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 compares Fourier estimators to mitigate asynchrony effects in finance.
Unified analytic account of correlation emergence and Epps effect in coupled limit order books
We analyse the dependence of stock return cross-correlations on the sampling frequency of the data known as the Epps effect: For high resolution data the cross-correlations are significantly smaller than their asymptotic value as observed on daily data. The former description implies that changing trading frequency sho…
Study confirms the Epps effect using different volume time averaging methods for JSE stocks.
The Epps effect helps distinguish between continuous and discrete financial tick data.
The Epps effect varies under different sampling schemes, affecting correlation emergence rates.
We find a sharp local maximum in cross-correlation of EUR/USD and BTC/USD pairs, indicating short-term momentum trading.
The Epps effect, the decrease of correlations between stock returns for short time windows, was traced back to the trading asynchronicity and to the occasional lead-lag relation between the prices. We study pairs of stocks where the latter is negligible and confirm the importance of asynchronicity but point out that al…
Model simulates correlation emergence in two coupled limit order books.
We present two statistical causes for the distortion of correlations on high-frequency financial data. We demonstrate that the asynchrony of trades as well as the decimalization of stock prices has a large impact on the decline of the correlation coefficients towards smaller return intervals (Epps effect). These distor…
We present a simple microstructure model of financial returns that combines (i) the well-known ARFIMA process applied to tick-by-tick returns, (ii) the bid-ask bounce effect, (iii) the fat tail structure of the distribution of returns and (iv) the non-Poissonian statistics of inter-trade intervals. This model allows us…
We present a method to compensate statistical errors in the calculation of correlations on asynchronous time series. The method is based on the assumption of an underlying time series. We set up a model and apply it to financial data to examine the decrease of calculated correlations towards smaller return intervals (E…
We review the decomposition method of stock return cross-correlations, presented previously for studying the dependence of the correlation coefficient on the resolution of data (Epps effect). Through a toy model of random walk/Brownian motion and memoryless renewal process (i.e. Poisson point process) of observation ti…
The most important part of model selection and hyperparameter tuning is the evaluation of model performance. The most popular measures, such as AUC, F1, ACC for binary classification, or RMSE, MAD for regression, or cross-entropy for multilabel classification share two common weaknesses. First is, that they are not on …
A detailed analysis of correlation between stock returns at high frequency is compared with simple models of random walks. We focus in particular on the dependence of correlations on time scales - the so-called Epps effect. This provides a characterization of stochastic models of stock price returns which is appropriat…
New meta-score EPP interprets model performance differences.
Cryptocurrency market becomes more cross-correlated over time.
We demonstrate that the lowest possible price change (tick-size) has a large impact on the structure of financial return distributions. It induces a microstructure as well as it can alter the tail behavior. On small return intervals, the tick-size can distort the calculation of correlations. This especially occurs on s…
We analyse the temporal changes in the cross correlations of returns on the New York Stock Exchange. We show that lead-lag relationships between daily returns of stocks vanished in less than twenty years. We have found that even for high frequency data the asymmetry of time dependent cross-correlation functions has a d…
The correlation coefficient between stocks depends on price history and includes information on hierarchical structure in financial markets. It is useful for portfolio selection and estimation of risk. I introduce the Life Time of Correlation between stocks prices to know how far we should investigate the price history…
We present a systematic study of various statistical characteristics of high-frequency returns from the foreign exchange market. This study is based on six exchange rates forming two triangles: EUR-GBP-USD and GBP-CHF-JPY. It is shown that the exchange rate return fluctuations for all the pairs considered are well desc…
A new uncertainty principle helps traders better understand market activity.
We investigate 17 digital currencies making an analogy with quantum systems and develop the concept of eigenportfolios. We show that the density of states of the correlation matrix of these assets shows a behavior between that of the Wishart ensemble and one whose elements are Cauchy distributed. A metric for the parti…
In addressing the question of the time scales characteristic for the market formation, we analyze high frequency tick-by-tick data from the NYSE and from the German market. By using returns on various time scales ranging from seconds or minutes up to two days, we compare magnitude of the largest eigenvalue of the corre…
We introduce a new stochastic model for the variations of asset prices at the tick-by-tick level in dimension 1 (for a single asset) and 2 (for a pair of assets). The construction is based on marked point processes and relies on linear self and mutually exciting stochastic intensities as introduced by Hawkes. We associ…
The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding …
Revisits elastic string model to explain interest rate correlations.
Enhances Fourier estimator performance for asynchronous event-data.
The Maximum Mean Discrepancy (MMD) has found numerous applications in statistics and machine learning, most recently as a penalty in the Wasserstein Auto-Encoder (WAE). In this paper we compute closed-form expressions for estimating the Gaussian kernel based MMD between a given distribution and the standard multivariat…
Paper compiles ML algorithm performance benchmarks on OpenML datasets.
Study of quasilocal energy in higher dimensions, focusing on small sphere limits.
The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointe…
New method for interpreting non-linear models using forward marginal effects.
The Kalinin effectivity is studied and applied to compactifications and Hilbert squares.
New method estimates treatment effects in network data, accounting for spillover effects.
Causalfe estimates treatment effects in panel data with fixed effects.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
GADGET framework decomposes global feature effects using recursive partitioning.
A new RL framework evaluates dynamic mediation effects over time.
New memory effect discovered in gravitational wave behavior.
Effective Yau-Tian-Donaldson conjecture for spherical varieties.
A new method estimates treatment effects in mixed groups, improving accuracy.
Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.
Study develops method for estimating causal effects in continuous variables.
ICA accurately estimates treatment effects even with confounders.
Method improves treatment effect estimation in randomized experiments.
New method discovers context effects in choice data.
Dropout introduces both explicit and implicit regularization effects.