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 Autoencoder Reconstruction Ratio detects increased asset co-movements.
Based on a recent theorem due to the authors, it is shown how the extreme tail dependence between an asset and a factor or index or between two assets can be easily calibrated. Portfolios constructed with stocks with minimal tail dependence with the market exhibit a remarkable degree of decorrelation with the market at…
MarketGAN generates financial returns using GANs to match empirical stylized facts.
In this chapter we studied the nonlinear co-movements between the Mexican Crude Oil price, the Mexican Stock Market Index and the USD/MXN Exchange Rate, for the sample period from 1994 to date. We used a battery of nonlinear tests, cf. (Patterson & Ashley, 2000) and one multivariate test, in order to determine the dyna…
Graph auto-encoders predict stock market instability by measuring graph structure changes.
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is meas…
A new framework improves volatility forecasting for financial markets.
Study finds significant BTC co-movements with equity markets, highlighting dynamic risk management needs.
Paper uses news data to model asset correlations without market data.
Crypto markets show negative spillovers between chains, not positive co-movements.
Framework analyzes stock price co-movement with fundamentals using big data.
This study examines local co-movements in energy, agriculture, and metal markets using copulas.
A network-based approach identifies financial factors from asset interactions, explaining market dynamics.
This paper examines momentum spillover across multiple asset classes using only pricing data.
As financial instruments grow in complexity more and more information is neglected by risk optimization practices. This brings down a curtain of opacity on the origination of risk, that has been one of the main culprits in the 2007-2008 global financial crisis. We discuss how the loss of transparency may be quantified …
The paper explains stock market predictability through a model of heterogeneous beliefs.
This study examines how economic policy uncertainty impacts commodity prices across different crises.
Model forecasts market structure from financial networks using machine learning.
Develops a new model to better estimate cryptocurrency and stock volatility.
TailCoR measures co-movement of financial crises events.
Study applies Gai-Kapadia framework to global equity markets to assess systemic risk and default cascades.
Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.
The paper examines how decentralized credit curators have taken over risk management from traditional protocols.
In this paper we use the Brooks and Hinich cross-bicorrelation test in order to uncover nonlinear dependence periods between USA Standard and Poor 500 (SP500), used as benchmark, and six Latin American stock markets indexes: Mexico (BMV), Brazil (BOVESPA), Chile (IPSA), Colombia (COLCAP), Peru (IGBVL) and Argentina (ME…
Study extends Gai-Kapadia framework to assess systemic risk in global equity markets.
Since the beginning of the new millennium, stock markets went through every state from long-time troughs, trade suspensions to all-time highs. The literature on asset pricing hence assumes random processes to be underlying the movement of stock returns. Observed procyclicality and time-varying correlation of stock retu…
As described in this paper, we study market-wide price co-movements around crashes by analyzing a dataset of high-frequency stock returns of the constituent issues of Nikkei 225 Index listed on the Tokyo Stock Exchange for the three years during 2007--2009. Results of day-to-day principal component analysis of the time…
TDA improves FX clustering quality over traditional methods.
Enhanced multivariate GARCH model using LSTM for better volatility forecasting.
In this article we review several techniques to extract information from stock market data. We discuss recurrence analysis of time series, decomposition of aggregate correlation matrices to study co-movements in financial data, stock level partial correlations with market indices, multidimensional scaling and minimum s…
Topological anomaly scores predict return curves in S&P 500 stocks
In this paper, we contribute to the literature on energy market co-movement by studying its dynamics in the time-frequency domain. The novelty of our approach lies in the application of wavelet tools to commodity market data. A major part of economic time series analysis is done in the time or frequency domain separate…
This paper analyzes the process of long-run co-movements and stock market globalization on the basis of cointegration tests and vector error correction (VEC) models. The cointegration tests used here allow for structural breaks to be explicitly modeled and breakpoints to be computed on a relative-time basis. The data u…
The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (M…
Study examines oil and US stock market interactions during coronavirus crisis.
Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.
The Moscow Stock Exchange was inefficient for most of 2012-2021.
This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained duri…
On the fifth of February, 2018, the Dow Jones Industrial Average dropped 1,175.21 points, the largest single-day fall in history in raw point terms. This followed a 666-point loss on the second, and another drop of over a thousand points occurred three days later. It is natural to ask whether these events indicate a tr…
RegimeFolio optimizes portfolios by adapting to changing market regimes.
ExCIR provides efficient, consistent, and scalable explainability for complex models.
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…
We demonstrate both analytically and numerically that the existing methods for measuring tail dependence in copulas may sometimes underestimate the extent of extreme co-movements of dependent risks and, therefore, may not always comply with the new paradigm of prudent risk management. This phenomenon holds in the conte…
Scale invariance, collective behaviours and structural reorganization are crucial for portfolio management (portfolio composition, hedging, alternative definition of risk, etc.). This lack of any characteristic scale and such elaborated behaviours find their origin in the theory of complex systems. There are several me…
Symmetric binary matrices representing relations among entities are commonly collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being in inference on the relationship structure and prediction. We propose a nonparametric Bayesian dynamic model, which reduces dimension…
We analyse time series of CDS spreads for a set of major US and European institutions on a pe- riod overlapping the recent financial crisis. We extend the existing methodology of ε-drawdowns to the one of joint ε-drawups, in order to estimate the conditional probabilities of abrupt co-movements among spreads. We correc…
The aim of this article is to briefly review and make new studies of correlations and co-movements of stocks, so as to understand the "seasonalities" and market evolution. Using the intraday data of the CAC40, we begin by reasserting the findings of Allez and Bouchaud [New J. Phys. 13, 025010 (2011)]: the average corre…
A2-SBNN models spatial data with copulas for non-Gaussian dependencies.