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.
New model explains low-volatility anomaly using adaptive multi-factor approach.
Bayesian model reduces stock volatility by identifying key cointegrated relationships.
Low-rank training improves neural network training on edge devices with non-volatile memory.
Study compares ANN and GARCH models for volatility prediction across sectors.
The MAXFLAT low-pass filter improves factor adjustment for better portfolio performance in China's stock market.
In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respect…
Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-Itô-OI model, we assume that the option-implied…
The paper introduces a new volatility model for state heterogeneous financial markets using high-frequency data.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
We use the expectation of the range of an arithmetic Brownian motion and the method of moments on the daily high, low, opening and closing prices to estimate the volatility of the stock price. The daily price jump at the opening is considered to be the result of the unobserved evolution of an after-hours virtual tradin…
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity markets. An empirical probability density function (pdf) of volatility is obtained and compared with the theoretical predictions of a lognormal model and of the Hull and White model. The lognormal model well describes the pd…
FlashIV solves Black-Scholes implied volatility efficiently and accurately.
We consider an interest rate model with log-normally distributed rates in the terminal measure in discrete time. Such models are used in financial practice as parametric versions of the Markov functional model, or as approximations to the log-normal Libor market model. We show that the model has two distinct regimes, a…
New DMEM models forecast volatility combining low- and high-frequency data.
This study develops a multi-factor framework where not only market risk is considered but also potential changes in the investment opportunity set. Although previous studies find no clear evidence about a positive and significant relation between return and risk, favourable evidence can be obtained if a non-linear rela…
Modeling cryptocurrency volatility and jumps with SVCJ model.
This paper investigates the relationship between price multiscaling and volatility roughness in financial markets.
Volatility forecasting and return prediction in high-frequency Chinese equity markets.
We study several aspects of the so-called low-vol and low-beta anomalies, some already documented (such as the universality of the effect over different geographical zones), others hitherto not clearly discussed in the literature. Our most significant message is that the low-vol anomaly is the result of two independent…
We present a comprehensive theory of homogeneous volatility (and variance) estimators of arbitrary stochastic processes that fully exploit the OHLC (open, high, low, close) prices. For this, we develop the theory of most efficient point-wise homogeneous OHLC volatility estimators, valid for any price processes. We intr…
Study improves forecast accuracy of daily volatility to enhance portfolio performance.
Two new rational formulae for normal implied volatility are presented.
Paper proposes a method to robustly estimate volatility from OTM options.
Study uses sentiment analysis to predict implied volatility surface, improving prediction accuracy.
Study tests rough fractional volatility model across different time scales, revealing new volatility patterns.
We tackle the calibration of the so-called Stochastic-Local Volatility (SLV) model. This is the class of financial models that combines the local and stochastic volatility features and has been subject of the attention by many researchers recently. More precisely, given a local volatility surface and a choice of stocha…
Mounting empirical evidence suggests that the observed extreme prices within a trading period can provide valuable information about the volatility of the process within that period. In this paper we define a class of stochastic volatility models that uses opening and closing prices along with the minimum and maximum p…
In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockc…
ARL and Hawkes processes improve market-making strategies with variable volatility.
We invert the Black-Scholes formula. We consider the cases low strike, large strike, short maturity and large maturity. We give explicitly the first 5 terms of the expansions. A method to compute all the terms by induction is also given. At the money, we have a closed form formula for implied lognormal volatility in te…
Paper predicts stock volatility using ESG news, showing deep learning's effectiveness.
The implied volatility is a crucial element of any financial toolbox, since it is used for quoting and the hedging of options as well as for model calibration. In contrast to the Black-Scholes formula its inverse, the implied volatility, is not explicitly available and numerical approximation is required. We propose a …
We present a theory of homogeneous volatility bridge estimators for log-price stochastic processes. The main tool of our theory is the parsimonious encoding of the information contained in the open, high and low prices of incomplete bridge, corresponding to given log-price stochastic process, and in its close value, fo…
We study the relationship between price spread, volatility and trading volume. We find that spread forms as a result of interplay between order liquidity and order impact. When trading volume is small adding more liquidity helps improve price accuracy and reduce spread, but after some point additional liquidity begins …
New model reduces volatility parameters and complexity.
Model captures rough volatility and jump clustering in stock vol dynamics.
Using a method rooted in information theory, we present results that have identified a large set of stocks for which social media can be informative regarding financial volatility. By clustering stocks based on the joint feature sets of social and financial variables, our research provides an important contribution by …
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
The study explains how market-makers' hedging affects stock volatility during gamma-squeeze events.
Study the hedging of cryptocurrency options in a volatile market.
CV outperforms mean-variance for stock returns, minimizing risk and maximizing growth.
This paper improves VAE-based imputation of FX implied volatilities, reducing errors and handling uncertainty.
Study approximates rough stochastic volatility models using diffusion processes.
Study evaluates three position sizing methods for put-writing on S&P 500 Index options.
In financial markets, greater volatility is usually considered synonym of greater risk and instability. However, large market downturns and upturns are often preceded by long periods where price returns exhibit only small fluctuations. To investigate this surprising feature, here we propose using the mean first hitting…
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…
Predicts stock volatility using Twitter data and random forests.