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We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models that are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multifactor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility. This article has online supplementary materials.

State-of-the-art stochastic volatility models generate a \"volatility smirk\" that explains why out-of-the-money index puts have high prices relative to the Black-Scholes benchmark. These models also adequately explain how the volatility smirk moves up and down in response to changes in risk. However, the data indicate that the slope and the level of the smirk fluctuate largely independently. Although single-factor stochastic volatility models can capture the slope of the smirk, they cannot explain such largely independent fluctuations in its level and slope over time. We propose to model these movements using a two-factor stochastic volatility model. Because the factors have distinct correlations with market returns, and because the weights of the factors vary over time, the model generates stochastic correlation between volatility and stock returns. Besides providing more flexible modeling of the time variation in the smirk, the model also provides more flexible modeling of the volatility term structure. Our empirical results indicate that the model improves on the benchmark Heston stochastic volatility model by 24% in-sample and 23% out-of-sample. The better fit results from improvements in the modeling of the term structure dimension as well as the moneyness dimension.

Finance serves as the lifeblood of the real economy’s development, fundamentally aimed at bolstering the real economy and mitigating financial risks. This paper investigates the application of machine learning technologies in finance, organizing a detailed classification and analysis of associated risks within the sector. Specifically, the study employs the Conditional Value at Risk (CoVaR) to quantify systemic financial risks. It introduces both the Fama-French three-factor and five-factor models for constructing multifactor financial models. It assesses the significance and regression coefficients of risk factors within these models to provide a comparative analysis. Moreover, this research develops an early warning model for financial risk by incorporating financial risk measurement indices into the XGBoost machine learning framework. It utilizes the SHAP (Shapley Additive exPlanations) explanatory framework to elucidate the key features influencing financial risk. This comprehensive approach not only enhances the understanding of financial risk dynamics but also advances the predictive capabilities of financial risk management. It was found that the modified R² increased from 36.52% to 56.25% after adding the IMU factor to the three-factor model, and the coefficient of SMB decreased from 0.135 to −0.215 the larger the size of the enterprise. The accuracy of the financial risk early warning model was 96.35%, and the probability of being predicted by the model to be a high-risk sample was lower when the value of the characteristic INDI was taken as [0,0.125]. Financial institutions can enhance their risk prevention ability by combining the financial risk factors obtained from the multifactor model with the supervised learning algorithm.

PurposeOur aim in this study is to investigate the relative importance of the economic policy uncertainty and of the geopolitical risk on U.S. REITs (Real Estate Investment Trusts) returns with a special focus on the different real estate sectors.Design/methodology/approachWe use an augmented Fama-French (1993)’s asset pricing model, including economic policy uncertainty indices (EPU), introduced by Baker et al. (2016), and geopolitical risk indices (GPR) recently developed by Caldara and Iacoviello (2022), to price the potential risk factors for U.S. Nareit indices returns. To obtain robust economic results, we correct for the problems of errors-in-variables in linear asset pricing models; we advocate the use of higher moments estimators as instruments in a generalized method of moments (GMM) framework.FindingsOur results report that economic policy uncertainty (EPU), and geopolitical risk (GPR) are priced for the different Nareit sectors for the last three decades. The GPR index stands as a relevant risk factor. The coefficient estimates are low compared to Fama-French risk factors. They are higher for Shopping Centers, Retail and Region Malls and lower for Health Care and Lodging/Resorts. EPU indices are also priced and less statistically significant. Health Care sector, followed by Shopping Centers and Retail are the most policy-sensitive sectors.Practical implicationsIn their “2023–2024 Top Ten Issues Affecting Real Estate” “political unrest and global economic health” is ranked 1 issue by the Counselors of Real Estate. Our results report that economic policy uncertainty and geopolitical risk are priced for the different Nareit sectors. They suggest implications for investors, insurers, bankers, policymakers and other stakeholders. The geopolitical risk index (GPR) stands as a relevant and significant risk factor for REITs returns.Originality/valueBased on parsimonious robust asset pricing models, the results shed a new light on the relative importance of geopolitical risk and economic policy uncertainty in the real estate sector, with a special focus on the different U.S. REITs sectors. They suggest possible implications for investors, insurers, bankers, policymakers and other stakeholders in a context marked by higher uncertainty shocks and geopolitical risks.

Current research indicates that aggressive sexual fantasies (ASF) are related to sexual aggression, above and beyond other risk factors for this behavior. There have, however, rarely been explicitly considered in multifactor models aiming to explain sexual aggression. One exception is the multifactorial Revised Confluence Model of Sexual Aggression that was replicated in two samples of male individuals who were convicted of sexual offenses and a small sample of men from the general population and evidenced a high relevance of ASF, respectively. There were, however, no further attempts to replicate the model in larger samples from the general population. We, therefore, used a subsample from the Finnish Genetics of Sexuality and Aggression project including 3269 men (age: M  = 26.17 years, SD  = 4.76) to do so. Cross-sectional latent structural equation models corroborated previous research and the assumption that ASF are a central component in multifactor models that aim to explain sexual aggression: ASF and antisocial behavior/aggression were equally important associates of sexual coercion when also considering adverse childhood experiences, hypersexuality, and callous-unemotional traits. Additionally, ASF mediated the links between hypersexuality, callous-unemotional traits, as well as childhood sexual abuse and sexual coercion. These links held stable when entering further risk factors, that is, distorted perceptions, rape-supportive attitudes, and violent pornography consumption into the model. Contrasting assumptions, alcohol consumption and antisocial behavior/aggression did not interact. These results illustrate the potential importance of ASF for sexual aggression. They indicate that ASF require consideration by research on sexual aggression as well as in the treatment and risk assessment of sexual perpetrators.

This study investigates the natural gas diffusion coefficients in tight sandstones, emphasizing the Sulige area′s geological conditions. Through experimental measurements and the simulation of natural gas diffusion processes, we explore the impact of various geological factors—including temperature, pressure, porosity, permeability, pore throat radius, and clay content—on gas diffusion coefficients. Our findings reveal that temperature and porosity positively influence diffusion, aligning with molecular collision theory, and displaying a linear relationship with the diffusion coefficient, respectively. Conversely, pressure and clay content negatively affect diffusion, with coefficients showing exponential decreases under higher pressures and increased clay content. Permeability and pore throat radius enhance diffusion in a logarithmic manner. Building on these individual relationships, we developed a multifactor model to accurately predict the gas diffusion coefficients in tight sandstones under diverse geological settings. Validation with the actual sample measurements confirms the precision of our model and its applicability across different geological periods and conditions. Our research offers valuable insights into understanding natural gas diffusion in tight sandstones, providing a solid foundation for further exploration and exploitation strategies in similar geological settings.

In complex financial markets, controlling risks while achieving high returns is a challenge for investors. Faced with market uncertainty and complexity, traditional investment strategies often struggle to meet the needs of modern investors. To address this issue, a new investment portfolio strategy was proposed by integrating the multifactor model with the upper confidence bound. Meanwhile, genetic algorithm was used to optimize and improve the weight allocation of the investment portfolio based on the upper confidence bound. These results confirmed that the cumulative return of GA-UCB was 187.4%, which was 68.3% higher than the cumulative return of 119.1% on the Shanghai and Shenzhen 300 indices, respectively. The maximum drawdown rate of GA-UCB was 13.5%, a decrease of 4.8% compared to the Shanghai and Shenzhen 300. In summary, the research on financial investment optimization by integrating multifactors and GA improved UCB effectively improves returns while controlling risks, providing a new perspective and tool for financial market investors.

Purpose This paper aims to investigate not only the performance of Portuguese mutual funds investing in domestic and international equities but also which fund characteristics, such as age, size, family size, expense ratios and flows, influence future performance. Design/methodology/approach Fund performance is evaluated over the 2005–2022 period by a robust six-factor model, while the impact of fund characteristics on performance is assessed by a set of fixed-effects panel data regressions with two-way cluster-robust standard errors. Findings The results show that, while funds investing in domestic equities predominantly exhibit neutral performance, most international equity funds have significantly negative alphas. The authors document a negative and statistically significant relationship between fund age and performance for all fund categories. Total expense ratios have an inverse relationship with domestic equity fund performance but do not impact the performance of international equity funds significantly. Though fund flows have a neutral effect on performance across the overall period, they are important determinants of both domestic and international funds’ performance in more recent years. Originality/value The authors contribute to the literature by carrying out a comprehensive analysis, based on recent and robust methodologies, of the impact of mutual fund characteristics on the future performance of Portuguese equity funds. The research findings serve as a premise for advising investors on how to choose the top-performing funds.

PurposeThis study aims to document the time varying risk premia for market, size, value and momentum factors for an emerging market using a sophisticated conditional asset pricing model. The focus of this study is Turkish stock market denominated in local currency with its peculiar risk premia.Design/methodology/approachThe authors employ Gagliardini et al.'s (2016) econometric method that uses cross-sectional and time series information simultaneously to infer the path of risk premia from individual stocks.FindingsUsing this methodology, the authors assess several conditioning information and conclude that local dividend yield, inflation and exchange rates have the most explanatory power. The authors document the time varying risk premia in Turkey over three decades.Originality/valueExisting studies on dynamic estimation of risk premia lack a consensus as to which state variables should be included and to what extent they impact the magnitude of the premium. The authors extend the conditioning information set beyond the ones existing in the literature to determine variables that are specifically important for an emerging market.

In the age of energy transition that we are going through today, many research studies discuss how to develop various approaches to making forecasts aimed at obtaining quantitative assessments of the technical and economic indicators of the energy industry. This paper considers the adaptation of a comprehensive approach to forecasting the structure of energy generation based on the factor and trend approach and using autoregressive and multifactor models that apply a linear regression tool with ridge regularization. To implement this approach, we propose a tool for automated selection of the factors that have the most significant impact on the change in the structure of energy generation. This approach allows us to forecast the dynamics of electricity generation by different types of generating facilities as affected by the key factors in energy transition in the short, medium, and long term. As a result, we obtained quantitative relationships for the energy generation structure. Over the next 10 years, the share of generation using renewable energy sources will increase to 10%, and the share of thermal power plants, on the contrary, will decrease to 50%, despite the growth in demand for electricity. Also, greenhouse gas emissions will be reduced by 30%. We have also provided scientific justification for the sufficient reliability of the forecasts we present.