International Journal of Financial Engineering

In this study, the currency exchange portfolios are optimized by applying an amalgamation of Coskewness and GARCH (1,1) models to ensure conditional skewness based on the GARCH (1,1) model, a new analysis technique is anticipated for the Conditional Skewness in auto-regressive uncertain instability. Applications of Mean–Variance through financial statistics confirm the validation of the projected evaluation process. The conditional skewness dependence on conditional variance is due to the proposed model’s effectiveness with the volatility feedback effect. For the Japanese stocks and the currency exchange rates, conditional risk-minimizing hedge strategies are estimated from July 2000 to June 2020. The empirical results illustrate that the best vibrant hedging strategies can moderately detain the currency rate variations seriously and decrease the currency rate risk by increasing and the portfolios’ risk-adjusted returns. Conditional skewness in the daily post-war Japanese stock returns confirms the importance of the model’s capability to optimize portfolios. All stock and currency exchange return portfolios are positively correlated with each other. Ultimately, our study results present that the exchange rate volatility can be modeled effectively by the GARCH model in a straightforward and uncomplicated method.

In this paper, the Shanghai Securities Composite Index and 18 A-share listed companies are used to justify the impact of contagion effects of media reports, investors’ sentiment and attention on stock market. Five indicators are built: The news media optimistic tendency, investors’ attention, investors’ sentiment, investors’ sentiment disagreement and media sentiment disagreement. Furthermore, theoretical models are constructed based on HAR-RV model to analyze the contagion structure between media sentiment and investors’ sentiment and its impact on the performance of stock market. Additionally, the “reverse silence spiral” theory is proposed to analyze the regulatory role of sentiment disagreement in the contagion effects according to the information communication theory. The empirical results demonstrate the following conclusions. (1) The optimism degree of media reports positively affects investors’ subjective sentiment and increases their transaction volume. (2) Strengthening investors’ attention to corporate-related information is the main path by which media sentiment interferes with investors’ sentiment. (3) Media sentiment will indirectly affect the excess return and volatility of stocks through investors’ sentiment and their transactions. (4) Media sentiment disagreement has weakened the influence of media sentiment on investors’ attention and sentiment. Investors’ sentiment disagreement has alleviated its impact on the excess returns and volatility of stocks.

In this paper, a time-consistent and arbitrage-free state space for the one-, two-, and three-factor Vasicek models for long-term bonds is constructed. To account for the uncertainty in long-term bond yields, we propose a stochastic time-dependent mean-reversion model. The state-space model allows for the computation of measurement errors from observed yields. Appropriate state and measurement linear equations are derived to allow the use of the Kalman filter for model implementation. Based on weekly South African Government Bonds from February 2010 to February 2021, we give parameter estimates for the one-factor, two-factor, and three-factor models. The results from the study show that the developed model can fit the term structure of very long-term bonds.

A recent study shows that index futures prices are influenced by the index’s liquidity. This research describes the state of the index futures market following the epidemic as seen by index futures mispricing affected by liquidity and volatility during the outbreak.

Based on multiquadric quasi-interpolation, this study presents a meshless numerical method for time fractional Black–Scholes (B–S) model. The method is highly accurate and flexible. The stability and convergence of the proposed scheme are discussed in detail. Numerical results show the accuracy and efficiency of the proposed method.

This paper examines the moderating role of corporate governance mechanisms on the relationship between mandatory international financial reporting standards (IFRS) and foreign investors’ ownership. This study is based on a sample of French firms listed on the SBF 120 stock index with an observation period from 2002 to 2012. The results show that IFRS adoption leads to an increase in foreign equity in the French firms. This is consistent with the idea that international accounting harmonization improves financial statements comparability and transactions transparency which attracts more foreign investors. We also show the role of governance mechanisms as means of IFRS enforcement. In fact, the increase in foreign investors’ ownership depends on effective enforcement. However, for the French case, only BIG four auditors are efficient in enforcing IFRS adoption while independent board members have close relationship with managers which results in a passive attitude. Our findings imply that information quality has an important role in decision making. But they also show that IFRS adoption should be joined by efficient enforcement tools to conduct presumed benefits.

Most experts try their best to enhance investment performance of multifactor strategy by appending new useful factors or by optimizing existing factors. However, few people take the supply chain into consideration during multifactor strategy designing. Indeed, supply and demand relationships in the industry can reflect the running situation of the relevant companies, so the relationships would be efficient in stock choosing. In this work, we propose a midterm strategy of multifactor, named MUSU, by introducing the industrial supply chain into the multifactor model. We verify the model by taking the industry of new Energy Vehicle as Example. The results show that MUSU performs much better than the traditional models, no matter with market value choosing mechanism or not. It reveals that the introduction of supply chain in multifactor model considerably improves multifactor strategy’s profitability and stability.

In this paper, we apply an updated Coppock trading rule and four trading strategies to two energy ETFs, United States Oil (USO) and United States Gasoline Fund (UGA), using weekly data from 2006 to 2022. Our four trading strategies are designed for different levels of risk tolerance. Strategy 1 is for low risk tolerance investors, strategy 2 for medium risk tolerance investors, and strategies 3 and 4 are for high-risk tolerance investors. For each ETF, we compare the performance of buying and holding this ETF (B&H strategy) to the performance of our trading rule for that ETF. We find our trading rules significantly outperforms the B&H strategy. Traders with low, medium, and high-risk tolerance can use our trading rule with combination of four recommended strategies and obtain an improved risk-return tradeoff than the B&H strategy. Further, our results are robust when we apply our trading system to two equal sub-periods for each ETF.

Due to the availability of innovative financial products and services, financial engineering has expanded to include more practices and engineering techniques (FinTech, InsurTech, and other technologies). Financial engineering (FE) has succeeded through the stock market, financial instruments, and service developments. This study combines qualitative and quantitative approaches with bibliometric analysis to address a vacuum in financial literature. Co-citation, co-occurrence, and bibliographic coupling analysis techniques are used to make inferences about the structure of FE research in finance from January 2007 to December 2022. The study used 347 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through “R” to justify study objectives. The relevance of authors, journals, and organizations in a contemporary topic study is recognized using bibliometrics analysis. The study topic analysis revealed that modern portfolio theory acknowledges the importance of FE’s revolutionary products, such as FinTech and InsurTech, as recent advances in risk management techniques. Future research could take a multi-dimensional approach based on the current theme. New research themes emerged from the study, i.e., Financial Engineering and emerging financial markets and risk management, investment and financial crisis, FinTech, and InsurTech innovation, and Option pricing.

Recently, a lot of research was done on how to evolve the artificial intelligence business model framework. However, one issue was still undeveloped, which was immature to understand. This has been vital for budding and progressive managers, policy decision makers, and academics alike, namely, how businesses transform and develop their AI business models framework to accomplish continuous value formation. Businesses, which achieve to make value over prolonged stages of time effectively, figure, acclimate and recommence their business models with AI technology to fuel such value formation. Sketch on verdicts from a research program on uninterruptedly budding businesses is given. This paper categorizes three perilous competences, namely, an alignment towards investigating with and manipulating innovative business openings, a well-adjusted use of properties, as well as attaining lucidity between management, ethos, and member of staff obligations, together they all decisive key maneuvering schedules. We conclude the paper by signifying the consequences for AI business model framework research and experts and by providing a tool for executives which permits them to reproduce and classify perilous problems relevant for shifting and emerging their business model to sustain value creation. In this paper, we try to explore the keys of Business Model Framework, which will revolutionize business market ecosystem.

Taking the Sino–US trade war as the background, we study the co-movement changes on horizontal return and volatility of the daily logarithmic return data of global stock indices from 2016 to January 23, 2020 from static and dynamic perspectives. Through static tools such as correlation coefficient matrices, complex networks, and Granger causality, it is found that the trade war has significantly strengthened the correlation between the Chinese stock market and the global stock market, and highlighted the position of the Hong Kong stock market. On dynamic analysis, the news impact surface shows the asymmetry of the co-movement for positive and negative news. The aDCC-EGARCH model is used to observe the dynamic co-movement changes between the Chinese stock market and the global stock market. It shows that trade conflicts have enhanced the co-movement between the Chinese stock market and the global stock market to varying degrees. At the same time, the Chinese stock market is strategically tilted towards the Asian market. Based on the research results in this paper, countries around the world need to pay more attention to the risk contagion between stock markets after the outbreak of the trade war. China’s inland markets should be alert to risk contagion from the Hong Kong market.

The credit default swap (CDS) market plays an important role for financial institutions. This is not only for their trading activities, but also as it provides a source of information to extract default probabilities to be used for (counterparty) credit risk purposes, as for instance in credit valuation adjustment calculations. Nonetheless, the number of entities for which liquid single-name CDSs are traded is of the order of a few thousands. This requires financial institutions to employ proxy methodologies to estimate the credit risk they face when trading with counterparties for which no (liquid) CDSs are available in the market. In this paper, we propose and compare different approaches to take into account counterparty-specific information in terms of rating, region, sector, etc. at cross-sectional level to strip risk-neutral default probabilities from CDSs. This is achieved by taking into account the intrinsic probabilistic information characterizing each CDS by means of suitably-defined Wasserstein distances and barycenters. The results suggest that default probabilities are likely to be overestimated if the construction of the proxy credit curves overlooks the probability structure underlying the CDS market, potentially resulting in a too conservative counterparty credit risk pricing framework.