Quantitative Finance

Gong, Shuyu; Hu, Taizhong; Zou, Zhenfeng

doi: 10.48550/arxiv.2507.09444pmid: N/A

Abstract:This paper proposes a novel class of generalized Expected-Shortfall (ES) norms constructed via distortion risk measures, establishing a unified analytical framework for risk quantification. The proposed norms extend conventional ES methodology by incorporating flexible distortion functions. Specifically, we develop the mathematical duality theory for generalized-ES norms to support portfolio optimization tasks, while demonstrating their practical utility through projection problem solutions. The generalizedES norms are also applied to detect anomalies of financial time series data.

Börner, Christoph J.; Hoffmann, Ingo

doi: 10.48550/arxiv.2507.08394pmid: N/A

Abstract:Models for spin systems, known from statistical physics, are applied analogously in econometrics in the form of agent-based models. The models discussed in the econophysics literature all use the state variable $T$, which, in physics, represents the temperature of a system. However, there is little evidence on how temperature can be measured in econophysics, so that the models can be applied. Only in idealized capital market applications has the relationship between temperature and volatility been demonstrated, allowing temperature to be determined through volatility measurements. The question remains how this can be achieved in agent systems beyond capital market applications. This paper focuses precisely on this question. It examines an agent system with two decision options in a news environment, establishes the measurement equation, and outlines the basic concept of temperature measurement. The procedure is illustrated using an example. In an application with competing subsystems, an interesting strategy for influencing the average opinion in the competing subsystem is presented.

Santarlasci, Lapo; Rungi, Armando; Zinilli, Antonio

doi: 10.48550/arxiv.2507.02287pmid: N/A

Abstract:This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains.

Schlegel, Christoph; Kilbourn, Quintus

doi: 10.48550/arxiv.2507.02027pmid: N/A

Abstract:We derive the arbitrage gains or, equivalently, Loss Versus Rebalancing (LVR) for arbitrage between \textit{two imperfectly liquid} markets, extending prior work that assumes the existence of an infinitely liquid reference market. Our result highlights that the LVR depends on the relative liquidity and relative trading volume of the two markets between which arbitrage gains are extracted. Our model assumes that trading costs on at least one of the markets is quadratic. This assumption holds well in practice, with the exception of highly liquid major pairs on centralized exchanges, for which we discuss extensions to other cost functions.

Platen, Eckhard

doi: 10.48550/arxiv.2507.18395pmid: N/A

Abstract:The paper derives the dynamics of a financial market from basic mathematical principles. It models the market dynamics using independent stationary scalar diffusions, assumes the existence of its growth optimal portfolio (GOP), interprets the market as a communication system, and minimizes, in an information-theoretical sense, the joint information of the risk-neutral pricing measure with respect to the real-world probability measure. In this information-minimizing market, its basic independent securities, their sums, minimum variance portfolio, and GOP, as well as the GOP of the entire market, represent squared radial Ornstein-Uhlenbeck processes with additivity and self-similarity properties.

Pontiggia, Milan

doi: 10.48550/arxiv.2507.00575pmid: N/A

Abstract:We assess the applicability of rough volatility models to Bitcoin realized volatility using the normalised p-variation framework of Cont and Das (2024). Applying this model-free estimator to high-frequency Bitcoin data from 2017 to 2024 across multiple sampling resolutions, we find that the normalised statistic remains strictly negative, precluding the estimation of a valid roughness index. Stationarity tests and robustness checks reveal no significant evidence of non-stationarity or structural breaks as explanatory factors. Instead, convergent evidence from three complementary diagnostics, namely Multifractal Detrended Fluctuation Analysis, log-log moment scaling, and wavelet leaders, reveals a multifractal structure in Bitcoin volatility. This behaviour violates the homogeneity assumptions underlying rough volatility estimation and accounts for the estimator's systematic failure. These findings suggest that while rough volatility models perform well in traditional markets, they are structurally misaligned with the empirical features of Bitcoin volatility.

Sprintson, Matthew; Oughton, Edward

doi: 10.48550/arxiv.2507.19989pmid: N/A

Abstract:It is estimated that over one-fourth of US households experienced a power outage in 2023, costing on average US $\$150$ Bn annually, with $87\%$ of outages caused by natural hazards. Indeed, numerous studies have examined the macroeconomic impact of power network interruptions, employing a wide variety of modeling methods and data parameterization techniques, which warrants further investigation. In this paper, we quantify the macroeconomic effects of three significant natural hazard-induced US power outages: Hurricane Ian (2022), the 2021 Texas Blackouts, and Tropical Storm Isaias (2020). Our analysis evaluates the sensitivity of three commonly used data parameterization techniques (household interruptions, kWh lost, and satellite luminosity), along with three static models (Leontief and Ghosh, critical input, and inoperability Input-Output). We find the mean domestic loss estimates to be US $\$3.13$ Bn, US $\$4.18$ Bn, and US $\$2.93$ Bn, respectively. Additionally, data parameterization techniques can alter estimated losses by up to $23.1\%$ and $50.5\%$. Consistent with the wide range of outputs, we find that the GDP losses are highly sensitive to model architecture, data parameterization, and analyst assumptions. Results sensitivity is not uniform across models and arises from important a priori analyst decisions, demonstrated by data parameterization techniques yielding $11\%$ and $45\%$ differences within a model. We find that the numerical value output is more sensitive than intersectoral linkages and other macroeconomic insights. To our knowledge, we contribute to literature the first systematic comparison of multiple IO models and parameterizations across several natural hazard-induced long-duration power outages, providing guidance and insights for analysts.

Roy, Soumita; Dihan, Md Muntasir Kamal; Haque, Tasnimah; Nomani, Nafisa; Preety, Sadia Islam

doi: 10.48550/arxiv.2507.21254pmid: N/A

Abstract:Purpose With an emphasis on elements like financial knowledge, financial attitude, social influence, financial self-efficacy, and financial management practices, this study explores the factors that influence employees' saving behavior in Dhaka, Bangladesh. We also welcome others to work on saving behavior, which is the main reason for publishing. The purpose is to make others aware of the methods for quantitative financial behavior analysis in Bangladesh. Design/methodology/approach The study uses a quantitative approach with a cross-sectional survey design. Data was collected from 40 participants through a structured questionnaire adapted from reliable sources. The questionnaire captured demographic information and used established items to measure the key variables. Data analysis included descriptive statistics, reliability analysis using Cronbachs alpha, and regression analysis to test the hypothesized relationships. Findings The results indicate that among the factors examined, only financial management practices had a significant positive relationship with saving behavior. Rest of the factors did not show significant relationships with saving behavior in this study sample. Limitation or Disclaimer It is still a work in progress, this paper is meant for pre-print with mostly incomplete and limited data. No data cleaning was performed, so it is very likely to include outliers and faulty data. Originality or value This study contributes to the limited research on saving behavior determinants in the Bangladeshi context, specifically among employees in the capital city of Dhaka. It explores the influence of multiple factors, including the rarely studied aspect of social influence.

Protte, Marius

doi: 10.48550/arxiv.2507.15568pmid: N/A

Abstract:This study replicates and adapts the experiment of Hoelzl and Rustichini (2005), which examined overplacement, i.e., overconfidence in relative self-assessments, by analyzing individuals' voting preferences between a performance-based and a lottery-based bonus payment mechanism. The original study found underplacement - the majority of their sample apparently expected to perform worse than others - in difficult tasks with monetary incentives, contradicting the widely held assumption of a general human tendency toward overconfidence. This paper challenges the comparability of the two payment schemes, arguing that differences in outcome structures and non-monetary motives may have influenced participants' choices beyond misconfidence. In an online replication, a fixed-outcome distribution lottery mechanism with interdependent success probabilities and no variance in the number of winners - designed to better align with the performance-based payment scheme - is compared against the probabilistic-outcome distribution lottery used in the original study, which features an independent success probability and a variable number of winners. The results align more closely with traditional overplacement patterns than underplacement, as nearly three-fourths of participants prefer the performance-based option regardless of lottery design. Key predictors of voting behavior include expected performance, group performance estimations, and sample question outcomes, while factors such as social comparison tendencies and risk attitudes play no significant role. Self-reported voting rationales highlight the influence of normative beliefs, control preferences, and feedback signals beyond confidence. These results contribute to methodological discussions in overconfidence research by reassessing choice-based overconfidence measures and exploring alternative explanations for observed misplacement effects.

LeFloc'h, Fabien

doi: 10.48550/arxiv.2507.13186pmid: N/A

Abstract:The COS method is a very efficient way to compute European option prices under Lévy models or affine stochastic volatility models, based on a Fourier Cosine expansion of the density, involving the characteristic function. This note shows how to compute the COS method formula with a non-uniform fast Fourier transform, thus allowing to price many options of the same maturity but different strikes at an unprecedented speed.