OptimalCuttingFrequency

OptimalCuttingFrequency computes the optimal cutting frequency for the Fourier estimator of integrated variance

Syntax

  • Nopt=OptimalCuttingFrequency(x,t)example

Description

OptimalCuttingFrequency computes the optimal cutting frequency for running the Fourier estimator of the integrated variance on noisy timeseries data. Note that this function calls function autocorr which needs the Econometric toolbox.

example

Nopt =OptimalCuttingFrequency(x, t) Computation of the optimal cutting frequency.

Examples

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  • Computation of the optimal cutting frequency.
  • Optimal cutting frequency for estimating the integrated variance from a vector x of noisy observations of a univariate diffusion process.

    % Generate data.
    n=1000;
    dt=1/n;
    t=0:dt:1;
    x=randn(n,1)*sqrt(dt);
    % generate the diffusion process
    x=[0;cumsum(x)];
    % Add noise: noise-to-signal ratio
    noise_to_signal =0.5; 
    sigma_eps = noise_to_signal*std(diff(x));
    noise=sigma_eps*randn(size(x));
    % add noise, which is i.i.d. N(0,sigma_eps^2)
    x=x+noise;
    Nopt = OptimalCuttingFrequency(x,t); % optimal cutting frequency
    ivar=FE_int_vol(x,t,'N',Nopt);
    disp(['The optimal cutting frequency is: ' num2str(Nopt)])
    disp(['The value of the integrated variance is: ' num2str(ivar)])
    The optimal cutting frequency is: 168
    The value of the integrated variance is: 1.1166
    

    Input Arguments

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    x — Observation values. Vector.

    Row or column vector containing the observed values.

    Data Types: single| double

    t — Observation times. Vector.

    Row or column vector with the same length of x containing the observation times

    Data Types: single| double

    Output Arguments

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    Nopt —Optimal cutting frequency. Scalar

    Integer representing the optimal cutting frequency.

    More About

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    Additional Details

    We assume our timeseries data are noisy observations $\tilde x$ from a diffusion process following the Ito stochastic differential equation $$dx(t)= \sigma(t) \ dW(t) + b(t) \ dt,$$ where $W$ is a Brownian motion on a filtered probability space. Let $\sigma$ and $b$ be random processes, adapted to the Brownian filtration.

    The integrated variance of the process over the time interval $[0,T]$ is defined as $$\int_0^T \sigma^2(t) dt.$$ For any positive integer $n$, let ${\cal S}_{n}:=\{ 0=t_{0}\leq \cdots \leq t_{n}=T \}$ be the observation times.

    The observations are affected by i.i.d. noise terms $\eta(t_i)$ with mean zero and finite variance $$\tilde x(t_i)=x(t_i)+\eta(t_i).$$ See the Reference for further mathematical details.

    Moreover, let $\delta_i(\tilde x):= \tilde x(t_{i+1})-\tilde x(t_i)$ be the increments of $\tilde x$.

    The optimal cutting frequency $N$ for computing the Fourier estimator of the integrated variance from noisy timeseries data is obtained by minimization of the estimated MSE.

    The Fourier estimator of the integrated variance over $[0,T]$, is then defined as $$\widehat\sigma^{2}_{n,N}:= {T^2 \over {2N+1}}\sum_{|s|\leq N} c_s(d\tilde x_n) c_{-s}(d\tilde x_n),$$ where for any integer $k$, $|k|\leq N$, the discretized Fourier coefficients of the increments are $$c_k(d\tilde x_{n}):= {1\over {T}} \sum_{i=0}^{n-1} e^{-{\rm i} {{2\pi}\over {T}} kt_i}\delta_i(\tilde x).$$

    References

    Mancino, M.E., Recchioni, M.C., Sanfelici, S. (2017), Fourier-Malliavin Volatility Estimation. Theory and Practice, "Springer Briefs in Quantitative Finance", Springer.

    See Also

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