Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

Trend · papers per month

1122 · Aug 200419922001200920172026
35 results for multiscaling

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.

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.

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 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 ττ, which are time intervals between volatilities above a given threshold qq. We explore the probability density function of ττ, Pq(τ)P_q(τ), assuming a stretched exp…

2008-08-23abs ↗pdf ↗

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…

2010-06-01abs ↗pdf ↗

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…

2013-12-17abs ↗pdf ↗

We find a nonlinear dependence between an indicator of the degree of multiscaling of log-price time series of a stock and the average correlation of the stock with respect to the other stocks traded in the same market. This result is a robust stylized fact holding for different financial markets. We investigate this re…

2018-02-04abs ↗pdf ↗

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…

2015-09-17abs ↗pdf ↗

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. …

2008-12-24abs ↗pdf ↗

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.

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…

2015-01-14abs ↗pdf ↗

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)f(α) we show that returns of both signs reveal multiscaling. Curiously, these spectra display a s…

2008-03-10abs ↗pdf ↗

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 qmq_{m}-exponencial, form (eqm=1x=exe_{q_{m}=1}^{x}=e^{x}). Specifically, we inspect the self-correlation function o…

2008-06-16abs ↗pdf ↗

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…

2005-08-22abs ↗pdf ↗

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…

2008-07-16abs ↗pdf ↗

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 …

2010-01-04abs ↗pdf ↗

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…

2012-01-07abs ↗pdf ↗

Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the pe…

2019-06-18abs ↗pdf ↗

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…

2009-12-17abs ↗pdf ↗

We investigate multifractality in the Korean stock-market index KOSPI. The generalized qqth order height-height correlation function shows multiscaling properties. There are two scaling regimes with a crossover time around tc=40t_c =40 min. We consider the original data sets and the modified data sets obtained by removin…

2004-12-15abs ↗pdf ↗

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…

2013-05-14abs ↗pdf ↗

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…

2013-07-08abs ↗pdf ↗

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.