Risks, Volume 11, Issue 8 (August 2023) – 13 articles

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an extreme dependence structure. Due to the bond’s low cashflow contingencies and the CAT bond’s high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce the moral hazard and increase the bond’s attractiveness with a lower trigger likelihood, displaying the determinants of the wiped-off coupon and principal by both the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulting from the potential catastrophic disaster might be associated with heavy-tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by a POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct an empirical analysis of pricing a three-event rainstorm CAT bond based on the resulting losses due to rainstorms in China during 2006–2020. Monte Carlo simulations are carried out to analyze the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators. Full article

This paper overviews our recent results of energy market modeling, including The option pricing formula for a mean-reversion asset, variance and volatility swaps on energy markets, applications of weather derivatives on energy markets, pricing crude oil options using the Lévy processes, energy contracts modeling with delayed and jumped volatilities, applications of mean-reverting processes on Alberta energy markets, and alternatives to the Black-76 model for options valuation of futures contracts. We will also consider the clean renewable energy prospective in Canada, and, in particular, in Alberta and Calgary. Full article

Traditionally, finance has paid attention to the risk-return trade-off. Recently, given the incorporation of the 2030 Agenda and climate change, a third pillar has been incorporated into the investment decision: sustainability. Socially responsible investment is an instrument that can incorporate all three pillars. This paper aims to assess sustainability by Spanish investors using a choice experiment by applying the Bayesian approach with Markov chain Monte Carlo sampling and obtain the willingness to pay (invest) for each attribute. The results show that profitability remains the most important factor, although risk is at the same level as sustainability. Full article

In this paper, we investigate the behavioral and statistical characteristics of cash flows for stable value funds provided by numerous U.S. employee benefit plans. We analyze participant-initiated aggregated cash flow data, representing approximately 80% of the market for large employer plans with stand-alone stable value wraps within a 401(k) offering. By leveraging this unique dataset and contextualizing the 401(k) ecosystem, we examine numerous behavioral lapse hypotheses. Our findings highlight key behavioral lapse hypotheses for modeling lapses and generating risk scenarios. We demonstrate that cash flows exhibit medium- to long-term non-monotonic trends. Factors within the plan sponsor’s ecosystem, such as employment growth, default 401(k) plan options, and the introduction of new investment options, significantly impact participant cash flow behavior indirectly. Moreover, we find that flight-to-safety behavior plays a dominant role during global market crises. Although the risk of mass lapses due to reputational issues is observed, their probability of occurrence is low. Other behavioral hypotheses discussed in the literature, such as the moneyness hypothesis, are found to be less prevalent in this context. Full article

Modelling the energy price in the Australian National Electricity Market (NEM) requires features that are not well reflected in existing models. We present a semi-structural, multi-regional model wherein bidding is not required to be cost-based, renewable fuels and storage technology are structurally integrated, and network constraints are often binding in optimal dispatch. Available fuel capacity then does not necessarily sum to registered bid capacity, as-bid fuel costs do not dependably follow input fuel prices, and cross-regional interconnectedness requires modelling trade. Furthermore, modelling the NEM spot price path must admit price negativity and price spikes. Extending previous work in the literature, the present paper proposes a hyperbolic bid stack approach to price modelling under these conditions. Full article

This study conducts a comparative analysis of the performance of Islamic and conventional indices in both developed and developing countries and territories, considering the pre- and post-COVID-19 pandemic periods. The research employs performance index tools and time–frequency wavelet-based analysis to assess how the COVID-19 pandemic affected the performance, volatility, and co-movement of Islamic and conventional stock indices. The findings reveal that Islamic stock indices are more resilient and tend to outperform conventional stocks during crisis periods in both developed and developing countries and territories, and this trend holds true in the long and short term across most countries. The analysis of wavelet coherence indicates a strong co-movement and coherence between Islamic and conventional indices. Furthermore, the study reveals that in developing countries and territories, the co-movement is characterized by weak coherence and high volatility compared to developed countries and territories. The study highlights the significance of Islamic indices as safe havens for investors during times of crisis, suggesting that including Islamic equities in investment portfolios can potentially yield higher returns compared to conventional indices. This research holds practical value for individual traders involved in the online trading of global stock indices, aiding them in constructing and designing internationally diversified portfolios. Unlike previous studies that focused on specific countries and territories and indices, this study offers a comprehensive examination of the behavior of Islamic and conventional indices across major global markets during both crisis and noncrisis periods. The results contribute significantly to the existing literature and offer valuable insights for investors. Full article

Option pricing is an important research field in financial markets, and the American option is a common financial derivative. Fast and accurate pricing solutions are critical to the stability and development of the market. Computational techniques, especially the least squares Monte Carlo (LSMC) method, have been broadly used in optimizing the pricing algorithm. This paper discusses the application of distributed computing technology to enhance the LSMC in American option pricing. Although parallel computing has been used to improve the LSMC method, this paper is the first to explore distributed computing technology for LSMC enhancement. Compared with parallel computing, distributed computing has several advantages, including reducing the computational complexity by the “divide and conquer” method, avoiding the complicated matrix transformation, and improving data privacy as well as security. Moreover, LSMC is suitable for distributed computing because the price paths can be simulated and regressed separately. This research aims to show how distributed computing, particularly the divide and conquer approach implemented by Apache Spark, can be used to improve the efficiency and accuracy of LSMC in American option pricing. This paper provides an innovative solution to the financial market and could contribute to the advancement of American option pricing research. Full article

One perspective suggests that firms heavily involved in innovation may face increased risks. It is essential to know the suitable proxies in measuring innovation related to risk taking. Many studies use research-and-development intensity (RDI) and research-and-development spending (RDS) as proxies for innovation related to risk taking. However, little evidence shows that positive association with risk taking. This study addresses this gap by using RDI and RDS as metrics for measuring innovation and assessing innovation-related risks. This study incorporated performance as a potential factor affecting the interaction between these variables. It is essential to consider the risks associated with innovation and allocate the RDI and RDS effectively to maximize revenue. We used a dataset of 3955 firm-year observations obtained from 548 listed firms in the Indonesian stock exchange for 2012–2021. We found that RDI and RDS positively affect risk taking. The test results show that the interaction between innovation and firm performance negatively affects risk taking. Thus, firm performance may mitigate the risks associated with innovation. Therefore, firms must balance their innovation projects with improved performance to minimize risks and achieve long-term success. Full article

In this article, we investigate the validity of diversification effect under extreme-value copulas, when the marginal risks of the portfolio are identically distributed, which can be any one having a finite endpoint or belonging to one of the three maximum domains of attraction. We show that Value-at-Risk (V@R) under extreme-value copulas is asymptotically subadditive for marginal risks with finite mean, while it is asymptotically superadditive for risks with infinite mean. Our major findings enrich and supplement the context of the second fundamental theorem of quantitative risk management in existing literature, which states that V@R of a portfolio is typically non-subadditive for non-elliptically distributed risk vectors. In particular, we now pin down when the V@R is super or subadditive depending on the heaviness of the marginal tail risk. According to our results, one can take advantages from the diversification effect for marginal risks with finite mean. This justifies the standard formula for calculating the capital requirement under Solvency II in which imperfect correlations are used for various risk exposures. Full article

This article aims to contribute to the academic knowledge in the field of scientific production regarding decision support tools for investments in the capital market, specifically focusing on fundamental analysis, technical analysis, and Ichimoku dynamics. Bibliometric analysis, following the three main laws (Bradford’s Law, Lotka’s Law, and Zipf’s Law), was employed to evaluate scientific production, identify publication patterns, and uncover gaps and collaboration networks over the last thirty years. To achieve these objectives, 1710 relevant academic publications on the topic were analyzed and retrieved from the Web of Science (WOS) database, pertaining to the last 30 years, between 1990 and 22 May 2023. The significance of this article lies in the contributions of the findings, which advance scientific knowledge by identifying gaps in the knowledge and research, particularly in the limited literature on Ichimoku; our review reveals a growing trend of research in this area. Another notable conclusion is the emergence of new research topics and areas of interest, as well as the identification of collaboration networks among authors, institutions, and countries. Moreover, the article provides valuable insights for financial professionals and investors who are interested in applying these methodologies as methods for price forecasting. The highlighted results support investment decision making, trading strategies, and portfolio management. Full article

The main problem in pricing variance, volatility, and correlation swaps is how to determine the evolution of the stochastic processes for the underlying assets and their volatilities. Thus, sometimes it is simpler to consider pricing of swaps by so-called pseudo-statistics, namely, the pseudo-variance, -covariance, -volatility, and -correlation. The main motivation of this paper is to consider the pricing of swaps based on pseudo-statistics, instead of stochastic models, and to compare this approach with the most popular stochastic volatility model in the Cox–Ingresoll–Ross (CIR) model. Within this paper, we will demonstrate how to value different types of swaps (variance, volatility, covariance, and correlation swaps) using pseudo-statistics (pseudo-variance, pseudo-volatility, pseudo-correlation, and pseudo-covariance). As a result, we will arrive at a method for pricing swaps that does not rely on any stochastic models for a stochastic stock price or stochastic volatility, and instead relies on data/statistics. A data/statistics-based approach to swap pricing is very different from stochastic volatility models such as the Cox–Ingresoll–Ross (CIR) model, which, in comparison, follows a stochastic differential equation. Although there are many other stochastic models that provide an approach to calculating the price of swaps, we will use the CIR model for comparison within this paper, due to the popularity of the CIR model. Therefore, in this paper, we will compare the CIR model approach to pricing swaps to the pseudo-statistic approach to pricing swaps, in order to compare a stochastic model to the data/statistics-based approach to swap pricing that is developed within this paper. Full article

The objective is to study the use of non-translation invariant risk measures within the equal risk pricing (ERP) methodology for the valuation of financial derivatives. The ability to move beyond the class of convex risk measures considered in several prior studies provides more flexibility within the pricing scheme. In particular, suitable choices for the risk measure embedded in the ERP framework, such as the semi-mean-square-error (SMSE), are shown herein to alleviate the price inflation phenomenon observed under the tail value at risk-based ERP as documented in previous work. The numerical implementation of non-translation invariant ERP is performed through deep reinforcement learning, where a slight modification is applied to the conventional deep hedging training algorithm so as to enable obtaining a price through a single training run for the two neural networks associated with the respective long and short hedging strategies. The accuracy of the neural network training procedure is shown in simulation experiments not to be materially impacted by such modification of the training algorithm. Full article

In recent years, Bitcoin and other cryptocurrencies like Ethereum and Dogecoin have emerged as important asset classes in general, and diversification and hedging instruments in particular. The recent COVID-19 pandemic has provided the chance to examine and assess cryptocurrencies’ behavior during extremely stressful times. The methodology of this study is based on an estimate using the ARDL model from 22 January 2020 to 12 March 2021, allowing us to analyze the long-term and short-term relationship between cryptocurrencies and COVID-19. Our results demonstrate that there is cointegration between the chosen cryptocurrencies in the market and COVID-19. The results indicate that Bitcoin, ETH, and DOGE prices were affected by COVID-19, which means that the pandemic seriously affected the three cryptocurrency prices. Full article