arXiv research

Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.

We show how bad and good volatility propagate through forex markets, i.e., we provide evidence for asymmetric volatility connectedness on forex markets. Using high-frequency, intra-day data of the most actively traded currencies over 2007 - 2015 we document the dominating asymmetries in spillovers that are due to bad r…

DRAG decreases regularization to accelerate semi-discrete OT convergence.

PhyDNN uses physics knowledge to improve drag force prediction models.

The study characterizes Sasakian manifolds from magnetic Hopf surfaces.

Simulation of high-speed train aerodynamics using RANS and machine learning.

We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects …

Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model…

Machine learning improves aircraft performance prediction by analyzing flight data.

This paper identifies a negative profit effect in limit order fills.

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not str…

New friction model for geometric locomotion systems.

We present a new approach for matching regular surfaces in a Riemannian setting. We use a Sobolev type metric on deformation vector fields which form the tangent bundle to the space of surfaces. In this article we compare our approach with the diffeomorphic matching framework. In the latter approach a deformation is pr…

Optimizes UUV hull design with a two-orders-of-magnitude speedup.

We construct the first combinatorial 1-cocycle with values in the $\mathbb{Z} [x,x^{-1}]$-module of isotopy classes of singular long knots in 3-space with a signed planar double point, and which represents a non trivial cohomology class in the topological moduli space of long knots. It can be interpreted as an invaria…

The paper analyzes optimal overbetting strategies for a satellite investment account.

Risk-only investment strategies have been growing in popularity as traditional in- vestment strategies have fallen short of return targets over the last decade. However, risk-based investors should be aware of four things. First, theoretical considerations and empirical studies show that apparently dictinct risk-based …

ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.

We introduce a novel description of the dynamics of the order book of financial markets as that of an effective colloidal Brownian particle embedded in fluid particles. The analysis of a comprehensive market data enables us to identify all motions of the fluid particles. Correlations between the motions of the Brownian…

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is t…

This paper develops a CVaR framework for managing tail risks using puts and trend-following strategies.

We here present a model of the dynamics of extremism based on opinion dynamics in order to understand the circumstances which favour its emergence and development in large fractions of the general public. Our model is based on the bounded confidence hypothesis and on the evolution of initially anti-conformist agents to…

Quantum model investigates financial derivative price dynamics with quantum interference effects.

TODS automates time series outlier detection with customizable pipelines.

We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural netw…

Exact relationships found between ATM slope, volatility swap, and zero vanna.

Study local volatility from rough volatility models, finding new skew rule.

Paper derives formulas for volatility swap strike and zero vanna implied volatility.

Design-by-Morphing creates radical airfoil designs without geometric constraints.

Enhanced volatility forecasting using options data and rough volatility model.

We consider an asset whose risk-neutral dynamics are described by a general class of local-stochastic volatility models and derive a family of asymptotic expansions for European-style option prices and implied volatilities. Our implied volatility expansions are explicit; they do not require any special functions nor do…

Volatility models must be rough to match market skew.

Study on estimating volatility of volatility using Fourier methods and provides insights into volatility dynamics.

New approach uses interpolation models and error bounds for verifiable scientific machine learning.

Extends Heston model with local volatility for better fit to market volatilities.

The paper values perpetual callable American volatility options using a mean-reverting volatility model.

The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. …

Develops a martingale expansion for stochastic volatility models.

Study large deviations in fractional volatility models with non-Gaussian volatility.

Estimates volatility of volatility and leverage effect using high-frequency options data.

This study compares three volatility metrics for Bitcoin, highlighting high expected volatility.

Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.

A spring-block chain placed on a running conveyor belt is considered for modeling stylized facts observed in the dynamics of stock indexes. Individual stocks are modeled by the blocks, while the stock-stock correlations are introduced via simple elastic forces acting in the springs. The dragging effect of the moving be…

Paper generalizes pricing and hedging of volatility swaps in stochastic models.

The article reviews how to set stochastic volatility model parameters.

Recent empirical studies suggest that the volatilities associated with financial time series exhibit short-range correlations. This entails that the volatility process is very rough and its autocorrelation exhibits sharp decay at the origin. Another classic stylistic feature often assumed for the volatility is that it …

This paper explores the harmonic mean of implied volatility and its relation to local volatility.

With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, w…