基于分形布朗模型的流量模拟控制

本文首先介绍了网络流量模拟的过程,接着介绍了网络中流量自相似现象,在已有自相似的流量模型中,分形布朗运动过程是最简单最易于求解的自相似流量模型,本文就分形布朗运动(FBM)模型的流量控制模拟进行了详细的分析。     

基于分数布朗运动和跳过程的股本权证定价模型

考虑到金融市场中资产价格具有的记忆性和长期相关性,模型假设股本权证标的资产价格服从分数布朗运动过程;并考虑到市场存在不确定因素而引起的价格巨大的波动,在模型中又引入了一个跳过程。首先得出权证定价的一般公式,最后在考虑股本权证行权后产生的稀释效应,得出稀释调整后的股本权证定价公式,并将其延伸到支付红利情况下。     

Asymptotics for duration-driven long range dependent processes

We consider processes with second order long range dependence resulting from heavy tailed durations. We refer to this phenomenon as duration-driven long range dependence (DDLRD), as opposed to the more widely studied linear long range dependence based on fractional differencing of an i.i.d. process. We consider in detail two specific processes having DDLRD, originally presented in Taqqu and Levy [1986. Using renewal processes to generate long-range dependence and high variability. Dependence in Probability and Statistics. Birkhauser, Boston, pp. 73–89], and Parke [1999. What is fractional integration? Review of Economics and Statistics 81, 632–638]. For these processes, we obtain the limiting distribution of suitably standardized discrete Fourier transforms (DFTs) and sample autocovariances. At low frequencies, the standardized DFTs converge to a stable law, as do the standardized sample autocovariances at fixed lags. Finite collections of standardized sample autocovariances at a fixed set of lags converge to a degenerate distribution. The standardized DFTs at high frequencies converge to a Gaussian law. Our asymptotic results are strikingly similar for the two DDLRD processes studied. We calibrate our asymptotic results with a simulation study which also investigates the properties of the semiparametric log periodogram regression estimator of the memory parameter.     

分数布朗运动环境下的美式看涨期权的定价方法

在分数布朗运动模拟算法基础上,提出了分数布朗运动环境下标的资产价格过程的一种数值模拟方法。然后应用于欧式和美式看涨期权定价。结果表明,该方法具有很快的收敛速度,而且基于最小二乘方法和偏最小二乘方法的美式看涨期权价格,都与对应的欧式看涨期权价格几乎完全一样。这恰恰验证了不支付红利的条件下,美式看涨期权不应该提前执行的理论论断。     

由分数布朗运动和Poisson过程驱动的随机微分方程解的存在性和唯一性

本文主要讨论了由分数布朗运动和Poisson过程驱动的随机微分方程。当方程的系数满足Lipschitz条件和线性增长条件时,给出方程解的存在性和唯一性定理。     

Some aspects of modeling and statistical inference for financial models

Modeling the stock price development as a geometric Brownian motion or, more generally, as a stochastic exponential of a diffusion, requires the use of specific statistical methods. For instance, the observations seldom reach us in the form of a continuous record and we are led to infer about diffusion coefficients from discrete time data. Next, often the classical assumption that the volatility is constant has to be dropped. Instead, a range of various stochastic volatility models is formed by the limiting transition from known volatility models in discrete time towards their continuous time counterparts. These are the main topics of the present survey. It is closed by a quick look beyond the usual Gaussian world in continuous time modeling by allowing a Levy process to be the driving process.     

The asymptotic codifference and covariation of log-fractional stable noise

Many econometric quantities such as long-term risk can be modeled by Pareto-like distributions and may also display long-range dependence. If Pareto is replaced by Gaussian, then one can consider fractional Brownian motion whose increments, called fractional Gaussian noise, exhibit long-range dependence. There are many extensions of that process in the infinite variance stable case. Log-fractional stable noise (log-FSN) is a particularly interesting one. It is a stationary mean-zero stable process with infinite variance, parametrized by a tail index α$\alpha$ between 1 and 2, and hence with heavy tails. The lower the value of α$\alpha$, the heavier the tail of the marginal distributions. The fact that α$\alpha$ is less than 2 renders the variance infinite. Thus dependence between past and future cannot be measured using the correlation. There are other dependence measures that one can use, for instance the “codifference” or the “covariation”. Since log-FSN is a moving average and hence “mixing”, these dependence measures converge to zero as the lags between past and future become very large. The codifference, in particular, decreases to zero like a power function as the lag goes to infinity. Two parameters play an important role: (a) the value of the exponent, which depends on α$\alpha$ and measures the speed of the decay; (b) a multiplicative constant of asymptoticity c$c$ which depends also on α$\alpha$. In this paper, it is shown that for symmetric α$\alpha$-stable log-FSN, the constant c$c$ is positive and that the rate of decay of the codifference is such that one has long-range dependence. It is also proved that the same conclusion holds for the second measure of dependence, the covariation, which converges to zero with the same intensity and with a constant of asymptoticity which is positive as well.     

The valuation of barrier options under a threshold rough Heston model

In this paper, we propose a novel model for pricing double barrier options, where the asset price is modeled as a threshold geometric Brownian motion time changed by an integrated activity rate process, which is driven by the convolution of a fractional kernel with the CIR process. The new model both captures the leverage effect and produces rough paths for the volatility process. The model also nests the threshold diffusion, Heston and rough Heston models. We can derive analytical formulas for the double barrier option prices based on the eigenfunction expansion method. We also implement the model and numerically investigate the sensitivities of option prices with respect to the parameters of the model.     

Econometric estimation in long-range dependent volatility models: Theory and practice

It is commonly accepted that some financial data may exhibit long-range dependence, while other financial data exhibit intermediate-range dependence or short-range dependence. These behaviours may be fitted to a continuous-time fractional stochastic model. The estimation procedure proposed in this paper is based on a continuous-time version of the Gauss–Whittle objective function to find the parameter estimates that minimize the discrepancy between the spectral density and the data periodogram. As a special case, the proposed estimation procedure is applied to a class of fractional stochastic volatility models to estimate the drift, standard deviation and memory parameters of the volatility process under consideration. As an application, the volatility of the Dow Jones, S&P 500, CAC 40, DAX 30, FTSE 100 and NIKKEI 225 is estimated.     

分数布朗运动下信用风险首次触发范式

在假设公司资产价值的波动以真实概率服从分数布朗运动的条件下,建立信用风险首次触发范式。     

Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading

With the rapid growth of carbon trading, the development of carbon financial derivatives such as carbon options has become inevitable. This paper established a model based on GARCH and fractional Brownian motion (FBM), hoping to provide reference for China's upcoming carbon option trading through carbon option price forecasting research. The fractal characteristic of carbon option prices indicates that it is reasonable to use FBM to predict option prices. The GARCH model can make up for the lack of fixed FBM volatility. In this paper, the daily closing prices of EUA option contracts on the European Energy Exchange are selected as samples for price prediction. The GARCH model was used to determine the return volatility, and then the FBM was used to calculate the forecast price for the next 60 days. The results showed that the predicted price can better fit the actual price. This paper further compares the price prediction results of this model with the other three models through line graphs and error evaluation indicators such as MAPE, MAE and MSE. It is confirmed that the prediction results of the model in this paper is the closest to the actual price.     

BitTorrent协议流的鉴别及自相似性评价

在近年进行的对一些对等网络流量特性的研究中发现，传统的马尔科夫模型结果与实际的网络性能有较大差异，而这些实际的对等网络流量特性满足网络流量自相似性。文章详细分析了BitTorrent；协议的鉴别，以及捕获BitTorrent流的方法，在此基础上分析了BitTorrent的自相似性问题。     

A new look at variance estimation based on low,high and closing prices taking into account the drift

The joint distribution of low, high and closing prices of the arithmetic Brownian motion is used to evaluate the properties of the most popular estimators of the variance constructed on the basis of high, low and closing prices. The expected values and mean square errors of the Parkinson, Garman–Klass and Rogers–Satchell estimators for the process with a zero drift and a non‐zero drift are derived. Moreover, new volatility estimators, more efficient in the majority of financial applications than the Rogers–Satchell estimator, are proposed. The considered estimators are applied to the estimation of the volatility of the Polish stock index WIG20.     

Joint dynamic modeling and option pricing in incomplete derivative-security market

In this study, we evaluate the option prices on two assets under stochastic interest rates when the stochastic process that underlying asset prices follow is depending on a correlated bivariate Markov-modulated geometric Brownian motion model with jump risks. More specifically, we conduct the joint dynamic modeling by identifying two independent compound Poisson processes with the log-normal jump sizes to describe both individual jumps and systematic cojumps. Facilitating the cojumping behavior this way with the time-inhomogeneity of the volatility, option pricing expressions are readily obtainable since the Gerber–Siu’s approach is employed to determine a pricing kernel. The empirical results and numerical illustrations are provided to show the impact of cojumps and stochastic volatilities on option prices.     

The one-sided Kolmogorov-Smirnov test in signal detection problems with Gaussian white noise

In this paper, we investigate the power of the Kolmogorov-Smirnov test for negativity of signals in signal detection problems with Brownian motion noise. Strict unbiasedness and admissibility are established. Moreover, taking up an approach of Häjek and Sidäk (1967) for the classical Brownian bridge case, we compute the directional derivative of the power function at the hypothesis, thus obtaining the principal direction of alternatives of the test. As an application, in a hazard rates set-up, one-sided Rényi-tests may be adjusted by choosing their weight function such that they are most sensitive against a prescribed type of alternatives. Finally, we give some numerical and simulation results.     

Testing the forward volatility unbiasedness hypothesis in exchange rates under long-range dependence

This paper analyses the unbiasedness hypothesis between spot and forward volatility, using both the actual and the continuous path of realised volatility, and focusing on long-memory properties. For this purpose, we use daily realised volatility with jumps for the USD/EUR exchange rate negotiated in the FX market and employ fractional integration and cointegration techniques. Both series have long-range dependence, and so does the error correction term of their long-run relationship. Hence, deviations from equilibrium are highly persistent, and the effects of shocks affecting the long-run relationship dissipate very slowly. While for long-term contracts, there is some empirical evidence that the forward volatility unbiasedness hypothesis does not hold – and, thus, that forward implied volatility is a systematically downward-biased predictor of future spot volatility – for short-term contracts, the evidence is mixed.     

Model-free CPPI

We consider Constant Proportion Portfolio Insurance (CPPI) and its dynamic extension, which may be called Dynamic Proportion Portfolio Insurance (DPPI). It is shown that these investment strategies work within the setting of Föllmer's pathwise Itô calculus, which makes no probabilistic assumptions whatsoever. This shows, on one hand, that CPPI and DPPI are completely independent of any choice of a particular model for the dynamics of asset prices. They even make sense beyond the class of semimartingale sample paths and can be successfully defined for models admitting arbitrage, including some models based on fractional Brownian motion. On the other hand, the result can be seen as a case study for the general issue of robustness in the face of model uncertainty in finance.     

On distribution-free goodness-of-fit testing of exponentiality

There is a need to test the hypothesis of exponentiality against a wide variety of alternative hypotheses, across many areas of economics and finance. Local or contiguous alternatives are the closest alternatives against which it is still possible to have some power. Hence goodness-of-fit tests should have some power against all, or a huge majority, of local alternatives. Such tests are often based on nonlinear statistics, with a complicated asymptotic null distribution. Thus a second desirable property of a goodness-of-fit test is that its statistic will be asymptotically distribution free. We suggest a whole class of goodness-of-fit tests with both of these properties, by constructing a new version of empirical process that weakly converges to a standard Brownian motion under the hypothesis of exponentiality. All statistics based on this process will asymptotically behave as statistics from a standard Brownian motion and so will be asymptotically distribution free. We show the form of transformation is especially simple in the case of exponentiality. Surprisingly there are only two asymptotically distribution free versions of empirical process for this problem, and only this one has a convenient limit distribution. Many tests of exponentiality have been suggested based on asymptotically linear functionals from the empirical process. We illustrate none of these can be used as goodness-of-fit tests, contrary to some previous recommendations. Of considerable interest is that a selection of well-known statistics all lead to the same test asymptotically, with negligible asymptotic power against a great majority of local alternatives. Finally, we present an extension of our approach that solves the problem of multiple testing, both for exponentiality and for other, more general hypotheses.     

Testing the parametric form of the volatility in continuous time diffusion models—a stochastic process approach

We present new tests for the form of the volatility function which are based on stochastic processes of the integrated volatility. We prove weak convergence of these processes to centered processes whose conditional distributions are Gaussian. In the case of testing for a constant volatility the limiting process are standard Brownian bridges. As a consequence an asymptotic distribution free test and bootstrap tests (for testing of a general parametric form) can easily be implemented. It is demonstrated that the new tests are more than the currently available procedures. The new approach is also demonstrated by means of a simulation study.     

A simple proof of a rate of embedding for the partial sum process

A simple proof of Skorokhod embedding of the partial sum process at a rate is given. Nothing more sophisticated than the HBjek-RBnyi inequality, the monotone inequality, the one sided reflection principal for Brownian motion and Mill's ratio is used.