本文研究了随机波动率模型的最小熵鞅测度和效用无差别定价。
This paper deals with the minimal entropy martingale measure and utility indifference pricing concerning a stochastic volatility model.
本文引入了基于日内价格幅度与回报两个测度指标的随机波动率模型。
In this paper we propose to a stochastic volatility model based on daily returns and intra-daily high-low price range jointly.
基于不完备的随机波动率模型，本文给出了不同著名鞅测度下定价的大小顺序。
This paper orders option prices under different well known martingale measures in an incomplete stochastic volatility model.
之后详细讨论了ARCH模型及其扩展形式，并对随机波动率模型做了简单介绍。
Then ARCH model is well discussed and the stochastic volatility model is introduced.
针对这种情况，本文试图建立一种比较简单的随机波动率模型——波动率服从有限马氏链的模型。
Aimed at this case, this paper try to construct a simple stochastic volatility model - volatility following a finite Markov chain.
利用中国股市数据进行的实证结果表明，与单测度指标的随机波动率模型相比，基于两个测度指标的随机波动率模型能更好地描述股票市场波动率和市场波动风险。
Empirical results on Chinese stock market indicate that stochastic volatility model based on the two index outperforms those based on one index in capturing volatility character and market risk.
在考虑一个带有股票和债券的金融市场后，本文提出了一个具随机波动率的股票价格的随机微分方程模型。
Considering a financial market with risky stocks and riskless bond, we describe the stochastic model of stock prices with stochastic volatility.
假设波动率是随机的，且资产价格服从l evy过程，即在前述模型基础上作了进一步推广。
The other is that the volatility is assumed to be stochastic and the price of the underlying asset is a levy process, namely we can further promote the model on the basis of the first one.
以净现值法为基础，应用蒙特卡罗原理，提出了在石油产量和市场油价随机变动条件下石油勘探项目实物期权应用模型中不同阶段的波动率参数估算方法。
On the basis of traditional net present value method and Monte Carlo theory, a method for calculating the stage volatilities in the petroleum real option model was given.
在此基础上利用随机波动类模型对两种收益率的波动性特征进行拟合，并对拟合优度进行分析。
Based on this we simulate the volatility features of the two kinds of yield rate time series and analyze their fitting results using the stochastic volatility models.
在此基础上利用随机波动类模型对两种收益率的波动性特征进行拟合，并对拟合优度进行分析。
Based on this we simulate the volatility features of the two kinds of yield rate time series and analyze their fitting results using the stochastic volatility models.
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