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.
Study finds long-term linear correlations in Chinese stock order aggressiveness.
Preformer improves Transformer for long-term time series forecasting.
The study analyzes macroeconomic factors affecting copper futures volatility and long-term correlation with S&P 500.
We focus on emergence of the power-law cross-correlations from processes with both short and long term memory properties. In the case of correlated error-terms, the power-law decay of the cross-correlation function comes automatically with the characteristics of separate processes. Bivariate Hurst exponent is then equa…
We introduce a new test for detection of power-law cross-correlations among a pair of time series - the rescaled covariance test. The test is based on a power-law divergence of the covariance of the partial sums of the long-range cross-correlated processes. Utilizing a heteroskedasticity and auto-correlation robust est…
CaLoNet integrates spatial and local correlations for multivariate time series classification.
DSTP-RNN improves long-term multivariate time series prediction using attention-based RNN.
We investigate the two components of the total daily return (close-to-close), the overnight return (close-to-open) and the daytime return (open-to-close), as well as the corresponding volatilities of the 2215 NYSE stocks from 1988 to 2007. The tail distribution of the volatility, the long-term memory in the sequence, a…
Investment strategies differ based on short-term and long-term market time scales.
We employ perturbation analysis technique to study multi-asset portfolio optimisation with transaction cost. We allow for correlations in risky assets and obtain optimal trading methods for general utility functions. Our analytical results are supported by numerical simulations in the context of the Long Term Growth Mo…
It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…
Stock market price fluctuations follow Lévy's stable distribution over long term.
A class of heterogeneous agent models is investigated where investors switch trading position whenever their motivation to do so exceeds some critical threshold. These motivations can be psychological in nature or reflect behaviour suggested by the efficient market hypothesis (EMH). By introducing different propensitie…
Proposes a model for long-term electricity contracts with explicit computation and easy calibration.
The optimal strategies for a long-term static investor are studied. Given a portfolio of a stock and a bond, we derive the optimal allocation of the capitols to maximize the expected long-term growth rate of a utility function of the wealth. When the bond has constant interest rate, three models for the underlying stoc…
We propose a comprehensive treatment of the leverage effect, i.e. the relationship between returns and volatility of a specific asset, focusing on energy commodities futures, namely Brent and WTI crude oils, natural gas and heating oil. After estimating the volatility process without assuming any specific form of its b…
We analyse a multiplex of networks between OECD countries during the decade 2002-2010, which consists of five financial layers, given by foreign direct investment, equity securities, short-term, long-term and total debt securities, and five environmental layers, given by emissions of N O x, P M 10 SO 2, CO 2 equivalent…
Hybrid method reveals true currency correlations.
CFTM uses fractional Brownian motion for dynamic topic modeling.
FPG uses fractional calculus for efficient reinforcement learning with long-term memory.
The paper identifies short-term and long-term time scales in stock markets with and without structural breaks.
We construct and analyze symmetrized delay correlation matrices for empirical data sets for atmopheric and financial data to derive information about correlation between different entities of the time series over time. The information about correlations is obtained by comparing the results for the eigenvalue distributi…
We investigate the temporal correlations and multifractal nature of trading volume of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. We find that the trading volume exhibit size-dependent non-universal long memory and multifractal nature. No crossover in the power-law dependence of the detrended fluctu…
Statistical test verifies long-term rating system calibration with overlapping time windows.
We introduce a general framework of the Mixed-correlated ARFIMA (MC-ARFIMA) processes which allows for various specifications of univariate and bivariate long-term memory. Apart from a standard case when , MC-ARFIMA also allows for processes with but also for long-range …
Study identifies precursors of financial crashes using correlation patterns.
New method identifies precursors of financial crises in market correlation structures.
Paper proposes a new LSTM model for spatio-temporal learning.
Equity activity is an essential topic for financial market studies. To explore its statistical regularities, we comprehensively examine the trading value, a measure of the equity activity, of the 3314 most-traded stocks in the U.S. equity market and find that (i) the trading values follow a log-normal distribution; (ii…
The study examines volatility models and finds decoupling of short- and long-term correlation structures.
We propose a hybrid model of portfolio credit risk where the dynamics of the underlying latent variables is governed by a one factor GARCH process. The distinctive feature of such processes is that the long-term aggregate return distributions can substantially deviate from the asymptotic Gaussian limit for very long ho…
Study shows Merton model limits to Poisson process with log-normal intensity, improving default portfolio prediction.
We investigate serial correlation, periodic, aperiodic and scaling behaviour of eigenmodes, i.e. daily price fluctuation time-series derived from eigenvectors, of correlation matrices of shares listed on the Johannesburg Stock Exchange (JSE) from January 1993 to December 2002. Periodic, or calendar, components are dete…
Deep RNNs excel at capturing long-term dependencies in sequential data.
ARIMA-LSTM hybrid model predicts stock price correlation coefficients.
For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencie…
We introduce a new measure for the capital market efficiency. The measure takes into consideration the correlation structure of the returns (long-term and short-term memory) and local herding behavior (fractal dimension). The efficiency measure is taken as a distance from an ideal efficient market situation. Methodolog…
Bayesian approach improves Nelson-Siegel yield curve modeling.
Study examines cross-training neural networks for financial index prediction.
The Schwartz-Smith model parameters are estimated using Kalman Filter with additional constraints.
EUNNs improve RNN performance and efficiency.
ST-SAN predicts flow with spatial-temporal dependencies using self-attention.
Using a recently introduced method to quantify the time varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be anti-correlated; (ii) the change in…
The study improves Monte Carlo simulations for long-term investments using advanced financial models.
Deep model forecasts correlated multivariate time series.
CVAE improves stock volume forecasting with advanced input variables.
Energy markets and the associated energy futures markets play a crucial role in global economies. We investigate the statistical properties of the recurrence intervals of daily volatility time series of four NYMEX energy futures, which are defined as the waiting times between consecutive volatilities exceeding a gi…
Model forecasts market structure from financial networks using machine learning.