1,468 research outputs found
Analysis of Binarized High Frequency Financial Data
A non-trivial probability structure is evident in the binary data extracted
from the up/down price movements of very high frequency data such as
tick-by-tick data for USD/JPY. In this paper, we analyze the Sony bank USD/JPY
rates, ignoring the small deviations from the market price. We then show there
is a similar non-trivial probability structure in the Sony bank rate, in spite
of the Sony bank rate's having less frequent and larger deviations than
tick-by-tick data. However, this probability structure is not found in the data
which has been sampled from tick-by-tick data at the same rate as the Sony bank
rate. Therefore, the method of generating the Sony bank rate from the market
rate has the potential for practical use since the method retains the
probability structure as the sampling frequency decreases.Comment: 8pages, 4figures, contribution to the 3rd International Conference
NEXT-SigmaPh
Complex Correlation Approach for High Frequency Financial Data
We propose a novel approach that allows to calculate Hilbert transform based
complex correlation for unevenly spaced data. This method is especially
suitable for high frequency trading data, which are of a particular interest in
finance. Its most important feature is the ability to take into account
lead-lag relations on different scales, without knowing them in advance. We
also present results obtained with this approach while working on Tokyo Stock
Exchange intraday quotations. We show that individual sectors and subsectors
tend to form important market components which may follow each other with small
but significant delays. These components may be recognized by analysing
eigenvectors of complex correlation matrix for Nikkei 225 stocks.
Interestingly, sectorial components are also found in eigenvectors
corresponding to the bulk eigenvalues, traditionally treated as noise
Anomalous volatility scaling in high frequency financial data
Volatility of intra-day stock market indices computed at various time
horizons exhibits a scaling behaviour that differs from what would be expected
from fractional Brownian motion (fBm). We investigate this anomalous scaling by
using empirical mode decomposition (EMD), a method which separates time series
into a set of cyclical components at different time-scales. By applying the EMD
to fBm, we retrieve a scaling law that relates the variance of the components
to a power law of the oscillating period. In contrast, when analysing 22
different stock market indices, we observe deviations from the fBm and Brownian
motion scaling behaviour. We discuss and quantify these deviations, associating
them to the characteristics of financial markets, with larger deviations
corresponding to less developed markets.Comment: 25 pages, 11 figure, 5 table
Testing the Markov property with ultra-high frequency financial data
This paper develops a framework to nonparametrically test whether discretevalued irregularly-spaced financial transactions data follow a Markov process. For that purpose, we consider a specific optional sampling in which a continuous-time Markov process is observed only when it crosses some discrete level. This framework is convenient for it accommodates not only the irregular spacing of transactions data, but also price discreteness. Under such an observation rule, the current price duration is independent of previous price durations given the current price realization. A simple nonparametric test then follows by examining whether this conditional independence property holds. Finally, we investigate whether or not bid-ask spreads follow Markov processes using transactions data from the New York Stock Exchange. The motivation lies on the fact that asymmetric information models of market microstructures predict that the Markov property does not hold for the bid-ask spread. The results are mixed in the sense that the Markov assumption is rejected for three out of the five stocks we have analyzed.Bid-ask spread, nonparametric testing, price durations, Markov property, ultra-high frequency data
- …