64,142 research outputs found
Fundamental Framework for Technical Analysis
Starting from the characterization of the past time evolution of market
prices in terms of two fundamental indicators, price velocity and price
acceleration, we construct a general classification of the possible patterns
characterizing the deviation or defects from the random walk market state and
its time-translational invariant properties. The classification relies on two
dimensionless parameters, the Froude number characterizing the relative
strength of the acceleration with respect to the velocity and the time horizon
forecast dimensionalized to the training period. Trend-following and contrarian
patterns are found to coexist and depend on the dimensionless time horizon. The
classification is based on the symmetry requirements of invariance with respect
to change of price units and of functional scale-invariance in the space of
scenarii. This ``renormalized scenario'' approach is fundamentally
probabilistic in nature and exemplifies the view that multiple competing
scenarii have to be taken into account for the same past history. Empirical
tests are performed on on about nine to thirty years of daily returns of twelve
data sets comprising some major indices (Dow Jones, SP500, Nasdaq, DAX, FTSE,
Nikkei), some major bonds (JGB, TYX) and some major currencies against the US
dollar (GBP, CHF, DEM, JPY). Our ``renormalized scenario'' exhibits
statistically significant predictive power in essentially all market phases. In
constrast, a trend following strategy and trend + acceleration following
strategy perform well only on different and specific market phases. The value
of the ``renormalized scenario'' approach lies in the fact that it always finds
the best of the two, based on a calculation of the stability of their predicted
market trajectories.Comment: Latex, 27 page
Robust Model Selection in Dynamic Models with an Application to Comparing Predictive Accuracy
A model selection procedure based on a general criterion function, with an example of the Kullback-Leibler Information Criterion (KLIC) using quasi-likelihood functions, is considered for dynamic non-nested models. We propose a robust test which generalizes Lien and Vuong's (1987) test with a Heteroscadasticity/Autocorrelation Consistent (HAC) variance estimator. We use the fixed-b asymptotics developed in Kiefer and Vogelsang (2005) to improve the asymptotic approximation to the sampling distribution of the test statistic. The fixed-b approach is compared with a bootstrap method and the standard normal approximation in Monte Carlo simulations. The fixed-b asymptotics and the bootstrap method are found to be markedly superior to the standard normal approximation. An empirical application for foreign exchange rate forecasting models is presented.
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