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.
This paper develops copula-based models for forecasting multivariate realized volatility.
We introduce a multivariate diffusion model that is able to price derivative securities featuring multiple underlying assets. Each asset volatility smile is modeled according to a density-mixture dynamical model while the same property holds for the multivariate process of all assets, whose density is a mixture of mult…
Enhanced multivariate GARCH model using LSTM for better volatility forecasting.
Modeling joint log-volatility dynamics with multivariate fractional Ornstein-Uhlenbeck process.
VMAT strategy improves multivariate pair trading performance.
A Bayesian procedure is developed for multivariate stochastic volatility, using state space models. An autoregressive model for the log-returns is employed. We generalize the inverted Wishart distribution to allow for different correlation structure between the observation and state innovation vectors and we extend the…
Develops a new model to better estimate cryptocurrency and stock volatility.
Large deviation principles for multivariate stochastic volatility models.
In this paper we develop a Bayesian procedure for estimating multivariate stochastic volatility (MSV) using state space models. A multiplicative model based on inverted Wishart and multivariate singular beta distributions is proposed for the evolution of the volatility, and a flexible sequential volatility updating is …
Correlations between asset returns are important in many financial applications. In recent years, multivariate volatility models have been used to describe the time-varying feature of the correlations. However, the curse of dimensionality quickly becomes an issue as the number of correlations is for asse…
This paper examines volatility in REITs using a multivariate GARCH based model. The Multivariate VAR-GARCH technique documents the return and volatility linkages between REIT sub-sectors and also examines the influence of other US equity series. The motivation is for investors to incorporate time-varyng volatility and …
Study compares deep learning models for volatility prediction using multivariate data.
The paper estimates CoVaR with various models for financial risk analysis.
We introduce a multivariate stochastic volatility model for asset returns that imposes no restrictions to the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. When the number of assets is prohibitively large, we propose a factor multivariate stochastic volatili…
This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distribu…
mfBm models and forecasts volatility with different Hurst exponents and correlations.
Unified econometric model for portfolio optimization and option valuation.
VHVM models financial time series with varying volatility.
SpotV2Net forecasts intraday spot volatilities using graph attention networks.
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and extend it to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence…
Graph Neural Network improves volatility forecasting for 500 S&P stocks.
A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressi…
New model reduces volatility parameters and complexity.
Paper proposes PMformer for better cryptocurrency price forecasting.
Graph neural networks improve volatility forecasting by capturing spillover effects.
A new model captures multifractal volatility in stock returns.
This paper optimizes portfolio selection for multivariate affine and quadratic Volterra models with rough volatilities.
Study proposes a method to construct copulas using corrected Hermite polynomial expansion for estimating foreign exchange volatility.
Multivariate probability density functions of returns are constructed in order to model the empirical behavior of returns in a financial time series. They describe the well-established deviations from the Gaussian random walk, such as an approximate scaling and heavy tails of the return distributions, long-ranged volat…
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables,…
Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the p…
Study analyzes how COVID-19 impacts crypto and stock market volatility.
Paper proposes a new model for multivariate risk measures using Wasserstein barycenters.
This paper presents the nonparametric inference for nonlinear volatility functionals of general multivariate Itô semimartingales, in high-frequency and noisy setting. Pre-averaging and truncation enable simultaneous handling of noise and jumps. Second-order expansion reveals explicit biases and a pathway to bias correc…
This paper reviews spatial and spatiotemporal volatility models.
Extends QHawkes to MQHawkes for analyzing financial co-jumps.
A new model captures multifractal volatility in stock returns.
The paper develops Hawkes-based models for LOB and applies them to European, spread, and basket option pricing.
We prove a large deviations principle for the class of multidimensional affine stochastic volatility models considered in (Gourieroux, C. and Sufana, R., J. Bus. Econ. Stat., 28(3), 2010), where the volatility matrix is modelled by a Wishart process. This class extends the very popular Heston model to the multivariate …
Dynamic functional time-series methods improve forecast accuracy for foreign exchange implied volatility surfaces.
Study uses VIX for zero-coupon Treasury rates, proving long-term stability and returns.
The Multi Variate Mixture Dynamics model is a tractable, dynamical, arbitrage-free multivariate model characterized by transparency on the dependence structure, since closed form formulae for terminal correlations, average correlations and copula function are available. It also allows for complete decorrelation between…
VOLARE provides standardized realized volatility measures from financial data.
We present a multivariate stochastic volatility model with leverage, which is flexible enough to recapture the individual dynamics as well as the interdependencies between several assets while still being highly analytically tractable. First we derive the characteristic function and give conditions that ensure its anal…
We consider stochastic partial differential equations appearing as Markovian lifts of matrix valued (affine) Volterra type processes from the point of view of the generalized Feller property (see e.g., \cite{doetei:10}). We introduce in particular Volterra Wishart processes with fractional kernels and values in the con…
In this paper we briefly review the recently inrtroduced Multifractal Random Walk (MRW) that is able to reproduce most of recent empirical findings concerning financial time-series : no correlation between price variations, long-range volatility correlations and multifractal statistics. We then focus on its extension t…
The study improves stock market valuation using volatility and earnings data.
Hybrid GARCH-LSTM models predict covariance matrices better than GARCH alone.