Patterns in high-frequency FX data: Discovery of 12 empirical scaling laws

We have discovered 12 independent new empirical scaling laws in foreign exchange data-series that hold for close to three orders of magnitude and across 13 currency exchange rates. Our statistical analysis crucially depends on an event-based approach that measures the relationship between different types of events. The scaling laws give an accurate estimation of the length of the price-curve coastline, which turns out to be surprisingly long. The new laws substantially extend the catalogue of stylised facts and sharply constrain the space of possible theoretical explanations of the market mechanisms.Comment: 26 pages, 3 figures, 23 tables,2nd version (text made more concise and readable, algorithm pseudocode, results unchanged), 5-year datasets (USD-JPY, EUR-USD) provided at http://www.olsen.ch/more/datasets

Distinct magnetotransport and orbital fingerprints of chiral bobbers

While chiral magnetic skyrmions have been attracting significant attention in the past years, recently, a new type of a chiral particle emerging in thin films $-$ a chiral bobber $-$ has been theoretically predicted and experimentally observed. Here, based on theoretical arguments, we provide a clear pathway to utilizing chiral bobbers for the purposes of future spintronics by uncovering that these novel chiral states possess inherent transport fingerprints that allow for their unambiguous electrical detection in systems comprising several types of chiral states. We reveal that unique transport and orbital characteristics of bobbers root in the non-trivial magnetization distribution in the vicinity of the Bloch points, and demonstrate that tuning the details of the Bloch point topology can be used to drastically alter the emergent response properties of chiral bobbers to external fields, which bears great potential for engineering chiral dynamics and cognitive computing.Comment: Supplementary available upon reques

Scaling and memory of intraday volatility return intervals in stock market

We study the return interval $\tau$ between price volatilities that are above a certain threshold $q$ for 31 intraday datasets, including the Standard & Poor's 500 index and the 30 stocks that form the Dow Jones Industrial index. For different threshold $q$, the probability density function $P_q(\tau)$ scales with the mean interval $\bar{\tau}$ as $P_q(\tau)={\bar{\tau}}^{-1}f(\tau/\bar{\tau})$, similar to that found in daily volatilities. Since the intraday records have significantly more data points compared to the daily records, we could probe for much higher thresholds $q$ and still obtain good statistics. We find that the scaling function $f(x)$ is consistent for all 31 intraday datasets in various time resolutions, and the function is well approximated by the stretched exponential, $f(x)\sim e^{-a x^\gamma}$, with $\gamma=0.38\pm 0.05$ and $a=3.9\pm 0.5$, which indicates the existence of correlations. We analyze the conditional probability distribution $P_q(\tau|\tau_0)$ for $\tau$ following a certain interval $\tau_0$, and find $P_q(\tau|\tau_0)$ depends on $\tau_0$, which demonstrates memory in intraday return intervals. Also, we find that the mean conditional interval $$ increases with $\tau_0$, consistent with the memory found for $P_q(\tau|\tau_0)$. Moreover, we find that return interval records have long term correlations with correlation exponents similar to that of volatility records.Comment: 19 pages, 8 figure

Age of the Peach Springs Tuff, Southeastern California and Western Arizona

Sanidine separates from pumice of the early Miocene Peach Springs Tuff are concordantly dated at 18.5 ± 0.2 Ma by two isotopic techniques. The Peach Springs Tuff is the only known unit that can be correlated between isolated outcrops of Miocene strata from the central Mojave Desert of southeastern California to the western Colorado Plateau in Arizona, across five structural provinces, a distance of 350 km. Thus the age of the Peach Springs Tuff is important to structural and paleogeographic reconstructions of a large region. Biotite and sanidine separates from bulk samples of the Peach Springs Tuff from zones of welding and vapor-phase alteration have not produced consistent ages by the K-Ar method. Published ages of mineral separates from 17 localities ranged from 16.2 to 20.5 Ma. Discordant 40Ar/39Ar incremental release spectra were obtained for one biotite and two of the sanidine separates. Ages that correspond to the last gas increments are as old as 27 Ma. The 40Ar/39Ar incremental release determinations on sanidine separated from blocks of Peach Springs Tuff pumice yield ages of 18.3 ± 0.3 and 18.6 ± 0.4 Ma. Laser fusion measurements yield a mean age of 18.51 ± 0.10. The results suggest that sanidine and biotite K-Ar ages older than about 18.5 Ma are due to inherited Ar from pre-Tertiary contaminants, which likely were incorporated into the tuff during deposition. Sanidine K-Ar ages younger than 18 Ma probably indicate incomplete extraction of radiogenic 40Ar, whereas laser fusion dates of biotite and hornblende younger than 18 Ma likely are due to postdepositional alteration. Laser fusion ages as high as 19.01 Ma on biotite grains from pumice suggest that minerals from pre-Tertiary country rocks also were incorporated in the magma chamber

Zn(O, S) layers for chalcoyprite solar cells sputtered from a single target

A simplified Cu(In, Ga)(S, Se)2/Zn(O, S)/ZnO:Al stack for chalcopyrite thin- film solar cells is proposed. In this stack the Zn(O, S) layer combines the roles of the traditional CdS buffer and undoped ZnO layers. It will be shown that Zn(O, S) films can be sputtered in argon atmosphere from a single mixed target without substrate heating. The photovoltaic performance of the simplified stack matches that of the conventional approach. Replacing the ZnO target with a ZnO/ZnS target may therefore be sufficient to omit the CdS buffer layer and avoid the associated complexity, safety and recycling issues, and to lower production cost

Scaling of the distribution of fluctuations of financial market indices

We study the distribution of fluctuations over a time scale $\Delta t$ (i.e., the returns) of the S&P 500 index by analyzing three distinct databases. Database (i) contains approximately 1 million records sampled at 1 min intervals for the 13-year period 1984-1996, database (ii) contains 8686 daily records for the 35-year period 1962-1996, and database (iii) contains 852 monthly records for the 71-year period 1926-1996. We compute the probability distributions of returns over a time scale $\Delta t$, where $\Delta t$ varies approximately over a factor of 10^4 - from 1 min up to more than 1 month. We find that the distributions for $\Delta t \leq$ 4 days (1560 mins) are consistent with a power-law asymptotic behavior, characterized by an exponent $\alpha \approx 3$, well outside the stable L\'evy regime $0 < \alpha < 2$. To test the robustness of the S&P result, we perform a parallel analysis on two other financial market indices. Database (iv) contains 3560 daily records of the NIKKEI index for the 14-year period 1984-97, and database (v) contains 4649 daily records of the Hang-Seng index for the 18-year period 1980-97. We find estimates of $\alpha$ consistent with those describing the distribution of S&P 500 daily-returns. One possible reason for the scaling of these distributions is the long persistence of the autocorrelation function of the volatility. For time scales longer than $(\Delta t)_{\times} \approx 4$ days, our results are consistent with slow convergence to Gaussian behavior.Comment: 12 pages in multicol LaTeX format with 27 postscript figures (Submitted to PRE May 20, 1999). See http://polymer.bu.edu/~amaral/Professional.html for more of our work on this are

Gaseous argon time projection chamber with electroluminescence enhanced optical readout

Systematic uncertainties in accelerator oscillation neutrino experiments arise mostly from nuclear models describing neutrino-nucleus interactions. To mitigate these uncertainties, we can study neutrino-nuclei interactions with detectors possessing enhanced hadron detection capabilities at energies below the nuclear Fermi level. Gaseous detectors not only lower the particle detection threshold but also enable the investigation of nuclear effects on various nuclei by allowing for changes in the gas composition. This approach provides valuable insights into the modelling of neutrino-nucleus interactions and significantly reduces associated uncertainties. Here, we discuss the design and first operation of a gaseous argon time projection chamber optically read. The detector operates at atmospheric pressure and features a single stage of electron amplification based on a thick GEM. Here, photons are produced with wavelengths in the vacuum ultraviolet regime. In an optical detector the primary constraint is the light yield. This study explores the possibility of increasing the light yield by applying a low electric field downstream of the ThGEM. In this region, called the electroluminescence gap, electrons propagate and excite the argon atoms, leading to the subsequent emission of photons. This process occurs without any further electron amplification, and it is demonstrated that the total light yield increases up to three times by applying moderate electric fields of the order of 3~kV/cm. Finally, an indirect method is discussed for determining the photon yield per charge gain of a ThGEM, giving a value of 18.3 photons detected per secondary electron

Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations

While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price fluctuations. We propose and study a model of an artificial stock market consisting of heterogeneous agents with DRA, and we find that DRA is the main driving force for excess price fluctuations and the associated volatility clustering. We employ a popular power utility function, $U(c,\gamma)=\frac{c^{1-\gamma}-1}{1-\gamma}$ with agent specific and time-dependent risk aversion index, $\gamma_i(t)$, and we derive an approximate formula for the demand function and aggregate price setting equation. The dynamics of each agent's risk aversion index, $\gamma_i(t)$ (i=1,2,...,N), is modeled by a bounded random walk with a constant variance $\delta^2$. We show numerically that our model reproduces most of the ``stylized'' facts observed in the real data, suggesting that dynamic risk aversion is a key mechanism for the emergence of these stylized facts.Comment: 17 pages, 7 figure