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arXiv 1305.0342math.PR
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从分数阶布朗运动的随机积分到多重分数阶布朗运动的随机积分

From Stochastic Integration wrt Fractional Brownian Motion to Stochastic Integration wrt Multifractional Brownian Motion

Joachim Lebovits

摘要:

随机积分w.r.t.分数布朗运动(fBM)近年来引起了强烈的兴趣,特别是受到金融和互联网流量建模中的应用的激励。由于fBm不是半鞅,随机积分需要具体的发展。多分数布朗运动(mBm)是一个高斯过程,它通过让局部霍德尔指数随时间变化来推广fBm。这在各个领域都很有用,包括金融建模和生物医学。在这项工作中,我们从事实出发,建立在\cite [Thm 2.1.(i)]{fBm_to_mBm_HerbinLebovitsVehel},mBm在定律上可以由“正切”fBm的序列来近似。我们用这个结果来说明如何可以定义一个随机积分w.r.t. mBm从随机积分w.r.t. fBm,在\cite{Ben1}中定义,在白噪声理论意义上。

英文摘要:

Stochastic integration w.r.t. fractional Brownian motion (fBm) has raised strong interest in recent years, motivated in particular by applications in finance and Internet traffic modelling. Since fBm is not a semi-martingale, stochastic integration requires specific developments. Multifractional Brownian motion (mBm) is a Gaussian process that generalizes fBm by letting the local Hölder exponent vary in time. This is useful in various areas, including financial modelling and biomedicine. In this work we start from the fact, established in \cite[Thm 2.1.(i)]{fBm_to_mBm_HerbinLebovitsVehel}, that an mBm may be approximated, in law, by a sequence of "tangent" fBms. We used this result to show how one can define a stochastic integral w.r.t. mBm from the stochastic integral w.r.t. fBm, defined in \cite{Ben1}, in the White Noise Theory sense.

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