Two effects explain low-vol anomaly: dividend-yield correlation and ex-dividend returns.
problem Explaining the low-volatility anomaly in stock markets.
method Analyzing historical data to identify and quantify two independent effects.
result The low-volatility anomaly is explained by two effects: dividend-yield correlation and ex-dividend returns.
New formulas for barrier options in stochastic volatility models with nonzero correlation.
problem Calculating barrier options prices in models with nonzero correlation.
method Derivation of two novel closed-form formulas: Hull and White type and Alòs-like decomposition.
result Closed-form formulas for barrier options in stochastic volatility models with nonzero correlation.
Improved binomial model for American put prices with error analysis.
problem Improving the accuracy of American put price approximations.
method Binomial approximation in the Black-Scholes model with consideration of continuous dividend yield.
result Error in approximation is O((lnn)α/n), where α depends on interest rate and dividend yield. Machine learning approximates implied volatility and dividend yield for American options.
problem Challenges in extracting implied information from American options due to computational costs.
method Employing a data-driven machine learning approach, specifically a Calibration Neural Network (CaNN), to estimate implied volatility and dividend yield efficiently.
result Machine learning can be used to estimate implied volatility and dividend yield for American options efficiently.
New asymptotic formula for option prices with interest rates and dividend yield effects.
problem Deriving option prices with interest rates and dividend yield effects in the local volatility model.
method Developed a new asymptotic limit for short-maturity option prices, including interest rates and dividend yield effects.
result Generalized the Berestycki-Busca-Florent formula to all orders in n for interest rates and dividend yield effects. Improved stock market valuation using modified dividend-price ratio.
problem Stationary assumption failure in classical dividend-price ratio.
method Multivariate regressions, dynamic econometric procedure, modified dividend-price ratio (mdp).
result Enhanced forecasting results over classical dividend-price ratio.
New method detects hidden market predictability despite anomalies.
problem Inference issues in predictive regressions with small violations.
method Novel testing framework resistant to violations of ideal assumptions.
result Large improvements in robust evidence of market predictability.
Financial contracts with options that allow the holder to extend the contract maturity by paying an additional fixed amount found many applications in finance. Closed-form solutions for the price of these options have appeared in the literature for the case when the contract underlying asset follows a geometric Brownia…
The incorporation of a dividend yield in the classical option pricing model of Black- Scholes results in a minor modification of the Black-Scholes formula, since the lognormal dynamic of the underlying asset is preserved. However, market makers prefer to work with cash dividends with fixed value instead of a dividend y…
Study on CEF discount in Bangladesh, finds size and maturity impact, turnover negative.
problem Exploring the discount puzzle in closed-end mutual funds in Bangladesh.
method Fixed effects panel regression with diagnostic tests.
result Fund size and maturity positively impact CEF discount, turnover negatively impacts.
Closed-form solution found for American put option boundary.
problem Finding the optimal exercise boundary for American put options.
method Three models of stock price dynamics with time-dependent parameters, leading to a closed-form solution for the exercise boundary.
result Explicit closed-form solution for the optimal exercise boundary of American put options.
Smooths local volatility model calibration with piecewise linear variance.
problem Calibrating local volatility models to option prices.
method Extends LiptonSepp2011 approach with piecewise linear variance and non-zero interest rates/dividends.
result Analytical tractability with Kummer's hypergeometric functions.
This paper surveys options pricing under arithmetic Brownian motion and derives formulas for various types of options.
problem The use of arithmetic Brownian motion in finance is not widely adopted.
method Risk-neutral valuation and derivation of formulas for European options under three types of underlying assets.
result Derivation of formulas for European options and partial differential equations for American options.
Study develops sector rotation models using factor and fundamental analysis.
problem Understanding and predicting sector shifts in financial markets.
method Systematic sector classification, factor analysis, and fundamental metrics evaluation.
result Developed predictive models with notable predictive capabilities.
This paper examines the valuation of a generalized American-style option known as a Game-style call option in an infinite time horizon setting. The specifications of this contract allow the writer to terminate the call option at any point in time for a fixed penalty amount paid directly to the holder. Valuation of a pe…
Analytical approximations for Asian option sensitivities in Black-Scholes model.
problem Calculating sensitivities of Asian options in the Black-Scholes model.
method Small maturity/volatility approximation and large deviations theory.
result Good agreement with alternative numerical simulation results for practical cases.
Derives a dual equation for various option types, leading to new pricing and hedging insights.
problem Pricing and hedging of various option types.
method Derives a dual equation with the same form as the Black-Scholes-Merton equation, applicable to homogeneous degree one payoffs.
result Provides simple analytic formulas for delta and gamma, and reveals put-call equality for various options.
This paper uses basket option formulas to price vanilla options with discrete dividends.
problem Pricing vanilla options on stocks with discrete cash dividends.
method Uses existing basket option formulas for European options on a single asset with cash dividends in the piecewise lognormal model.
result Explains the use of basket option formulas for a specific problem in the piecewise lognormal model.
Asymptotic Laplace transform for geometric Brownian motion applied to bond pricing.
problem Laplace transform of geometric Brownian motion time integral.
method Asymptotic analysis for σ2To0 limit. result Approximation for zero coupon bond prices in Dothan model.
According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint dynamics of stock price,…
Proposes a new model for stock and dividend derivatives pricing.
problem Pricing stock and dividend derivatives with positive stock prices and non-negative dividends.
method Jointly specifies dynamics for stock price and dividend rate, using mean-reverting dividend rate.
result Closed-form expressions for stock and dividend futures prices, accurate option approximations.
The paper develops a neural network model for SPX option pricing.
problem Developing an empirical model for SPX option pricing.
method Formulated and rigorously evaluated several statistical models including neural network, random forest, and linear regression.
result The neural network model outperforms other models and Black-Scholes-Merton model for SPX option pricing.
We extend Dupire's formula for stochastic interest rates and local volatility.
problem Deriving formulas for stochastic interest rates and local volatility.
method Generalizations of Dupire's formula for stochastic drift and local volatility.
result Validated the limits of the generalized Dupire formulae for specific cases.
Estimates potential correlations using hypercontractivity.
problem Discovering hidden correlations from data.
method Postulated natural axioms, hypercontractivity coefficient, novel estimator.
result Estimator discovers potential correlations robustly.
This work optimizes induced correlation in joint graph embeddings.
problem Optimizing correlation across embedded networks in joint graph embeddings.
method Developed corr2Omni algorithm to estimate optimal Omnibus weights.
result corr2Omni algorithm improves inference fidelity compared to classical Omnibus construction.
Letter analyzes cryptocurrency correlations with financial assets.
problem Understanding correlations among cryptocurrencies and financial assets.
method Used a generalized DCC class model to analyze conditional correlations.
result Cryptocurrency correlations are positive but vary over time.
In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two va…
We analyze the daily stock data of the Nasdaq Composite index in the 22-year period 1992-2013 and identify market states as clusters of correlation matrices with similar correlation structures. We investigate the stability of the correlation structure of each state by estimating the statistical fluctuations of correlat…
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
problem Misleading results from Pearson correlation in financial networks.
method Local Gaussian correlation coefficient for capturing nonlinear dependence and heavy-tailed distributions.
result Local Gaussian correlation network among negative tails is more sensitive to stock market risks.
The study uses DCC for financial market analysis, revealing hidden correlations.
problem Identifying hidden nonlinear correlations in financial markets.
method Agglomerative hierarchical clustering with distance correlation coefficient.
result DCC reveals more information than Pearson correlation for financial data.
Correlated noise improves deep CNN performance on occluded images.
problem Understanding and leveraging correlated variability in neural networks.
method Implemented correlated noise models in deep convolutional neural networks, defined as a function of neuron selectivity and distance.
result Correlated noise models often improve performance on occluded images compared to other regularization techniques.
Polynomial time algorithm matches correlated Gaussian matrices without vanishing correlation.
problem Matching vertices in two correlated Erdős-Rényi graphs.
method Iterative matching algorithm for correlated Gaussian Wigner matrices.
result First polynomial time algorithm for graph matching with arbitrarily small constant correlation.
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…
Deep LSTMs learn correlated representations of time-series data.
problem Learning nonlinear transformations and correlated embeddings of variable-length sequences.
method Use LSTMs to transform multi-view time-series data, then correlate outputs to find a fixed-dimensional representation.
result Deep LSTMs can effectively learn and project correlated representations of time-series data.
This paper introduces anti-correlation networks to study China's stock market.
problem Previous studies ignored anti-correlation in financial networks.
method Constructed weighted temporal anti-correlation and positive correlation networks.
result Unveiled differences in topological measurements between anti-correlation and positive correlation networks.
The study shows how trade uncertainty affects stock-bond correlations over time.
problem Impact of trade policy uncertainty on stock-bond correlations.
method Daily data analysis using GARCH-based models (CCC, STCC, DCC) with TPU and political dummy variables.
result Time-varying correlation models better capture the dynamics of stock-bond correlations than constant models.
Infinite CNNs lose spatial correlations, but can be restored by correlated weights.
problem Infinite CNNs lose spatial correlations, which are crucial for their performance.
method Introduced correlated weights to restore spatial correlations in infinite CNNs.
result Optimal performance is achieved with a moderate level of weight correlation.
We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Specifically, we discuss how to define and obtain hierarchical …
This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.
problem Rare spurious correlations in neural networks and their privacy risks.
method Introducing spurious patterns correlated with a fixed class to a few training examples, analyzing ℓ2 regularization and Gaussian noise. result Rare spurious correlations can significantly impact neural network accuracy and privacy, and specific mitigation methods can be effective.
CVAEs improve VAEs by accounting for correlations in latent representations.
problem VAEs fail to account for correlations between data points, limiting their effectiveness.
method CVAEs incorporate correlation structure into VAEs using a prior and tractable approximations.
result CVAEs outperform baseline algorithms in matching and link prediction tasks.
The study reveals how synaptic correlations promote dimension reduction in neural networks.
problem Understanding how synaptic correlations affect neural correlations and dimension reduction in deep neural networks.
method A simplified model of dimension reduction considering pairwise correlations among synapses, using mathematical self-consistency for both binary and continuous synapses.
result Weakly-correlated synapses encourage dimension reduction compared to orthogonal synapses, and they also slow down the decorrelation process.
A factor model for stress-testing correlations, focusing on large portfolios.
problem Stress-testing correlations in large portfolios to assess risk.
method Factor model using Mahalanobis distance for identifying adverse scenarios.
result Demonstrated how correlation and volatility stress tests can be combined.
Proposes PSCCA for estimating correlations and canonical correlations in sparse count data.
problem Estimating correlations and canonical correlations in sparse count data from next-generation sequencing.
method Probabilistic approach for sparse count data sets (PSCCA).
result PSCCA outperforms other methods in estimating true correlations and canonical correlations at the natural parameter level.
Develops a theory of common decomposition for correlated Brownian motions.
problem Tackles the modeling of correlated Brownian motions in financial applications.
method Uses change of time method to represent correlated Brownian motions as a triplet of processes.
result Shows equivalent conditions for the triplet being independent and proposes a new method for constructing correlated Brownian motions.
Study examines NFT market dynamics using correlation and noise analysis.
problem Understanding correlations and noise in NFT market.
method Used detrended correlation coefficient and correlation matrix analysis.
result Correlation strength in NFT market is lower than in cryptocurrency markets.
Polynomial-time algorithm matches correlated random graphs with non-vanishing correlation.
problem Matching correlated random graphs with non-vanishing edge correlation.
method Iterative algorithm for polynomial-time recovery of latent matching.
result Algorithm succeeds in recovering latent matching as long as edge correlation is non-vanishing.
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
Enhances community detection in correlated networks with node attributes.
problem Community detection in multiple networks with correlated node attributes and edges.
method Introduced the correlated Contextual Stochastic Block Model (CSBM), developed a two-step matching procedure.
result Algorithm recovers exact node correspondence, enabling enhanced community detection.