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.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
While the use of volatilities is pervasive throughout finance, our ability to determine the instantaneous volatility of stocks is nascent. Here, we present a method for measuring the temporal behavior of stocks, and show that stock prices for 24 DJIA stocks follow a stochastic process that describes an efficiently pric…
Model forecasts global stock market volatility using dynamic graphs and all trading days.
We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon ses…
GARCH models predict stock volatility in Indian sectors.
We calculate realized volatility of the Nikkei Stock Average (Nikkei225) Index on the Tokyo Stock Exchange and investigate the return dynamics. To avoid the bias on the realized volatility from the non-trading hours issue we calculate realized volatility separately in the two trading sessions, i.e. morning and afternoo…
Bayesian model reduces stock volatility by identifying key cointegrated relationships.
The study predicts stock volatility using LSTM and GARCH models.
Graph Signal Processing improves stock market volatility forecasting.
VolTS uses stats & ML to forecast stock market trends based on volatility.
A universal LSTM model outperforms asset-specific models in forecasting stock volatilities.
A model explains stock returns and volatility using multifractal and rough components.
Graph Neural Network improves volatility forecasting for 500 S&P stocks.
Study uses CSIE to estimate portfolio volatility relative to market.
Study finds Twitter activity correlates with stock volatility but not sentiment.
Study improves stock price prediction using adaptive Mixture of Experts framework.
Stochastic volatility models describe stock returns as driven by an unobserved process capturing the random dynamics of volatility . The present paper quantifies how much information about volatility and future stock returns can be inferred from past returns in stochastic volatility models in terms of …
The study improves stock market valuation using volatility and earnings data.
The study uses machine learning to forecast stock volatility, showing superior performance over traditional methods.
The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. …
Study compares ANN and GARCH models for volatility prediction across sectors.
Analyzes how rough volatility affects stock pricing and risk premium.
Predicts stock volatility using Twitter data and random forests.
This paper uses Gaussian processes to forecast short-term stock price volatility.
We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatilit…
We study the dependence of volatility on the stock price in the stochastic volatility framework on the example of the Heston model. To be more specific, we consider the conditional expectation of variance (square of volatility) under fixed stock price return as a function of the return and time. The behavior of this fu…
We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock pr…
Improved volatility forecasting using 1D CNNs with transfer learning.
This study examines investor sentiment's impact on stock market liquidity and volatility using deep learning and TVP-VAR models.
Paper predicts stock volatility using ESG news, showing deep learning's effectiveness.
Being able to forcast extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between volatilities exceeding a certain threshold for a fixed expected recurrence time . We find that the recurrence inter…
Study compares MoE and RNN models for stock price prediction across volatility profiles.
We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent .
This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …
New approach decodes stock volatility states for S&P500 network.
Asymmetries in volatility spillovers are highly relevant to risk valuation and portfolio diversification strategies in financial markets. Yet, the large literature studying information transmission mechanisms ignores the fact that bad and good volatility may spill over at different magnitudes. This paper fills this gap…
Modified Jones-Faddy skew t-distribution captures asymmetry in stock returns.
We perform return interval analysis of 1-min {\em{realized volatility}} defined by the sum of absolute high-frequency intraday returns for the Shanghai Stock Exchange Composite Index (SSEC) and 22 constituent stocks of SSEC. The scaling behavior and memory effect of the return intervals between successive realized vola…
In this paper, we are interested in continuous time models in which the index level induces some feedback on the dynamics of its composing stocks. More precisely, we propose a model in which the log-returns of each stock may be decomposed into a systemic part proportional to the log-returns of the index plus an idiosyn…
Paper introduces CSIE for estimating stock market volatility.
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,…
AMA-LSTM improves stock volatility prediction using adversarial training.
New models analyze how ECB's unconventional policies affect stock market volatility.
We study the asymptotic behavior of distribution densities arising in stock price models with stochastic volatility. The main objects of our interest in the present paper are the density of time averages of the squared volatility process and the density of the stock price process in the Stein-Stein and the Heston model…
Study examines asymmetry impacts on Japanese stock market volatility modeling and forecasting.
This paper calculates risk-dependent centrality of Brazilian stocks, showing rankings vary with external risk and crisis events.
We propose a stochastic process for stock movements that, with just one source of Brownian noise, has an instantaneous volatility that rises from a type of statistical feedback across many time scales. This results in a stationary non-Gaussian process which captures many features observed in time series of real stock r…
We examine volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Volatility pattern of the market is found using the BSE index for the three-year period 2000-2002. Random matrix analysis is carried out us…