基于时间变换和分数型过程下的期权定价及模拟分析

由布朗运动驱动的期权定价模型是最为经典的模型,但该模型不能准确地描述资产价格的长相依性和短时间的不变性.本文提出了时间变换下的次分数布朗运动支付红利期权定价模型.首先,建立了次分数布朗运动扩散B-S模型,获得了带红利的欧式期权定价公式.其次,利用金融实际数据进行统计模拟,研究表明新模型能够反映金融资产真实值.     

分数布朗运动下带交易费用和红利的两值期权定价

本文研究了在分数布朗运动环境下带交易费用和红利的两值期权定价问题.在标的资产服从几何分数布朗运动的情况下,利用分数It公式和无风险套利原理建立了分数布朗运动环境下带交易费用和红利的两值期权的定价模型.再通过用偏微分方程的方法进行求解此定价模型,得到了在分数布朗运动下带交易费用和红利的两值期权定价公式.所得结果推广了已有结论.     

分数布朗运动下可分离交易可转债的定价

假定利率满足Vasicek模型和标的股票的价格遵循分数布朗运动的条件下,建立了分离交易的可转换公司债券的的定价模型,并利用风险中性定价原理推导出其定价公式.最后,所做模拟研究表明分数布朗运动下可分离交易可转债的定价模型是合理的.     

时变混合分数布朗运动下带交易费用的亚式期权定价

考虑到经典的Black-Scholes(B-S)期权定价模型不能描述金融资产价格常值周期性、长相依性等特征,因此采用时变混合分数布朗运动描述金融资产价格的变动,并且对亚式期权定价时也考虑了交易费用.运用自融资Delta对冲策略得出了在离散情形下几何平均亚式看涨期权价格所满足的偏微分方程以及几何平均亚式看涨、看跌期权的定...     

基于正则逆Gamma分布和广义极值分布的VaR计算

股指收益率的分布和风险价值(VaR)的计算是证券市场研究的热点问题.本文对来自上证指数和深证成指日收益率采用正则逆Gamma分布和偏T分布(SST)分别进行拟合,对极值序列(周、月极大值和极小值)建立广义极值分布函数。并由此计算VaR值,度量这几种序列的风险价值.结果表明正则逆Gamma分布能更好地拟合日收益率的分布,以及采用周极值收益率的广义极值分布计算VaR值来估计风险较为合理.     

应用EWMA-GARCH(1,1)-M模型对沪深300股指期货最小VaR套期保值效果研究

VaR是目前国际上应用最广泛的度量金融风险的指标之一,其核心在于波动率,也就是方差的参数估计.采用EWMA模型估计方差,并且结合风险溢价特征的GARCH(1,1)-M模型计算出沪深300股指及其期货的最优衰减因子为0.933 25,摒弃了以往采用0.940 0作为衰减因子的一贯做法,并且运用Cornish-Fisher方程对正态分布的分位数进行了修正,得到修正后的套期保值比率以及资产组合的VaR,与传统的套期保值模型相比,该模型的风险价值VaR降低的程度明显,并且对投资组合未来的VaR具有很好的预测效果,表明EWMA-GARCH(1,1)-M模型对沪深300股指期货的套期保值效果较好.     

VaR最小化的股指期货套期保值比率研究

以VaR最小化为目标,结合波动率预测建立套期保值模型,充分反应了金融收益率尖峰厚尾和波动聚集的特征。通过对沪深300股指期货的日结算数据实证研究发现,在现货组合与股指期货高相关性的条件下,VaR最小化套期保值较最小方差套期保值能进一步降低组合样本外收益率的VaR值,EWMA与Cornish-Fisher展开相结合的方法能取得最好的VaR最小化套期保值效果。     

对数正态分布的VAR数学模型及其计算

VaR(Value at Risk)是一种以规范的统计技术来度量市场风险的新标准,目前在金融数学领域被广泛使用,它是指在正常的市场条件和给定的置信度下,在给定的持有期间内,测度某一投资组合所面临的最大的潜在损失的数学方法.传统的VaR计算方法在计算开放式基金时,可能存在着低估风险的情况.着重论述了VaR模型的数学原理以及该模型的计算方法,运用对数正态分布假设来评估开放式基金的风险,以验证其结果是否更加接近实际风险值.     

基于不同分布EGARCH模型的EU ETS价格波动和风险研究

分别基于正态分布、t分布、GED分布假设下的EGARCH模型,考察EUA和CER期货价格收益率的波动特征,并估算期货市场的风险VaR值,利用LR统计量检验VaR,估计值的准确程度.实证结果表明:碳期货收益率存在明显的"尖峰厚尾"特性;碳期货市场存在负的"杠杆效应","利多"的影响小于"利空"的影响;EUA期货市场相比CER期货市场具有更高的风险;EGARCH-GED模型对碳期货市场的风险刻画能力最强,其次是EGARCH-N模型,EGARCH-t模型刻画能力最差.     

时变t-copula蒙特卡罗模拟的亚洲股指组合风险研究

应用时变条件t-copula函数描述沪市与亚洲主要股票市场指数收益序列之间的时变相依结构.时变条件t-copula模型的难点在于如何设定时变相依参数的演化方程,建立了用于描述包含时变自由度在内的所有时变相依模型参数的演化方程.进而采用蒙特卡洛仿真方法计算了各种指数组合的VaR,分析了沪综指与亚洲主要股指组合风险的演化趋势,并对结果进行后验测试,结果表明,时变条件t-copula函数仿真估计VaR可以覆盖最大损失风险.     

Optimal investment–reinsurance strategies with state dependent risk aversion and VaR constraints in correlated markets

In this paper, we investigate the optimal time-consistent investment–reinsurance strategies for an insurer with state dependent risk aversion and Value-at-Risk (VaR) constraints. The insurer can purchase proportional reinsurance to reduce its insurance risks and invest its wealth in a financial market consisting of one risk-free asset and one risky asset, whose price process follows a geometric Brownian motion. The surplus process of the insurer is approximated by a Brownian motion with drift. The two Brownian motions in the insurer’s surplus process and the risky asset’s price process are correlated, which describe the correlation or dependence between the insurance market and the financial market. We introduce the VaR control levels for the insurer to control its loss in investment–reinsurance strategies, which also represent the requirement of regulators on the insurer’s investment behavior. Under the mean–variance criterion, we formulate the optimal investment–reinsurance problem within a game theoretic framework. By using the technique of stochastic control theory and solving the corresponding extended Hamilton–Jacobi–Bellman (HJB) system of equations, we derive the closed-form expressions of the optimal investment–reinsurance strategies. In addition, we illustrate the optimal investment–reinsurance strategies by numerical examples and discuss the impact of the risk aversion, the correlation between the insurance market and the financial market, and the VaR control levels on the optimal strategies.     

Long‐range dependence and performance in telecom networks

Telecommunications systems have recently undergone significant innovations. These call for suitable statistical models that can properly describe the behaviour of the input traffic in a network. Here we use fractional Brownian motion (FBM) to model cumulative traffic network, thus taking into account the possible presence of long‐range dependence in the data. A Bayesian approach is devised in such a way that we are able to: (a) estimate the Hurst parameter H of the FBM; (b) estimate the overflow probability which is a parameter measuring the quality of service of a network: (c) develop a test for comparing the null hypothesis of long‐range dependence in the data versus the alternative of short‐range dependence. In order to achieve these inferential results, we elaborate an MCMC sampling scheme whose output enables us to obtain an approximation of the quantities of interest. An application to three real datasets, corresponding to three different levels of traffic, is finally considered. Copyright © 2004 John Wiley & Sons, Ltd.     

A fractional credit model with long range dependent default rate

Motivated by empirical evidence of long range dependence in macroeconomic variables like interest rates we propose a fractional Brownian motion driven model to describe the dynamics of the short and the default rate in a bond market. Aiming at results analogous to those for affine models we start with a bivariate fractional Vasicek model for short and default rate, which allows for fairly explicit calculations. We calculate the prices of corresponding defaultable zero-coupon bonds by invoking Wick calculus. Applying a Girsanov theorem we derive today’s prices of European calls and compare our results to the classical Brownian model.     

Oscillatory Fractional Brownian Motion

We introduce oscillatory analogues of fractional Brownian motion, sub-fractional Brownian motion and other related long range dependent Gaussian processes, we discuss their properties, and we show how they arise from particle systems with or without branching and with different types of initial conditions, where the individual particle motion is the so-called c-random walk on a hierarchical group. The oscillations are caused by the ultrametric structure of the hierarchical group, and they become slower as time tends to infinity and faster as time approaches zero. A randomness property of the initial condition increases the long range dependence. We emphasize the new phenomena that are caused by the ultrametric structure as compared with results for analogous models on Euclidean space.     

Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data

Value-at-Risk (VaR) is a popular measure of market risk. To convey information regarding potential exceedances beyond the VaR, Expected Shortfall (ES) has become the risk measure for trading book bank regulation. However, the estimation of VaR and ES is challenging, as it requires the estimation of the tail behaviour of daily returns. In this paper, we take advantage of recent research that develops joint scoring functions for VaR and ES. Using these functions, we present a novel approach to estimating the two risk measures based on intraday data. We focus on the intraday range, which is the difference between the highest and lowest intraday log prices. In contrast to intraday observations, the intraday low and high are widely available for many financial assets. To alleviate the challenge of modelling extreme risk measures, we propose the use of the intraday low series. We draw on a theoretical result for Brownian motion to show that a quantile of the daily returns can be estimated as the product of a constant term and a less extreme quantile of the intraday low returns, which we define as the difference between the lowest log price of the day and the log closing price of the previous day. In view of this, we use estimates of the VaR and ES of the intraday low returns to estimate the VaR and ES of the daily returns. We provide empirical support for the new proposals using data for five stock indices and five individual stocks.     

基于下界VaR对沪深股市市场风险的实证研究

本文应用Luciano和Marena提出的计算资产组合VaR的上下界的方法,对沪深股市的市场风险做了实证研究,并与传统的正态VaR做了比较。实证分析表明,沪深股市的市场风险确实存在“厚尾”和“波动聚集”现象。本文对国内市场的波动聚集现象进行了详细分析．并讨论了风险控制模型的相关检验,下界VaR通过了两次模型检验。     

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??Model of option pricing driven by Brownian motion is the most classical model. However, it can not describe long-term property and invariance in a short period of time of asset price. In this article, option pricing model driven by sub-fractional Brownian motion is studied under time-transform with dividend-paying. Firstly, the model of diffusion B-S model of sub-fractional Brownian motion is build, and get option pricing formula with dividends. Secondly, statistical simulation is used by real data in finance and show that new model can reflect real financial assets.     

混合分数布朗运动下欧式期权模糊定价研究

本文采用混合分数布朗运动来刻画标的股票价格的动态变化,以此体现金融市场的长记忆性特征。在混合分数Black-Scholes模型的基础上,基于标的股票价格、无风险利率和波动率均是模糊数的假定下,构建了欧式期权模糊定价模型。其次,分析了金融市场长记忆性的度量指标Hurst指数H对欧式期权模糊定价模型的影响。最后,数值实验表明：考虑长记忆性特征得到的欧式期权模糊定价模型更符合实际。     

混合分数布朗运动驱动的幂期权定价模型

假设标的资产遵循由混合分数布朗运动驱动的随机微分方程,建立了混合分数布朗运动环境下的金融数学模型.利用拟鞅方法,获得了欧式幂期权定价公式的解析式及其平价公式.最后阐述了分数布朗运动只是混合布朗运动的一种特殊情形.     

HMM based scenario generation for an investment optimisation problem

The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.