Quantitative Finance

Crawley, Andrew; Daigneault, Adam; Gendron, Jonathan

doi: 10.48550/arxiv.2503.06087pmid: N/A

Abstract:From 2000 to 2017, 64% of Maine's pulp and paper processing mills shut down; these closures resulted in harmful effects to communities in Maine and beyond. One question this research asks is how will key macroeconomic and related variables for Maine's forestry and logging industry change in the future? To answer this, we forecast key macroeconomic and related variables with a vector error correction (VEC model) to assess past and predict future economic contributions from Maine's forestry and logging industry. The forecasting results imply that although the contribution of the industry in Maine would likely remain stable due to level prices and a slight increase in output, local Maine communities could be worse off due to decreases in employment and firms. We then incorporated these forecasts into a 3-stage modeling process to analyze how a negative shock to exchange rates from an increase in tariffs could affect Maine's employment and output. Our results suggest that increased tariffs will reduce output and increase employment volatility in Maine. Rising uncertainty and costs of business operations suggest care should be taken when changing tariffs and trade restrictions, especially when changes to business operations can harm markets and communities.

Deng, Qi; Zhou, Zhong-guo

doi: 10.48550/arxiv.2503.08693pmid: N/A

Abstract:We construct liquidity-adjusted return and volatility using purposely designed liquidity metrics (liquidity jump and liquidity diffusion) that incorporate additional liquidity information. Based on these measures, we introduce a liquidity-adjusted ARMA-GARCH framework to address the limitations of traditional ARMA-GARCH models, which are not effectively in modeling illiquid assets with high liquidity variability, such as cryptocurrencies. We demonstrate that the liquidity-adjusted model improves model fit for cryptocurrencies, with greater volatility sensitivity to past shocks and reduced volatility persistence of erratic past volatility. Our model is validated by the empirical evidence that the liquidity-adjusted mean-variance (LAMV) portfolios outperform the traditional mean-variance (TMV) portfolios.

Liu, Xiangdong; Fu, Sicheng; Hong, Shaopeng

doi: 10.48550/arxiv.2503.00851pmid: N/A

Abstract:Volatility forecasting in financial markets is a topic that has received more attention from scholars. In this paper, we propose a new volatility forecasting model that combines the heterogeneous autoregressive (HAR) model with a family of path-dependent volatility models (HAR-PD). The model utilizes the long- and short-term memory properties of price data to capture volatility features and trend features. By integrating the features of path-dependent volatility into the HAR model family framework, we develop a new set of volatility forecasting models. And, we propose a HAR-REQ model based on the empirical quartile as a threshold, which exhibits stronger forecasting ability compared to the HAR-REX model. Subsequently, the predictive performance of the HAR-PD model family is evaluated by statistical tests using data from the Chinese stock market and compared with the basic HAR model family. The empirical results show that the HAR-PD model family has higher forecasting accuracy compared to the underlying HAR model family. In addition, robustness tests confirm the significant predictive power of the HAR-PD model family.

Liu, Yuhao; Yang, Nian; Zhang, Gongqiu

doi: 10.48550/arxiv.2503.11053pmid: N/A

Abstract:This paper develops general approaches for pricing various types of American-style Parisian options (down-in/-out, perpetual/finite-maturity) with general payoff functions based on continuous-time Markov chain (CTMC) approximation under general 1D time-inhomogeneous Markov models. For the down-in types, by conditioning on the Parisian stopping time, we reduce the pricing problem to that of a series of vanilla American options with different maturities and their prices integrated with the distribution function of the Parisian stopping time yield the American Parisian down-in option price. This facilitates an efficient application of CTMC approximation to obtain the approximate option price by calculating the required quantities. For the perpetual down-in cases under time-homogeneous models, significant computational cost can be reduced. The down-out cases are more complicated, for which we use the state augmentation approach to record the excursion duration and then the approximate option price is obtained by solving a series of variational inequalities recursively with the Lemke's pivoting method. We show the convergence of CTMC approximation for all the types of American Parisian options under general time-inhomogeneous Markov models, and the accuracy and efficiency of our algorithms are confirmed with extensive numerical experiments.

Mamun, Tuhin G. M. Al; Abdur, Rahim Md.; Hassan, Md. Sharif; Amin, Mohammad Bin; Oláh, Judit

doi: 10.48550/arxiv.2503.12178pmid: N/A

Abstract:The research investigates the effects of public spending on health and education in shaping the human development in south Asian three countries: India, Pakistan and Bangladesh. The study uses the VAR (Vector Auto regression) model to estimate the effects on government spending on these sectors to evaluate the human development. The findings state that there are different degrees of impact in these three countries. In Bangladesh and India, health spending increases the human development in short term. On the other hand education spending shows the significance on the this http URL, the study also highlights that there are different levels of effectiveness of government spending across these three countries. In order to maximize the human development an optimum country specific strategies should be adopted.

Jaber, Eduardo Abi; Guellil, Maxime

doi: 10.48550/arxiv.2503.02965pmid: N/A

Abstract:Fourier-based methods are central to option pricing and hedging when the Fourier-Laplace transform of the log-price and integrated variance is available semi-explicitly. This is the case for the Volterra Stein-Stein stochastic volatility model, where the characteristic function is known analytically. However, naive evaluation of this formula can produce discontinuities due to the complex square root of a Fredholm determinant, particularly when the determinant crosses the negative real axis, leading to severe numerical instabilities. We analyze this phenomenon by characterizing the determinant's crossing behavior for the joint Fourier-Laplace transform of integrated variance and log-price. We then derive an expression for the transform to account for such crossings and develop efficient algorithms to detect and handle them. Applied to Fourier-based pricing in the rough Stein-Stein model, our approach significantly improves accuracy while drastically reducing computational cost relative to existing methods.

Xu, Songci; Cheng, Qiangqiang; Lee, Chi-Guhn

doi: 10.48550/arxiv.2503.20987pmid: N/A

Abstract:In the realm of stock prediction, machine learning models encounter considerable obstacles due to the inherent low signal-to-noise ratio and the nonstationary nature of financial markets. These challenges often result in spurious correlations and unstable predictive relationships, leading to poor performance of models when applied to out-of-sample (OOS) domains. To address these issues, we investigate \textit{Domain Generalization} techniques, with a particular focus on causal representation learning to improve a prediction model's generalizability to OOS domains. By leveraging multi-factor models from econometrics, we introduce a novel error bound that explicitly incorporates causal relationships. In addition, we present the connection between the proposed error bound and market nonstationarity. We also develop a \textit{Causal Discovery} technique to discover invariant feature representations, which effectively mitigates the proposed error bound, and the influence of spurious correlations on causal discovery is rigorously examined. Our theoretical findings are substantiated by numerical results, showcasing the effectiveness of our approach in enhancing the generalizability of stock prediction models.

Zhang, Ting; Li, Peng-Fei; Zhou, Wei-Xing

doi: 10.48550/arxiv.2503.06599pmid: N/A

Abstract:The study examines the return connectedness between climate policy uncertainty (CPU), clean energy, fossil energy, and food markets. Using the time-domain method of Diebold and Yilmaz (2012) and frequency-domain methods of Barun{í}k and K{ř}hl{í}k (2018), we find substantial spillover effects between these markets. Furthermore, high frequency domain is the primary driver of overall connectedness. In addition, CPU is a net contributor of return shocks in the short term, whereas it turns to be a net recipient in the medium and long terms. Across all frequencies, clean energy and oils are consistent net recipients, while meat is a dominant net contributor.

Milazzo, Matteo; Musciotto, Federico; Piilo, Jyrki; Mantegna, Rosario N.

doi: 10.48550/arxiv.2503.17778pmid: N/A

Abstract:We study the long term dynamics of the stock portfolios owned by single Finnish legal entities in the Helsinki venue of the Nasdaq Nordic between 2001 and 2021. Using the Herfindahl-Hirschman index as a measure of concentration for the composition of stock portfolios, we investigate the concentration of Finnish household portfolios both at the level of each individual household and tracking the time evolution of an aggregated Finnish household portfolio. We also consider aggregated portfolios of two other macro categories of investors one comprising Finnish institutional investors and the other comprising foreign investors. Different macro categories of investors present a different degree of concentration of aggregated stock portfolios with highest concentration observed for foreign investors. For individual Finnish retail investors, portfolio concentration estimated by the Herfindahl-Hirschman index presents high values for more than half of the total number of retail investors. In spite of the observation that retail stock portfolios are often composed by just a few stocks, the concentration of the aggregated stock portfolio for Finnish retail investors has a portfolio concentration comparable with the one of Finnish institutional investors. Within retail investors, stock portfolios of women present a similar pattern of portfolios of men but with a systematic higher level of concentration observed for women both at individual and at aggregated level.

Crawley, Andrew; Daigneault, Adam; Gendron, Jonathan

doi: 10.48550/arxiv.2503.23569pmid: N/A

Abstract:Several key states in various regions of the U.S. have experienced recent sawtimber as well as pulp and paper mill closures, which raises an important policy question: how have and will key macroeconomic and industry specific indicators within the U.S. forest sector likely to change over time? This study provides empirical evidence to support forest-sector policy design by using a vector error correction (VEC) model to forecast economic trends in three major industries - forestry and logging, wood manufacturing, and paper manufacturing - across six of the most forest-dependent states found by the location quotient (LQ) measure: Alabama, Arkansas, Maine, Mississippi, Oregon, and Wisconsin. Overall, the results suggest a general decline in employment and the number of firms in the forestry and logging industry as well as the paper manufacturing industry, while wood manufacturing is projected to see modest employment gains. These results also offer key insights for regional policymakers, industry leaders, and local economic development officials: communities dependent on timber-based manufacturing may be more resilient than other forestry-based industries in the face of economic disruptions. Our findings can help prioritize targeted policy interventions and inform regional economic resilience strategies. We show distinct differences across forest-dependent industries and/or state sectors and geographies, highlighting that policies may have to be specific to each sector and/or geographical area. Finally, our VEC modeling framework is adaptable to other resource-dependent industries that serve as regional economic pillars such as mining, agriculture, and energy production offering a transferable tool for policy analysis in regions with similar economic structures.