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.
The paper provides an efficient method to price path-dependent derivatives using multiscale stochastic volatility models.
The study offers a multiscale model for SPX and VIX options pricing.
This paper proposes a novel multiscale estimator for the integrated volatility of an Ito process, in the presence of market microstructure noise (observation error). The multiscale structure of the observed process is represented frequency-by-frequency and the concept of the multiscale ratio is introduced to quantify t…
We perform a scaling analysis on NYSE daily returns. We show that volatility correlations are power-laws on a time range from one day to one year and, more important, that they exhibit a multiscale behaviour.
This paper compares two extensions of the Heston model for option pricing.
In this paper we present a new method to compute the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility of Fouque \textit{et al.} (2011, CUP). It provides an alternative method to the singular perturbation technique presented in Hikspoors and Jaimungal (2…
Multiscale stochastic volatility models have been developed as an efficient way to capture the principle effects on derivative pricing and portfolio optimization of randomly varying volatility. The recent book Fouque, Papanicolaou, Sircar and Sølna (2011, CUP) analyzes models in which the volatility of the underlying i…
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
Optimizes trading strategies with price impact, predictable returns, and stochastic volatility.
The dynamics of prices in financial markets has been studied intensively both experimentally (data analysis) and theoretically (models). Nevertheless, a complete stochastic characterization of volatility is still lacking. What it is well known is that absolute returns have memory on a long time range, this phenomenon i…
We study the exponential Ornstein-Uhlenbeck stochastic volatility model and observe that the model shows a multiscale behavior in the volatility autocorrelation. It also exhibits a leverage correlation and a probability profile for the stationary volatility which are consistent with market observations. All these featu…
Deep learning predicts currency volatility accurately.
We consider stochastic control systems affected by a fast mean reverting volatility driven by a pure jump Lévy process. Motivated by a large literature on financial models, we assume that evolves at a faster time scale than the assets, and we study the asymptotics as $\varepsilon\t…
Using tools from spectral analysis, singular and regular perturbation theory, we develop a systematic method for analytically computing the approximate price of a derivative-asset. The payoff of the derivative-asset may be path-dependent. Additionally, the process underlying the derivative may exhibit killing (i.e. jum…
Paper introduces MN-DAG for modeling evolving causal relationships in multivariate time series.
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of …
We consider the problem of valuing a European option written on an asset whose dynamics are described by an exponential Lévy-type model. In our framework, both the volatility and jump-intensity are allowed to vary stochastically in time through common driving factors -- one fast-varying and one slow-varying. Using Four…
We study, both analytically and numerically, an ARCH-like, multiscale model of volatility, which assumes that the volatility is governed by the observed past price changes on different time scales. With a power-law distribution of time horizons, we obtain a model that captures most stylized facts of financial time seri…
The paper proves Gorenstein contractions for multiscale differentials on nodal curves.
Study cryptocurrency price dynamics using adaptive EMD and spectral analysis.
Proposes a new algorithm for non-stationary bandits.
Bayesian model learns multiscale interactions in complex systems.
Graph clustering uses multiscale community detection for improved performance.
DMGNN predicts 3D human motions using adaptive multiscale graphs.
Neural network approach simplifies multiscale problem homogenization.
We propose a simple stochastic model of market behavior. Dividing market participants into two groups: trend-followers and fundamentalists, we derive the general form of a stochastic equation of market dynamics. The model has two characteristic time scales: the time of changes of market environment and the characterist…
We discuss multiscale representations of discrete manifold-valued data. As it turns out that we cannot expect general manifold-analogues of biorthogonal wavelets to possess perfect reconstruction, we focus our attention on those constructions which are based on upscaling operators which are either interpolating or midp…
MELD model clusters data at multiple scales, improving understanding of latent structure.
New algorithm learns switching dynamics from multiple neural signals.
Optimal multiscale learning of linear operators
MsIGN tackles high-dimensional Bayesian inference using multiscale structure.
Kernel analog forecasting studied for multiscale systems.
The paper extends entropy maximization to multiscale settings and applies it to neural networks.
New method analyzes knots and links using multiscale Gauss link integral.
Extracts causal brain dynamics across multiple scales.
Novel graph network learns hierarchical network structure.
New algorithm clusters hyperspectral images at multiple scales.
iLED framework offers interpretable dynamics for multiscale systems.
Study compares Bitcoin, gold, and gas price complexity using multifractal and multiscale entropy methods.
In this paper, we extend the first-order asymptotics analysis of Fouque et al. to general path-dependent financial derivatives using Dupire's functional Ito calculus. The main conclusion is that the market group parameters calibrated to vanilla options can be used to price to the same order exotic, path-dependent deriv…
In this paper, we propose a generic framework for devising an adaptive approximation scheme for value function approximation in reinforcement learning, which introduces multiscale approximation. The two basic ingredients are multiresolution analysis as well as tree approximation. Starting from simple refinable function…
Study optimal futures trading strategies for assets with multiscale central tendency price model.
Many problems in sequential decision making and stochastic control often have natural multiscale structure: sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure, particularly beyond a single level of abstraction, has remained a longstanding challen…
New MHSNs extract multiscale features from complex data for robust classification.
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series. Currently, most of multiscale RNNs use fixed scales, which do not comply with the nature of dynamical temporal patterns among sequences…
Deterministic GD can behave stochastically in large learning rates for multiscale functions.
Current state-of-the-art discrete optimization methods struggle behind when it comes to challenging contrast-enhancing discrete energies (i.e., favoring different labels for neighboring variables). This work suggests a multiscale approach for these challenging problems. Deriving an algebraic representation allows us to…
A new sampling method estimates scores without training or nested MCMC.