International Journal of Financial Engineering

This paper investigates the factors that interact with interest rates. The results show that interest rates positively correlate with deviations in inflation, the logarithm of the real GDP (gross domestic product) ratio, the logarithm of the money supply ratio, deviations in unemployment rates, money velocity, deficit, and the dollar index. The interest rates negatively correlate with debt, the Dow Jones Industrial Average (DJIA), debt to GDP ratio, and real GDP growth rate. Regression analysis shows with statistical significance an increase in interest rates with deviations in inflation, money velocity, the dollar index, and a decrease with the DJIA. The analysis is refined by removing insignificant variables and addressing a multicollinearity problem. The results could provide governments and central banks with focal points for developing interest rate policies.

The landscape of earnings has become commonplace with evolving social, cultural, personal, psychological, and environmental variables empowering women to earn on par with men. In response to the increasing economic factors, it has become imperative for women to invest, safeguarding against present and future uncertainties. This study aims to scrutinize, identify, and evaluate the investment behavior and decision-making processes of women investors. Traditionally, future investment decisions were exclusive to men, but in today’s world, women are gradually assuming this role. Recognizing the distinct financial habits of men and women, this study endeavors to explore the factors influencing the investment behavior of working women from their perspective. Data were gathered from 385 working women in Chennai City using the snowball sampling technique. The data were analyzed through exploratory factor analysis, and the investment behavior of working women was examined across five major dimensions: investment awareness, investment attributes, investment preference, investment decision, and investment satisfaction. The study findings indicate that sub-factors within each dimension significantly influence the investment behavior of working women.

The ravages of COVID-19 have had a huge impact on the global economy, and in the grain market, grain prices have fluctuated greatly due to the impact of factors such as rising circulation costs and sudden panicky demand, which has increased the business risks of sellers and deep-processing enterprises. To effectively reduce and avoid the risks brought by COVID-19, this paper takes utility maximization as the goal and helps grain enterprises manage grain price risks by constructing optimal dynamic behavioral hedging strategies. Therefore, this paper first measures the corn price risk during COVID-19, and then, fits the corn spot and futures yield fluctuations based on the DCC-GARCH model, which highlights the model’s advantages for portraying the corn spot and futures price fluctuations during COVID-19. Subsequently, the dynamically optimal hedging ratio is solved from the perspective of behavioral hedging by combining the loss aversion (LA) utility function. Finally, the LA-DCC-GARCH model is compared with the Minimum variance (MV)-DCC-GARCH model in terms of hedging performance, which highlights the conclusion that the behavioral hedging strategy significantly outperforms the traditional hedging strategy.

This short paper derives a formula for the risk-neutral density function at discrete points over the strike range using Abel’s summation formula in terms of the arbitrage-free call price function in a European option contingent. The estimate of risk-neutral density is crucial from a risk management perspective and other crucial assessments.

The research examines the effect of the COVID-19 outbreak on the dynamic co-movement of the stock markets of China, Japan, and the USA using a VAR model and a DCC-GJR-GARCH model. Especially, this study focuses on during and preceding the COVID period. This study data period extends from 1 January 2016 to 31 April 2022. The results demonstrate that COVID-19’s effect increases stock market volatility. Meanwhile, the VAR model revealed that the USA’s exogeneity was greater during COVID-19. In addition, the pre-crisis Granger Causality between China–USA and Japan–China is substantially higher than during the crisis. The findings of DCC-GJR-GARCH indicate the presence of volatility clustering in each of the stock markets. Moreover, the results suggest that the time-varying correlations between China and the USA during the pre-COVID period are greater than during the COVID period. The study’s findings highlight that investors attempting to increase investment diversification opportunities worldwide should always consider dynamic co-movement in different periods to maximize returns and minimize risk.

Fluctuations in interconnectedness within different industrial sectors are hence critical not only for financial market stability but also for the development of real economy. Thus, this paper contractures the varying network structure and estimates the tail risk spillover linkages at the industry level by applying the TENET (Tail-Event driven NETwork) framework on China’s 24 sectors from 2007 to 2018. There is a higher level of risk spillovers and spatial linkages between sectors during turmoil periods, and the centrality indicators can effectively identify the systemically important industries. Notably, it is more comprehensive and accurate to judge the systemic importance node by considering its location and connection in the network rather than just its internal attributes. Furthermore, the high-order polynomial regressions show that degree and eigenvector centrality have a significantly positive effect on risk contagion, while closeness centrality has a two-regime (“both robust and fragile”) contagion mechanism. From a policy point of view, regulators could formulate early-warning measures based on the impact mechanism and spread paths of systemic risk, especially in financial turmoil periods. Meanwhile, they should pay more attention to preventing the aggregate risks of the most influential sectors.

This paper aims to develop a supervised deep learning scheme to compute call option prices for the Barndorff-Nielsen and Shephard model with a non-martingale asset price process having infinite active jumps. In our deep learning scheme, teaching data are generated through the Monte Carlo method developed by [ (). Monte Carlo simulation for Barndorff-Nielsen and Shephard model under change of measure, Mathematics and Computers in Simulation, 218, 223–234]. Moreover, the BNS model includes many parameters, which makes the deep learning accuracy worse. Therefore, we will create another input parameter using the Black–Scholes formula. As a result, the accuracy is improved dramatically.

Crude oil prices wield substantial influence over economic stability and sustainability, exerting a profound impact across various sectors and significantly moulding the economic well-being of nations. Thus, precision of predicting crude oil prices is of utmost importance for a wide array of stakeholders, including policymakers, investors, and participants in the energy market. This study offers an empirical exploration of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMA-X) method, employing RMSE and MAPE values for forecasting crude oil prices during the most volatile periods from 2020 to 2023, including both COVID-19 pandemic and Russia Ukraine war period. The results indicate that the SARIMA-X method is effective for predicting crude oil prices during turbulent market conditions. This model can be a valuable tool for investors, traders, and other market participants, enabling them to make informed decisions when it comes to both intraday trading and long-term forecasting of crude oil prices.

In this paper, we investigate the optimal hedging strategy in a continuous time framework that is more adequate for commodities. We consider the consumption-investment problem where all asset prices follow mean-reverting jump-diffusion processes. The optimal investment and consumption strategies are derived in closed form. The framework is used to address one of the major risk factors faced by commodity producers. We show that a commodity producer will be better off hedging his/her futures contracts by simultaneously investing in foreign exchange products to minimize the adverse impacts of the jump risk prevalent in commodity prices.

Stock returns demonstrate different levels of sensitivity to marketwide sentiment fluctuations. Previous studies argue that stock sentiment risk is caused by information opacity and that companies lacking information transparency tend to be young, small, paying no dividend, volatile, and fast growing. However, little direct evidence exists regarding the impact of information transparency on stock sentiment sensitivity/beta. This paper contributes to fill this gap by employing proximate measures of information transparency: quality of accruals and earnings, and accuracy of analyst forecast. Empirical results validate that information transparency indeed helps curb stock sentiment beta. Such an impact is more pronounced during periods of low market sentiment when irrational investors are mostly sidelined. Two mediating factors are identified: noise trading and stable institutional shareholding. Additionally, improving information transparency on corporate governance also constrains stock sentiment sensitivity. Our results are robust to alternative measures and the endogeneity concern.