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.     

Correlations and multi-affinity in high frequency financial datasets

In this paper we perform a quantitative check of long term correlations and multi-affinity in Deutsche Mark/US Dollar exchange rates using high frequency data. We show that the use of business time, i.e., the ranking of the quotes in the sequences, eliminates most of the seasonality in financial-time series, allowing a precise estimation of some return anomalies.     

Spectral analysis informs the proper frequency in the sampling of financial time series data

Applied econometricians tend to show a long neglect for the proper frequency to be considered while sampling the time series data. The present study shows how spectral analysis can be usefully employed to fix this problem. The case is illustrated with ultra-high-frequency data and daily prices of four selected stocks listed on the Sao Paulo stock exchange.     

Time-dependent effects in high viscosity fluid dynamics

Flows dominated by high viscosity are often described using the steady Stokes equation. We will discuss three situations where the limits that are needed in order to arrive at this representation are questionable, and will show that unexpected flow behaviour arises: (i) under the influence of a time-dependent force, flows need not be reversible and chaotic advection can arise; (ii) the flow field around an oscillating sphere becomes that of the steady flow only for slow motion and sufficiently close to the sphere; (iii) the time-dependence in the fluid motion modifies the response of a sphere to an oscillatory force; (iv) when these forces are taken into account, the limits of particle size to zero and observation time to infinity do not commute and small particles do not maintain a uniform distribution.     

Forbidden patterns in financial time series

The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, such as Lyapunov exponent or Kolmogorov entropy, thus allowing to separate deterministic (usually chaotic) from random series; however, it requires fewer values of the series to be calculated, and it is suitable for using with small datasets. In this paper, the appearance of forbidden patterns is studied in different economical indicators such as stock indices (Dow Jones Industrial Average and Nasdaq Composite), NYSE stocks (IBM and Boeing), and others (ten year Bond interest rate), to find evidence of deterministic behavior in their evolutions. Moreover, the rate of appearance of the forbidden patterns is calculated, and some considerations about the underlying dynamics are suggested.     

Dependence structure of the Korean stock market in high frequency data

This paper analyzes the evolution of the dependence structure for various time window intervals, known as Epps effect, using the Trade and Quote data of 663 actively traded stocks in Korean stock market. It is found that the random matrix theory analysis could not represent the dependence structure of the stock market in the microstructure regime. The Cook-Johnson copula is introduced as a parsimonious alternative method to handle this problem, and the existence of the Epps effect is confirmed for the 663 stocks using high frequency data. It was also found that large capitalization companies tend to have a stronger dependence structure, except for the largest capitalization group, since the phenomenon of price level resistance leads to the weak dependence structure in the largest capitalization group. In addition, grouping the industry as a sub-portfolio is an appropriate approach for hour interval traders, whereas this approach is not a strategy recommended for high frequency traders.     

The magnetization-energy scaling limit in high dimension

In the single-phase region (including the critical point) of a nearest-neighbor Ising ferromagnet with zero external field, the block magnetization and energy within the infinite-volume system are, asymptotically for large block size, independent Gaussian variables when the dimensiond exceeds four. For other models, including ones with long-range interactions, a sufficient condition for such triviality of the scaling limit is finiteness of the bubble quantity.On leave from the University of Arizona, Tucson, Arizona 85721.     

Ising type models applied to Geophysics and high frequency market data

The classical Ising model was used to re-create the ferromagnetic phenomenon in statistical mechanics. The model describes the behavior of atoms in a lattice. Each atom may interact only with its neighbors, and has two states called spins. When the atoms polarize their spins, the resulting material exhibits a net magnetization. A similar model has been used before in financial math: the spins correspond to the buy/sell position of a trader and the polarization is equivalent with all the traders in the market wanting to sell. This leads to a market crash. In this work, we present extensions and applications to geophysics and high frequency market data.     

Evidence for criticality in financial data

The European Physical Journal B - We study the temperature dependencies of the mean sizes of the Cooper pairs in a two-band BCS-type s-wave superconductivity model with coupling cut-off in the...     

Long memory features in the high frequency data of the Korean stock market

This paper examines the long memory property in the high frequency data of KOSPI 200 using the FIAPARCH model. The empirical results indicate that the FIAPARCH model can capture asymmetry and long memory in the volatility of intraday KOSPI 200 returns. Interestingly, the presence of long memory is invariant to the temporally aggregated intraday returns, implying that a long memory phenomenon is an inherent characteristic of the data generating process, not a result of structural breaks.     

Gauge invariant lattice quantum field theory: Implications for statistical properties of high frequency financial markets

We report on initial studies of a quantum field theory defined on a lattice with multi-ladder geometry and the dilation group as a local gauge symmetry. The model is relevant in the cross-disciplinary area of econophysics. A corresponding proposal by Ilinski aimed at gauge modeling in non-equilibrium pricing is implemented in a numerical simulation. We arrive at a probability distribution of relative gains which matches the high frequency historical data of the NASDAQ stock exchange index.     

Small scale behavior of financial data

A new approach is presented to describe the change in the statistics of the log return distribution of financial data as a function of the timescale. To this purpose a measure is introduced, which quantifies the distance of a considered distribution to a reference distribution. The existence of a small timescale regime is demonstrated, which exhibits different properties compared to the normal timescale regime for timescales larger than one minute. This regime seems to be universal for individual stocks. It is shown that the existence of this small timescale regime is not dependent on the special choice of the distance measure or the reference distribution. These findings have important implications for risk analysis, in particular for the probability of extreme events.     

Volatility clustering and scaling for financial time series due to attractor bubbling

A microscopic model of financial markets is considered, consisting of many interacting agents (spins) with global coupling and discrete-time heat bath dynamics, similar to random Ising systems. The interactions between agents change randomly in time. In the thermodynamic limit, the obtained time series of price returns show chaotic bursts resulting from the emergence of attractor bubbling or on-off intermittency, resembling the empirical financial time series with volatility clustering. For a proper choice of the model parameters, the probability distributions of returns exhibit power-law tails with scaling exponents close to the empirical ones.     

Temperature-dependent scaling of pinning force data in Bi-based high-Tc superconductors

The scaling of the normalized volume pinning forces, Fp/Fp,max, versus a reduced field h=Ha/Hscale has proven to be a very informative tool to study the origin of the flux pinning in superconductors. Remarkably, on Bi2Sr2Ca2Cu3O10+ (Bi-2223) and Bi2Sr2CaCu2O8+ (Bi-2212) data were mostly analyzed only in a narrow temperature range around 77 K. Here, we present a study of the pinning forces in various Bi-2223 samples at temperatures between 18 K and 80 K. The measurements clearly reveal that there is an apparent non-scaling of the pinning force data; instead, two clearly different temperature regimes for the scaling can be recognized, which are found to be in direct relation to a second step observed in the m(T) curves obtained upon field-cooling and -warming.