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Sökning: WFRF:(Hautsch Nikolaus)

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1.
  • Bodnar, Taras, et al. (författare)
  • Dynamic Conditional Correlation Multiplicative Error Processes
  • 2016
  • Ingår i: Journal of Empirical Finance. - : Elsevier BV. - 0927-5398 .- 1879-1727. ; 36, s. 41-67
  • Tidskriftsartikel (refereegranskat)abstract
    • We introduce a dynamic model for multivariate processes of (non-negative) high-frequency tradingvariables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model, we map the resulting residuals into aGaussian domain using a copula-type transformation. Based on high-frequency volatility, cumulativetrading volumes, trade counts and market depth of various stocks traded at the NYSE, we show thatthe proposed transformation is supported by the data and allows capturing (multivariate) dynamicsin higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficientlyflexible to be applicablein high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in tradingprocesses supports the usefulness of the approach. Taking these higher-order dynamics explicitlyinto account significantly improves the goodness-of-fit and out-of-sample forecasts of the multiplicative error model.
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2.
  • Menkveld, Albert J., et al. (författare)
  • Nonstandard Errors
  • 2024
  • Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
  • Tidskriftsartikel (refereegranskat)abstract
    • In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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