most VIX puts, in particular with deep in-the-money and deep out-of-the-money. Pricing errors for calls can be further reduced with a two-factor model.     

A jump diffusion model for VIX volatility options and futures

Volatility indices are becoming increasingly popular as a measure of market uncertainty and as a new asset class for developing derivative instruments. Although jumps are widely considered as a salient feature of volatility, their implications for pricing volatility options and futures are not yet fully understood. This paper provides evidence indicating that the time series behaviour of the VIX index is well approximated by a mean reverting logarithmic diffusion with jumps. This process is capable of capturing stylized facts of VIX dynamics such as fast mean-reversion at higher levels, level effects of volatility and large upward movements during times of market stress. Based on the empirical results, we provide closed-form valuation models for European options written on the spot and forward VIX, respectively.     

Estimating and using GARCH models with VIX data for option valuation

This paper uses information on VIX to improve the empirical performance of GARCH models for pricing options on the S&P 500. In pricing multiple cross-sections of options, the models’ performance can clearly be improved by extracting daily spot volatilities from the series of VIX rather than by linking spot volatility with different dates by using the series of the underlying’s returns. Moreover, in contrast to traditional returns-based Maximum Likelihood Estimation (MLE), a joint MLE with returns and VIX improves option pricing performance, and for NGARCH, joint MLE can yield empirically almost the same out-of-sample option pricing performance as direct calibration does to in-sample options, but without costly computations. Finally, consistently with the existing research, this paper finds that non-affine models clearly outperform affine models.     

U.S. stock market crash risk, 1926–2010

This paper examines how well alternate time-changed Lévy processes capture stochastic volatility and the substantial outliers observed in U.S. stock market returns over the past 85 years. The autocorrelation of daily stock market returns varies substantially over time, necessitating an additional state variable when analyzing historical data. I estimate various one- and two-factor stochastic volatility/Lévy models with time-varying autocorrelation via extensions of the Bates (2006) methodology that provide filtered daily estimates of volatility and autocorrelation. The paper explores option pricing implications, including for the Volatility Index (VIX) during the recent financial crisis.     

VCRIX — A volatility index for crypto-currencies

Public interest, explosive returns, and diversification opportunities gave stimulus to the adoption of traditional financial tools to crypto-currencies. While the CRIX offered the first scientifically-backed proxy to the crypto-market (analogous to S&P 500), measuring the forward-oriented risk in the crypto-currency market posed a challenge of a different kind. Following the intuition of the “fear index” VIX for the American stock market, the VCRIX volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on CRIX and offers a forecast based on the Heterogeneous Auto-Regressive (HAR) model. The HAR model was selected as the most suitable out of a horse race of volatility models, with two proxies for implied volatility, namely the 30 days mean annualized volatility and realized volatility. The model was further examined by the simulation of VIX (resulting in a correlation of 78% between the actual VIX and a “VIX” version estimated with the VCRIX technology). Trading strategies confirmed the predictive power of VCRIX and supported the selection of the 30 days means annualized volatility proxy. The best performing trading strategy with the use of VCRIX outperformed the benchmark strategy for 99.8% of the tested period and 164% additional returns. VCRIX provides forecasting functionality and serves as a proxy for the investors’ expectations in the absence of a developed crypto derivatives market. These features provide enhanced decision making capacities for market monitoring, trading strategies, and potentially option pricing.     

Volatility dynamics for the S&P 500: Further evidence from non-affine,multi-factor jump diffusions

We apply Markov chain Monte Carlo methods to time series data on S&P 500 index returns, and to its option prices via a term structure of VIX indices, to estimate 18 different affine and non-affine stochastic volatility models with one or two variance factors, and where jumps are allowed in both the price and the instantaneous volatility. The in-sample fit to the VIX term structure shows that the second (stochastic long-term volatility) factor is required to fit the VIX term structure. Out-of-sample tests on the fit to individual option prices, as well as in-sample tests, show that the inclusion of jumps is less important than allowing for non-affine dynamics. The estimation and testing periods together cover more than 21 years of daily data.     

Partial moment volatility indices

Forward‐looking partial moment volatility indices are developed using state‐pricing, called the bear index (BEX) and bull index (BUX). Using S&P 500 index (SPX) option prices, we find that BEX and BUX provide superior forecasts for the lower and upper partial moments of future market realised volatility, respectively. We examine the relation between SPX returns and changes in BEX and BUX at the daily level. Results are consistent with the volatility feedback hypothesis. Further, we show that BEX may be more suitable as the ‘investor fear gauge’ than VIX.     

Continuous-time VIX dynamics: On the role of stochastic volatility of volatility

This paper examines the ability of several different continuous-time one- and two-factor jump-diffusion models to capture the dynamics of the VIX volatility index for the period between 1990 and 2010. For the one-factor models we study affine and non-affine specifications, possibly augmented with jumps. Jumps in one-factor models occur frequently, but add surprisingly little to the ability of the models to explain the dynamic of the VIX. We present a stochastic volatility of volatility model that can explain all the time-series characteristics of the VIX studied in this paper. Extensions demonstrate that sudden jumps in the VIX are more likely during tranquil periods and the days when jumps occur coincide with major political or economic events. Using several statistical and operational metrics we find that non-affine one-factor models outperform their affine counterparts and modeling the log of the index is superior to modeling the VIX level directly.     

Double-jump diffusion model for VIX: evidence from VVIX

This paper studies the continuous-time dynamics of VIX with stochastic volatility and jumps in VIX and volatility. Built on the general parametric affine model with stochastic volatility and jumps in the logarithm of VIX, we derive a linear relationship between the stochastic volatility factor and the VVIX index. We detect the existence of a co-jump of VIX and VVIX and put forward a double-jump stochastic volatility model for VIX through its joint property with VVIX. Using the VVIX index as a proxy for stochastic volatility, we use the MCMC method to estimate the dynamics of VIX. Comparing nested models of VIX, we show that the jump in VIX and the volatility factor are statistically significant. The jump intensity is also stochastic. We analyse the impact of the jump factor on VIX dynamics.     

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.