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.
A new option pricing model uses a time-varying Hurst exponent for more accurate financial predictions.
We conduct cluster analysis on a class of locally asymptotically self-similar stochastic processes, which includes multifractional Brownian motion as a representative. When the true number of clusters is supposed to be known, a new covariance-based dissimilarity measure is introduced, from which we obtain the approxima…
When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using classic detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover…
Complex systems are composed of mutually interacting components and the output values of these components are usually long-range cross-correlated. We propose a method to characterize the joint multifractal nature of such long-range cross correlations based on wavelet analysis, termed multifractal cross wavelet analysis…
In this paper we study BSE Index financial time series for fractal and multifractal behaviour. We show that Bombay stock Exchange (BSE)Index time series is mono-fractal and can be represented by a fractional Brownian motion.
Many complex systems generate multifractal time series which are long-range cross-correlated. Numerous methods have been proposed to characterize the multifractal nature of these long-range cross correlations. However, several important issues about these methods are not well understood and most methods consider only o…
In the canonical framework, we propose an alternative approach for the multifractal analysis based on the detrending moving average method (MF-DMA). We define a canonical measure such that the multifractal mass exponent is related to the partition function and the multifractal spectrum can be directly det…
We consider the structure functions S^(q)(T), i.e. the moments of order q of the increments X(t+T)-X(t) of the Foreign Exchange rate X(t) which give clear evidence of scaling (S^(q)(T)~T^z(q)). We demonstrate that the nonlinearity of the observed scaling exponent z(q) is incompatible with monofractal additive stochasti…
Mutually interacting components form complex systems and the outputs of these components are usually long-range cross-correlated. Using wavelet leaders, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call joint multifractal analysis based on wavel…
A new model captures multifractal volatility in stock returns.
A new model captures multifractal volatility in stock returns.
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…
This paper investigates multiscaling in the rough Bergomi model, finding it primarily due to fat-tailed returns.
There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross-correlations. The multifractal detrended cross-correlation analysis (MF-DCCA) approaches can be used to quantify such cross-correlations, such as the MF-DCCA based on detrend…
We introduce a general class of stochastic processes driven by a multifractional Brownian motion (mBm) and study the estimation problems of their pointwise Hölder exponents (PHE) based on a new localized generalized quadratic variation approach (LGQV). By comparing our suggested approach with the other two existing ben…
Introduces log S-fBM model to unify rough and multifractal volatility.
Model predicts stock price volatility using stochastic differential equations.
The superfamily phenomenon of time series with different dynamics can be characterized by the motif rank patterns observed in the nearest-neighbor networks of the time series in phase space. However, the determinants of superfamily classification are unclear. We attack this problem by studying the influence of linear t…
Study on determinants of unitary Brownian motion and their asymptotic laws.
Researchers created a continuous Markov martingale that mimics Brownian motion but lacks the strong Markov property.
Study refracted skew Brownian motion, find densities and asymptotics.
Paper defines multi-dimensional fractional Brownian motion under volatility uncertainty.
New model uses generalized fractional Brownian motion for stock price prediction.
Researchers calculate the Laplace transform of a geometric Brownian motion integral.
Geodesic walks converge to Brownian motion on Finsler manifolds.
New SDEs use -Brownian motion, extending mean-field models.
A new model captures option price dynamics using sub-fractional Brownian motion.
This paper derives the non-analytic solution to the Fokker-Planck equation of fractional Brownian motion using the method of Laplace transform. Sequentially, by considering the fundamental solution of the non-analytic solution, this paper obtains the transition probability density function of the random variable that i…
Replacing Black-Scholes' driving process, Brownian motion, with fractional Brownian motion allows for incorporation of a past dependency of stock prices but faces a few major downfalls, including the occurrence of arbitrage when implemented in the financial market. We present the development, testing, and implementatio…
The discrete sum of geometric Brownian motions plays an important role in modeling stochastic annuities in insurance. It also plays a pivotal role in the pricing of Asian options in mathematical finance. In this paper, we study the probability distributions of the infinite sum of geometric Brownian motions, the sum of …
Detrended fluctuation analysis (DFA) is a simple but very efficient method for investigating the power-law long-term correlations of non-stationary time series, in which a detrending step is necessary to obtain the local fluctuations at different timescales. We propose to determine the local trends through empirical mo…
The paper studies the question of whether the classical mirror and synchronous couplings of two Brownian motions minimise and maximise, respectively, the coupling time of the corresponding geometric Brownian motions. We establish a characterisation of the optimality of the two couplings over any finite time horizon and…
The book explores stochastic areas and heat kernels on manifolds.
Modeling financial markets with memory using fractional calculus and Brownian motion.
Estimates spectral gap for Brownian motion on sticky-reflecting domains.
Two insurance companies collaborate to maximize the probability of none going bankrupt.
The paper extends Merton model to price equity warrants under subdiffusive fractional Brownian motion of the short rate.
Solves optimal liquidation problem for stock price following geometric Brownian motion.
Study Brownian motions and heat kernel bounds on Kähler and quaternion Kähler manifolds.
Universal approximation for stochastic processes using Brownian motion.
Quaternionic Brownian motion on flag manifold linked to sphere diffusion.
Upper bounds on constants for Brownian motion with sticky boundary.
Proves CLT for Brownian paths on pinched negative curvature manifolds.
Rough volatility models are becoming increasingly popular in quantitative finance. In this framework, one considers that the behavior of the log-volatility process of a financial asset is close to that of a fractional Brownian motion with Hurst parameter around 0.1. Motivated by this, we wish to define a natural and re…
Study bounds for Brownian motion on manifolds with sticky boundary conditions.
An innovative extension of Geometric Brownian Motion model is developed by incorporating a weighting factor and a stochastic function modelled as a mixture of power and trigonometric functions. Simulations based on this Modified Brownian Motion Model with optimal weighting factors selected by goodness of fit tests, sub…
Time-subordinated Brownian motion models improve financial market stochastic distribution.
We provide an explicit formula giving the optimal number of paths needed to simulate two correlated Brownian motions.