FORECASTING VOLATILITY WITH THE MULTIFRACTAL RANDOM WALK MODEL

By J. Duchon, R. Robert, Vincent Vargas. 2008

57 citations recorded by the source corpus.

We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill‐posed problem of estimating the correlation length T of the model, we introduce a limiting object defined in a quotient space; formally, this object is an infinite range log volatility. For this object and the nonlimiting object, we obtain precise prediction formulas and we apply them to the problem of forecasting volatility and pricing options with the MRW model in the absence of a reliable estimate of σ and T.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f09dfa9faf72e026b24cbeb46390fbab4bc6a5b2

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Citations in the corpus

Gaussian multiplicative chaos revisited (2008, background): “Volatility modeling and forecasting is an important field of finance since it is related to option pricing and risk forecasting: we refer to [ 8 ] for the problem of forecasting volatility with this choice of m.”
Volatility is rough (2014, unknown): “Remark that our predictor can be linked to that of [21], where the issue of the prediction of the log-volatility in the multifractal random walk model of [5] is tackled.”
Gaussian multiplicative chaos and applications: A review (2013, background): “Motivated by financial applications where the correlation length T is too big to be measured on markets, the authors of [50] adressed the issue of forecasting volatility Mγ in the limit T → ∞.”