International Journal of Financial Engineering

To meet carbon peak and neutrality targets, accurate carbon trading price forecasting is very important for enterprises making emission reduction decisions. By fusing convolutional neural network (CNN) and long short-term memory network (LSTM), the CNN–LSTM model is constructed. After variational mode decomposition (VMD), several intrinsic mode functions (IMFs) components are obtained and input into the CNN–LSTM model, thus constructing the combined sooty tern optimization algorithm (STOA)–VMD–CNN–LSTM forecasting model. To test this model, the carbon trading prices of the carbon emission trading markets of Hubei, Guangdong and Shenzhen were forecast. The prediction performance of the STOA–VMD–CNN–LSTM model is compared with ARIMA, BP, CNN and LSTM benchmark models and models combining different decomposition technologies. The international carbon trading price (EUR and CER) is used for prediction. Compared with other methods, the developed model makes fewer errors and achieves superior performance. Several important implications are provided for investors and risk managers involved in carbon financial products.

This paper mainly studies the interdependence structure and risk transmission between China’s north–south capital flow and the stock market and foreign exchange market. The results show that the correlation between southward capital and the mainland stock market is stronger, and the correlation between northward capital and the Hong Kong stock market is stronger. The correlation between northward capital and RMB exchange rate is greater than that between southward capital and RMB exchange rate. After the outbreak of COVID-19, the correlation between capital flows and the stock market has strengthened, while the correlation with the foreign exchange market has weakened. Furthermore, there is a bidirectional risk transmission between the north–south capital flow and China’s stock market and foreign exchange market. In general, the north–south capital flow has a greater impact on the stock market, and the exchange rate of RMB/US dollar has a greater impact on the north–south capital flow. This study has implications for investors and policymakers.

This paper examines the nonlinear (NL) asymmetric long- and short-run interconnections between energy indices (S&P 500-ENERGY, MSCI-CHINA, and MSCI-WORLD) and the crypto (Bitcoin) market. The daily data have been used from January 1, 2013, to February 24, 2023, archived from the wind database. We use a nonlinear autoregressive distributed lag (NARDL) framework for the research to be empirical. The NARDL test reveals the presence of a nonlinear asymmetric relationship, both in the long-run and short-run, among the MSCI-China, MSCI-World, S&P500-ENERGY indices, and the price of Bitcoin. The S&P 500 energy index and Bitcoin price have an inverse asymmetric connection. The prices of Bitcoin are positively influenced by both positive and negative shifts in the MSCI-CHINA and MSCI-WORLD indices. Overall, this study underscores the growing significance of the cryptocurrency market as a hedge against risks, offering evidence that Bitcoin can be perceived as a safe haven amidst the turbulence of the energy market. This insight holds implications for Bitcoin investors, suggesting its potential as a refuge in times of energy market instability.

We derive an explicit asymptotic approximation for implied volatilities of caplets under the assumption that the short-rate is described by a generic quadratic term-structure model. In addition to providing an asymptotic accuracy result, we perform numerical experiments in order to gauge the accuracy of our approximation.

Various studies have attempted to explore rainfall index (RI)-based derivatives as a tool to hedge rainfall risk. But no study delved into the issue to discuss the potential deployment of those tools for hedging rainfall risk on a model based on RI-based futures (RIBFs). An illustrative analysis of RIBFs contracts as hedging instruments has been undertaken in this study. The study is fundamentally based on empirical data. A brief account of the rainfall risk profile is presented to indicate the risk diversification opportunity. Besides, the results of an analysis of the correlation between the RI and the stock market index are projected to establish the RI as a unique asset class. The monsoon rainfall data for 50 years, i.e., from 1971 to 2020 have been considered. The Sensex and Nifty stock market indices data covered a period of 30 years, from 1991 to 2020. The study used correlation, the GARCH (1,1) model, and ratio analysis to test the hypotheses. A hypothetical insurance company representing major stakeholders in need of rainfall risk hedging has been taken as an example for evaluating the utility of RIBFs contracts as hedging tools. The results reveal that the party buying RIBFs contracts can significantly reduce the loss of revenue due to adverse monsoon outcomes.

This paper investigates the dynamics of cross-listing in the US and accounting quality. Using an unbalanced panel of 11,780 observations for the period 2000–2019, we show that cross-listing improves accounting quality (in terms of conservatism, earnings management and informational relevance). This result holds for both types of cross-listing (i.e., exchange and OTC listing). Moreover, taking institutional variables into account leads to the conclusion that companies from countries with a weak legal and informational environment have better accounting quality following compliance with the strict standards of shareholder protection and disclosure of information imposed by US authorities. Finally, a complementary analysis shows us that companies listed in the US benefit from a better accounting quality record and less investment inefficiency. This explains why cross-listing in the US reduces the inefficiency of investments thanks to better accounting quality.

This paper addresses the “curse of dimensionality” in the loss valuation of credit risk models. A dimension reduction methodology based on the Bayesian filter and smoother is proposed. This methodology is designed to achieve a fast and accurate loss valuation algorithm in credit risk modeling, but it can also be extended to valuation models of other risk types. The proposed methodology is generic, robust and can easily be implemented. Moreover, the accuracy of the proposed methodology in the estimation of expected loss and value-at-risk (VaR) is illustrated by numerical experiments. The results suggest that, compared to the currently most used Principal Component Analysis (PCA) approach, the proposed methodology provides more accurate estimation of expected loss and VaR of a loss distribution.

The Black–Scholes equation is an important analytical tool for option pricing in finance. This paper discusses the Lie symmetry analysis of bi-fractional Black–Scholes equation derived by the fractional G-Brownian motion. Then some exact solutions are obtained and the figures of which are presented to illustrate the characteristics with different values of the parameters. In addition, the new conservation theorem and the generalization of the Noether operators are developed to construct the conservation laws for bi-fractional Black–Scholes equation.

In the most of the option price dynamics in the financial markets, the transaction cost of option is ignored. Considering the transaction costs will lead to the emergence of models with nonlinear PDE. In this paper, transaction cost of option in the assumed market is considered, and the resulting dynamic is a nonlinear PDE, whose exact and numerical solutions have been computed in the present paper. To find the exact solution of the cited nonlinear equation, the Lie group algebra method has been used. The numerical solution has been given using the Chebyshev spectral method. In this method, the solution of the considered equation is approximated using Chebyshev polynomials. The convergence of the obtained polynomials to the solution of the differential equation has been shown, as well.

Investment is the action of saving something at present for the probability of earning return in future. Women have broken various personal and social hindrances to earn and manage their money for the purpose of safeguarding their future. The factors that highly influence investment satisfaction include profitability, liquidity, tax implications, investment risk, investment stability, investment regulation, investment location, investment decision and investment service provider. The main purpose of the study is to analyze the factors that influence the investor’s satisfaction towards various investment avenues. It is very much challenging for the companies and various financial marketers, to understand the factors that influence the satisfaction of women investors. In order to earn return, the investors choose the suitable investment option based on their income and savings pattern. Factor analysis, linear multiple regression analysis and one-way analysis of variance have been used for data analysis. The result of the study states that various demographic factors and investment details influence the investment satisfaction of women investors.