arXiv subjects

Ioannis P. Antoniades

Publications and source records attributed to Ioannis P. Antoniades.

3 recordsLinked to original sources

arXiv2021

Modeling the dynamical behavior of memristive {NiTi} alloy at constant stress for time-varying electric current input signals

The dynamical electric behavior of a NiTi smart alloy thin filament when driven by time varying current pulses is studied by a structure-based phenomenological model that includes rate-based effects. The simulation model relates the alloy's electrical resistivity to the relative proportions of the three main structural phases namely Martensite, Austenite and R-phase, experimentally known to exist in NiTi alloy lattice structure. The relative proportions of the phases depend on temperature and applied stress. Temperature varies due to the self-heating of the filament by the Joule effect when a current pulse passes and also due to convective/radiative interchange with the ambient. The temperature variation with time causes structural phase transitions, which result in abrupt changes in the sample resistivity as the proportions of each lattice phase vary. The model is described by a system of four 1st-order nonlinear differential-algebraic equations yielding the temporal evolution of resistivity and output voltage across the filament for any given time-varying input current pulse. The model corresponds to a 4th-order extended memristor, described by four state variables, which are the proportions of each of the three NiTi lattice phases and temperature. Simulations are experimentally verified by comparing to measurements obtained for samples self-heated by triangular current input waveforms as well as for passively samples with no current input. Numerical results reproduce very well measurements of resistance vs. temperature at equilibrium as well as the full dynamics of experimentally observed I-V characteristic curves and resistance vs. driving current for time-varying current input waveforms of a wide range of frequencies (0.01-10~Hz).

cond-mat.mtrl-sci↗

arXiv2021

Dynamical Characteristics of Global Stock Markets Based on Time Dependent Tsallis Non-Extensive Statistics and Generalized Hurst Exponents

We perform non-linear analysis on stock market indices using time-dependent extended Tsallis statistics. Specifically, we evaluate the q-triplet for particular time periods with the purpose of demonstrating the temporal dependence of the extended characteristics of the underlying market dynamics. We apply the analysis on daily close price timeseries of four major global markets (S&P 500, Tokyo-NIKKEI, Frankfurt-DAX, London-LSE). For comparison, we also compute time-dependent Generalized Hurst Exponents (GHE) Hq using the GHE method, thus estimating the temporal evolution of the multiscaling characteristics of the index dynamics. We focus on periods before and after critical market events such as stock market bubbles (2000 dot.com bubble, Japanese 1990 bubble, 2008 US real estate crisis) and find that the temporal trends of q-triplet values significantly differ among these periods indicating that in the rising period before a bubble break, the underlying extended statistics of the market dynamics strongly deviates from purely stochastic behavior, whereas, after the breakdown, it gradually converges to the Gaussian-like behavior which is a characteristic of an efficient market. We also conclude that relative temporal variation patterns of the Tsallis q-triplet can be connected to different aspects of market dynamics and reveals useful information about market conditions especially those underlying the development of a stock market bubble. We found specific temporal patterns and trends in the relative variation of the indices in the q-triplet that distinguish periods just before and just after a stock-market bubble break. Differences between endogenous and exogenous stock market crises are also captured by the temporal changes in the Tsallis q-triplet. Finally, we introduce two new time-dependent empirical metrics (Q-metrics) that are functions of the Tsallis q-triplet.

q-fin.ST↗

arXiv2020

The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), $H_q$, for various values of the parameter $q$. Using $H_q$, we introduce a new visual methodology to algorithmically detect critical changes in the scaling of the underlying complex time-series. The methodology involves the degree of multiscaling at a particular time instance, the multiscaling trend which is calculated by the Change-Point Analysis method, and a rigorous evaluation of the statistical significance of the results. Using this algorithm, we have identified particular patterns in the temporal co-evolution of the different $H_q$ time-series. These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A). We apply the visual methodology to time-series comprising of daily close prices of four stock market indices: two major ones (S\&P~500 and NIKKEI) and two peripheral ones (Athens Stock Exchange general Index and Bombay-SENSEX). Results show that multiscaling varies greatly with time: time periods of strong multiscaling behavior and time periods of uniscaling behavior are interchanged while transitions from uniscaling to multiscaling behavior occur before critical market events, such as stock market bubbles. Moreover, particular asymmetric multiscaling patterns appear during critical stock market eras and provide useful information about market conditions. In particular, they can be used as 'fingerprints' of a turbulent market period as well as provide warning signals for an upcoming stock market 'bubble'. The applied visual methodology also appears to distinguish between exogenous and endogenous stock market crises, based on the observed patterns before the actual events.

q-fin.ST↗