I am wondering if Vector Autoregression (and other autoregressive models) is a sound modelling for the daily (not high-frequency!) log-returns of time series from liquid financial markets.

One can find through google scholar or in many books on financial time series analysis this kind of approach.

However, partly from my practitioner experience, I am dubious about the relevance of such modelling. One can also read in Empirical properties of asset returns: stylized facts and statistical issues:

Mandelbrot [85] expressed this property by stating that ‘arbitrage tends to whiten the spectrum of price changes’. This property implies that traditional tools of signal processing which are based on second-order properties, in the time domain—autocovariance analysis, ARMA modelling—or in the spectral domain— Fourier analysis, linear filtering—cannot distinguish between asset returns and white noise. This points out the need for nonlinear measures of dependence in order to characterize the dependence properties of asset returns.

I am thus looking for personal experience of (un)success with such autoregressive modelling, and authoritative paper with in-depth experimental analysis on real market data.

  • $\begingroup$ I can agree with the citation. Daily returns on liquid financial assets are not likely to have autocorrelations, and even if they exist, they will not be present for periods of considerable length. The pattern would be too simple for the traders to not notice and not exploit. $\endgroup$ Commented Feb 25, 2016 at 20:59


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