This paper investigates multiscaling in the rough Bergomi model, finding it primarily due to fat-tailed returns.
problem Understanding multiscaling in the rough Bergomi model to improve financial modelling and risk management.
method Introducing a two-stage statistical testing procedure: first, testing for multiscaling against uniscaling; second, using shuffled surrogates to preserve return distributions.
result Multiscaling in the rough Bergomi model arises primarily from fat-tailed return distributions, not memory effects.
Study finds nonlinear relationship between stock correlation and multiscaling indicator.
problem Understanding the relationship between stock correlation and multiscaling in financial markets.
method Investigated the relationship between an indicator of multiscaling and stock correlation, considering capitalization and kurtosis.
result Observed a robust stylized fact of nonlinear dependence between multiscaling indicator and stock correlation across different markets.
Bitcoin market matures as statistical properties approach mature market indicators.
problem Assessing maturity of Bitcoin market using statistical properties.
method Analysis of 1-minute price changes since 2012, focusing on return distributions, volatility autocorrelation, Hurst exponents, and multiscaling effects.
result Statistical properties of Bitcoin market approach mature market indicators over time.
New visual tool detects financial market changes using multiscaling analysis.
problem Detecting relevant changes in financial time series.
method Time-dependent Generalized Hurst Exponents (GHE) and Change-Point Analysis.
result Identifies patterns distinguishing between uniscaling and multiscaling, and provides warning signals.
An agent-based computational economical toy model for the emergence of money from the initial barter trading, inspired by Menger's postulate that money can spontaneously emerge in a commodity exchange economy, is extensively studied. The model considered, while manageable, is significantly complex, however. It is alrea…
New method estimates multiscaling exponents for financial risk assessment.
problem Estimating multiscaling properties in financial time series.
method Generalized Hurst Exponent (GHE) and RNSGHE method.
result MSVaR method improves VaR forecasts for multiscaling financial data.
We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analysing the multi/uni-scaling behaviour of synthetic time-series with known properties. We use the results from the synthe…
This paper investigates the relationship between price multiscaling and volatility roughness in financial markets.
problem The inability of traditional models to capture financial stylized facts like volatility roughness and multiscaling.
method Simulation experiments and real data analysis using a rough volatility model.
result The rough volatility model fails to reproduce the multiscaling features of real data, indicating a negative interplay between multiscaling and volatility roughness.
We investigate the probability distribution of the return intervals τ between successive 1-min volatilities of two Chinese indices exceeding a certain threshold q. The Kolmogorov-Smirnov (KS) tests show that the two indices exhibit multiscaling behavior in the distribution of τ, which follows a stretched exponent…
The distribution of the return intervals τ between volatilities above a threshold q for financial records has been approximated by a scaling behavior. To explore how accurate is the scaling and therefore understand the underlined non-linear mechanism, we investigate intraday datasets of 500 stocks which consist of …
We study the volatility time series of 1137 most traded stocks in the US stock markets for the two-year period 2001-02 and analyze their return intervals τ, which are time intervals between volatilities above a given threshold q. We explore the probability density function of τ, Pq(τ), assuming a stretched exp…
We propose a simple stochastic volatility model which is analytically tractable, very easy to simulate and which captures some relevant stylized facts of financial assets, including scaling properties. In particular, the model displays a crossover in the log-return distribution from power-law tails (small time) to a Ga…
We investigate the correlation properties of transaction data from the New York Stock Exchange. The trading activity f(t) of each stock displays a crossover from weaker to stronger correlations at time scales 60-390 minutes. In both regimes, the Hurst exponent H depends logarithmically on the liquidity of the stock, me…
Study on Bitcoin transaction flows and holding times, revealing multifractal and power-law distributions.
problem Characterizing the temporal behavior and variability of Bitcoin transactions and holding times.
method Analysis of Bitcoin transaction data, including holding-time distributions, multiscaling, and multifractality.
result Found multifractal and power-law distributions in Bitcoin transaction flows and holding times, with significant variations in holding times.
For many externally driven complex systems neither the noisy driving force, nor the internal dynamics are a priori known. Here we focus on systems for which the time dependent activity of a large number of components can be monitored, allowing us to separate each signal into a component attributed to the external drivi…
In this manuscript, we analytically and numerically study statistical properties of an heteroskedastic process based on the celebrated ARCH generator of random variables whose variance is defined by a memory of qm-exponencial, form (eqm=1x=ex). Specifically, we inspect the self-correlation function o…
In this paper, we use the generalized Hurst exponent approach to study the multi- scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multiscaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measure…
Study on oil price's multifractal cross-correlations with other financial markets.
problem Analyzing statistical and multiscaling characteristics of oil prices and their cross-correlations with other financial instruments.
method Multifractal analysis, detrended cross-correlation coefficient, multifractal cross-correlation analysis.
result Multifractal cross-correlations between oil prices and other financial markets, especially with oil-producing countries' currencies.
Based on empirical financial time-series, we show that the "silence-breaking" probability follows a super-universal power law: the probability of observing a large movement is inversely proportional to the length of the on-going low-variability period. Such a scaling law has been previously predicted theoretically [R. …
The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding …
We consider a stochastic volatility model which captures relevant stylized facts of financial series, including the multi-scaling of moments. The volatility evolves according to a generalized Ornstein-Uhlenbeck processes with super-linear mean reversion. Using large deviations techniques, we determine the asymptotic sh…
We study quantitatively the level of false multifractal signal one may encounter while analyzing multifractal phenomena in time series within multifractal detrended fluctuation analysis (MF-DFA). The investigated effect appears as a result of finite length of used data series and is additionally amplified by the long-t…
We perform an analysis of fractal properties of the positive and the negative changes of the German DAX30 index separately using Multifractal Detrended Fluctuation Analysis (MFDFA). By calculating the singularity spectra f(α) we show that returns of both signs reveal multiscaling. Curiously, these spectra display a s…
Collective phenomena with universal properties have been observed in many complex systems with a large number of components. Here we present a microscopic model of the emergence of scaling behavior in such systems, where the interaction dynamics between individual components is mediated by a global variable making the …
We reanalyze high resolution data from the New York Stock Exchange and find a monotonic (but not power law) variation of the mean value per trade, the mean number of trades per minute and the mean trading activity with company capitalization. We show that the second moment of the traded value distribution is finite. Co…
A symmetry-guided definition of time may enhance and simplify the analysis of historical series with recurrent patterns and seasonalities. By enforcing simple-scaling and stationarity of the distributions of returns, we identify a successful protocol of time definition in Finance. The essential structure of the stochas…
In setting up a stochastic description of the time evolution of a financial index, the challenge consists in devising a model compatible with all stylized facts emerging from the analysis of financial time series and providing a reliable basis for simulating such series. Based on constraints imposed by market efficienc…
We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest versi…
By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities rev…
A phenomenological investigation of the endogenous and exogenous dynamics in the fluctuations of capital fluxes is investigated on the Chinese stock market using mean-variance analysis, fluctuation analysis and their generalizations to higher orders. Non-universal dynamics have been found not only in α exponents diff…
We present a comparative analysis of multifractal properties of financial time series built on stock indices from developing (WIG) and developed (S&P500) financial markets. It is shown how the multifractal image of the market is altered with the change of the length of time series and with the economic situation on the…
We investigate multifractality in the Korean stock-market index KOSPI. The generalized qth order height-height correlation function shows multiscaling properties. There are two scaling regimes with a crossover time around tc=40 min. We consider the original data sets and the modified data sets obtained by removin…
Cryptocurrency market decouples from Forex, showing multifractality.
problem Decoupling of crypto market from Forex.
method High-frequency recordings and multiscale cross-correlations.
result Cryptocurrency market shows multifractality, decoupling from Forex.
Stylized facts of empirical assets log-returns Z include the existence of (semi) heavy tailed distributions fZ(z) and a non-linear spectrum of Hurst exponents τ(β). Empirical data considered are daily prices of 10 large indices from 01/01/1990 to 12/31/2004. We propose a stylized model of price dynamics which is…
Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.
problem Understanding stock market dynamics and predicting market bubbles.
method Non-linear analysis using time-dependent Tsallis statistics and Generalized Hurst Exponents.
result Temporal trends of q-triplet values differ before and after market bubbles, indicating significant market dynamics changes.
The leverage effect weakly impacts return distributions, especially for small firms.
problem The leverage effect's impact on return distributions is inconsistent and puzzling.
method Analyzed the determinants of return distributions and proposed an indirect method to measure the interaction effect.
result The interaction effect between leverage and mean-reversion is weak and impacts return distributions mainly for small firms.
New method for interpreting non-linear models using forward marginal effects.
problem Interpreting non-linear models' feature effects is challenging.
method Introducing forward marginal effects and partitioning feature space for better interpretation.
result Improved interpretation of non-linear prediction functions.
The Kalinin effectivity is studied and applied to compactifications and Hilbert squares.
problem Understanding Kalinin effectivity in compactifications and its applications.
method Definition, construction methods, and analysis of Kalinin effectivity in various compactifications.
result Wonderful compactifications of hyperplane arrangements and configuration spaces are Kalinin effective.
New method estimates treatment effects in network data, accounting for spillover effects.
problem Treatment effect estimation in networks with spillover effects.
method Augmented inverse probability weighting (AIPW) with cross-fitting and machine learning.
result Semiparametric treatment effect estimator converges at parametric rate and follows Gaussian distribution.
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
GADGET framework decomposes global feature effects using recursive partitioning.
problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.
A new RL framework evaluates dynamic mediation effects over time.
problem Dynamic mediation effects in sequentially assigned treatments.
method Reinforcement Learning framework for decomposition and estimation of causal effects.
result Superior performance demonstrated through numerical studies and real data analysis.
The Zumbach effect is significant under rough Heston but negligible in classical Heston.
problem Identifying the Zumbach effect in stochastic volatility models.
method Explicit computations of the Zumbach effect under rough Heston model.
result The Zumbach effect is negligible in the classical Heston model but significant under rough Heston.
New method predicts drug side effects from combined use.
problem Predicting side effects from drug combinations.
method Multi-relational knowledge graph completion.
result State-of-the-art results on polypharmacy side effect prediction.
A new method purifies interaction effects in models to improve interpretability.
problem Interaction effects can be misinterpreted as separate main effects, complicating model interpretation.
method Proposes pure interaction effects and a Functional ANOVA decomposition algorithm to identify and isolate interaction effects.
result Identifies and separates interaction effects from main effects, showing large disparities in model interpretation.
New memory effect discovered in gravitational wave behavior.
problem Understanding gravitational wave behavior in spacetimes with angular momentum.
method Mathematical analysis of Minkowski spacetime and Kerr black holes.
result Angular momentum memory effect observed at future null infinity.
Decagon models polypharmacy side effects using graph convolutional networks.
problem Discovering polypharmacy side effects due to complex drug interactions.
method Developed a graph convolutional neural network for multirelational link prediction in multimodal networks.
result Accurately predicts polypharmacy side effects, outperforming baselines by up to 69%.