Kavli Affiliate: Feng Wang
| First 5 Authors: Peng Yang, Junji Ren, Feng Wang, Ke Tang,
| Summary:
The fidelity of financial market simulation is restricted by the so-called
"non-identifiability" difficulty when calibrating high-frequency data. This
paper first analyzes the inherent loss of data information in this difficulty,
and proposes to use the Kolmogorov-Smirnov test (K-S) as the objective function
for high-frequency calibration. Empirical studies verify that K-S has better
identifiability of calibrating high-frequency data, while also leads to a much
harder multi-modal landscape in the calibration space. To this end, we propose
the adaptive stochastic ranking based negatively correlated search algorithm
for improving the balance between exploration and exploitation. Experimental
results on both simulated data and real market data demonstrate that the
proposed method can obtain up to 36.0% improvement in high-frequency data
calibration problems over the compared methods.
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