A time series can be decomposed into two sub-series: a magnitude series and a sign series. Here we analyze separately the scaling properties of the magnitude series and the sign series using the increment time series of cardiac interbeat intervals as an example. We find that time series having...

We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent α ≌ 0.9.

It is well known that while daily price returns of financial markets are uncorrelated, their absolute values (‘volatility’) are long-term correlated. Here we provide evidence that certain subsequences of the returns themselves also exhibit long-term memory. These subsequences consist of...

Using detrended fluctuation analysis, we study the scaling properties of the volatility time series Vi=|Ti+1−Ti| of daily temperatures Ti for 10 chosen sites around the globe. We find that the volatility is long-range power-law correlated with an exponent γ close to 0.8 for all sites...

This manuscript is a brief summary of a talk designed to address the question of whether two of the pillars of the field of phase transitions and critical phenomena—scale invariance and universality—can be useful in guiding research on interpreting empirical data on economic fluctuations....

In recent years, physicists have started applying concepts and methods of statistical physics to study economic problems. The word “Econophysics” is sometimes used to refer to this work. Much recent work is focused on understanding the statistical properties of financial time series. One...

Properly estimating correlations and understanding how they change under different economic conditions plays a key role in asset pricing models, risk management, and many econometric models. In this paper we introduce a robust framework to identify a meaningful correlation relationship, address...