Volatility

We Don’t Quite Know What We are Talking About When We Talk About Volatility ArXiv ID: ssrn-970480 “View on arXiv” Authors: Unknown Abstract Finance professionals, who are regularly exposed to notions of volatility, seem to confuse mean absolute deviation with standard deviation, causing an underesti Keywords: Volatility, Risk Management, Standard Deviation, Statistical Analysis Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper relies on a conceptual mathematical argument about Jensen’s inequality and the relationship between standard deviation and mean absolute deviation, but the core math is relatively simple. Empirical rigor is high due to the conducted survey (87 participants across three professional groups) with presented statistical results (frequency histograms, error ratios) and clear data collection methodology. flowchart TD A["Research Question: Do finance professionals<br>understand volatility?"] --> B["Key Methodology: Survey<br>and statistical analysis"] B --> C["Data/Inputs: Responses from<br>finance professionals"] C --> D["Computation: Calculate and compare<br>Mean Absolute Deviation vs Standard Deviation"] D --> E["Key Findings/Outcomes:<br>Confusion between MAD and SD<br>Underestimation of volatility"]

Joint deep calibration of the 4-factor PDV model ArXiv ID: 2507.09412 “View on arXiv” Authors: Fabio Baschetti, Giacomo Bormetti, Pietro Rossi Abstract Joint calibration to SPX and VIX market data is a delicate task that requires sophisticated modeling and incurs significant computational costs. The latter is especially true when pricing of volatility derivatives hinges on nested Monte Carlo simulation. One such example is the 4-factor Markov Path-Dependent Volatility (PDV) model of Guyon and Lekeufack (2023). Nonetheless, its realism has earned it considerable attention in recent years. Gazzani and Guyon (2025) marked a relevant contribution by learning the VIX as a random variable, i.e., a measurable function of the model parameters and the Markovian factors. A neural network replaces the inner simulation and makes the joint calibration problem accessible. However, the minimization loop remains slow due to expensive outer simulation. The present paper overcomes this limitation by learning SPX implied volatilities, VIX futures, and VIX call option prices. The pricing functions reduce to simple matrix-vector products that can be evaluated on the fly, shrinking calibration times to just a few seconds. ...

Multiscale Causal Analysis of Market Efficiency via News Uncertainty Networks and the Financial Chaos Index ArXiv ID: 2505.01543 “View on arXiv” Authors: Masoud Ataei Abstract This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty. ...

Empirical Study on the Factors Influencing Stock Market Volatility in China ArXiv ID: 2501.08668 “View on arXiv” Authors: Unknown Abstract This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China’s Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Shanghai Composite Index return, and the main independent variables are the spot exchange rate of the RMB against the US dollar, the 10-year treasury bond yields in China and the United States and their lagged variables, with the effect of the time factor added. Firstly, the development of the stock, foreign exchange and bond markets and the basic theories are reviewed, and then each variable is analyzed by descriptive statistics, the correlation between the independent variables and the dependent variable is expanded theoretically, and the corresponding empirical analyses are briefly introduced, and then the empirical analyses and modeling of the relationship between the independent variables and the dependent variable are carried out on the basis of the theoretical foundations mentioned above with the support of the daily data, and the model conclusions are analyzed economically through a large number of tests, then the model conclusions are analyzed economically. economic analysis of the model conclusions, and finally, the author proposes three suggestions to enhance the stability and return of the Chinese stock market, respectively. Key Words: Chinese Stock Market, Volatility, GARCH, ARDL Model ...

From Votes to Volatility Predicting the Stock Market on Election Day ArXiv ID: 2412.11192 “View on arXiv” Authors: Unknown Abstract Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them to specific scenarios has become increasingly important. Election Day represents one such critical scenario, characterized by intensified market volatility, as the winning candidate’s policies significantly impact various economic sectors and companies. To address this challenge, we propose the Election Day Stock Market Forecasting (EDSMF) Model. Our approach leverages the contextual capabilities of large language models alongside specialized agents designed to analyze the political and economic consequences of elections. By building on a state-of-the-art architecture, we demonstrate that EDSMF improves the predictive performance of the S&P 500 during this uniquely volatile day. ...

Riding Wavelets: A Method to Discover New Classes of Price Jumps ArXiv ID: 2404.16467 “View on arXiv” Authors: Unknown Abstract Cascades of events and extreme occurrences have garnered significant attention across diverse domains such as financial markets, seismology, and social physics. Such events can stem either from the internal dynamics inherent to the system (endogenous), or from external shocks (exogenous). The possibility of separating these two classes of events has critical implications for professionals in those fields. We introduce an unsupervised framework leveraging a representation of jump time-series based on wavelet coefficients and apply it to stock price jumps. In line with previous work, we recover the fact that the time-asymmetry of volatility is a major feature. Mean-reversion and trend are found to be two additional key features, allowing us to identify new classes of jumps. Furthermore, thanks to our wavelet-based representation, we investigate the reflexive properties of co-jumps, which occur when multiple stocks experience price jumps within the same minute. We argue that a significant fraction of co-jumps results from an endogenous contagion mechanism. ...

Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization ArXiv ID: 2402.08108 “View on arXiv” Authors: Unknown Abstract We propose a new method for finding statistical arbitrages that can contain more assets than just the traditional pair. We formulate the problem as seeking a portfolio with the highest volatility, subject to its price remaining in a band and a leverage limit. This optimization problem is not convex, but can be approximately solved using the convex-concave procedure, a specific sequential convex programming method. We show how the method generalizes to finding moving-band statistical arbitrages, where the price band midpoint varies over time. ...

Exploring the Impact: How Decentralized Exchange Designs Shape Traders’ Behavior on Perpetual Future Contracts ArXiv ID: 2402.03953 “View on arXiv” Authors: Unknown Abstract In this paper, we analyze traders’ behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patterns of trader behavior in relation to trading volume, open interest, liquidation, and leverage. Our detailed examination of DEXs, especially those utilizing the Virtual Automated Market Making (VAMM) Model, uncovers a differential impact of open interest on long versus short positions. In exchanges which operate under the Oracle Pricing Model, we find that traders primarily act as price takers, with their trading actions reflecting direct responses to price movements of the underlying assets. Furthermore, our research highlights a significant propensity among less informed traders to overreact to positive news, as demonstrated by an increase in long positions. This study contributes to the understanding of market dynamics in digital asset exchanges, offering insights into the behavioral finance for future innovation of decentralized finance. ...

Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain ArXiv ID: ssrn-3586723 “View on arXiv” Authors: Unknown Abstract Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundame Keywords: COVID-19, Volatility, Market Turbulence, Risk Management, Crisis Economics, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses advanced econometric methods (e.g., time-series regressions with fixed effects) but is fundamentally an empirical study relying on a massive proprietary transaction dataset (23 million transactions) to analyze real-world consumer behavior, with no code/backtests presented but heavy data and implementation details. flowchart TD A["Research Goal:<br>Assess UK consumer<br>spending volatility<br>amid COVID-19"] --> B["Data Source:<br>UK Finance Admin Data<br>(n = 70M accounts)"] B --> C["Methodology:<br>Panel Regression &<br>Time-Series Analysis"] C --> D["Computational Process:<br>Compare Pre/Post-<br>Pandemic Spending Trends"] D --> E["Key Finding 1:<br>Immediate spending<br>contraction (Mar 2020)"] D --> F["Key Finding 2:<br>Shift from services<br>to durable goods"] D --> G["Key Finding 3:<br>Volatility spiked;<br>uncertainty persisted"]