Search Results (1,206)

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 ([email protected]) 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 [email protected] of a portfolio is typically non-subadditive for non-elliptically distributed risk vectors. In particular, we now pin down when the [email protected] 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

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes. Full article

Earnings manipulation is often associated with deceiving public information that is displayed in sustainability reports. Therefore, the current study aims to explore the nexus between earnings management and sustainability reporting practices in the context of Indonesia. This study employs 408 firm-year observations from listed companies in Indonesia during the 2010–2021 period to test the hypothesis using fixed effect regression analyses with standard error estimates. By examining their sustainability reports and financial statements over a specific period, the authors assess the extent to which earnings management influences sustainability reporting practices. This implies that companies engaging in earnings management practices are more likely to exhibit higher-quality sustainability reporting practices. The results contribute valuable and significant empirical insights into the interplay between earnings management and sustainability reporting specifically within the Indonesian context. Furthermore, this study goes beyond examining the relationship itself and delves into potential factors that may influence this relationship. Full article

Descriptive statistics that are easy to generate and interpret are central to policy decision making. The GINI coefficient and the coefficient of variation are used widely when assessing inequality. In many areas of inequality, such as wealth and income holdings, the distribution is skewed. Here, simple power laws could provide useful ‘descriptive’ exponents. The Zipf-Pareto power law and Lavalette’s law are used to reveal a steepening in the distribution of district house prices in Britain that began before the financial crash of 2008. The time profiles indicate the exponents closely mirror those of the GINI coefficient and the coefficient of variation. As such, they are useful tools in the quantification of inequalities. Full article

In the context of pension plans, the employer and the worker have distinct interests and face different risks. The worker seeks higher retirement benefits, while the employer aims to minimize the cost of fulfilling his obligations. To address these diverse needs, the defined contribution plan managed with participating life insurance (DC-PL) and the cash balance plan managed with unit-linked insurance (CB-UL) serve as suitable choices. The multi-criteria analysis is conducted using the cumulative prospect theory model to measure the utility of the parties involved toward a mixed product combining these two pension plans. By assigning weights to risk measures and maximizing utilities, the paper employs both additive utility and Nash equilibrium approaches. The results reveal that the CB-UL plan aligns with employers’ interests, offering potential financial gains, while the DC-PL plan attracts workers due to its profit-sharing aspect. Significantly, when equal importance is given to both parties, the CB-UL plan emerges as the prevailing choice. This study contributes to the understanding of pension plan design and decision-making dynamics between employers and workers, providing valuable insights for achieving a balance between retirement benefits and cost management. Full article

This study aims to understand the impact of the additional remuneration of the Chief Executive Officer (CEO) over the mean remuneration of the board of directors on firms’ financial performance. The objective is to understand if the highest compensation of the CEO is a firm performance driver. In addition to the impact of total remuneration, the different remuneration components were split and analyzed. An unbalanced panel data of listed companies in DAX–Germany over the period from 2006 until 2019 is analyzed. Using dynamic methodology to estimate the models, the results show that higher additional remuneration positively explains higher firm performance measured using both accounting and market measures. The impact is also evident when additional remuneration components are analyzed. These results support the tournament theory, since when CEOs feel rewarded, they are more efficient in increasing the firm’s performance. Moreover, the firms’ financial characteristics, as well as macroeconomic factors, are also relevant to explaining its performance. Full article

This paper investigates the impact of financial inclusion on sustainable firm growth in Northern African countries (Egypt, Morocco, and Tunisia) during the period of 2007–2020. To this end, this study employs a dynamic panel threshold regression (DPTR) model. This model is a panel-data model that can capture different behaviors of data, depending on a threshold variable. The main results showed the existence of a threshold effect. This means that when financial inclusion is low (high), sustainable firm growth is limited (significant) due to the absence (presence) of appropriate financing, information, and financial tools. However, the levels of financial inclusion in North African countries are insufficient and require improvement. Therefore, it is essential for policymakers and managers to continue to promote the quality of financial inclusion by improving access to financial services and the regulatory environment to facilitate firms’ access to financing and support their sustainability. Full article

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing risks and determining insurance premiums for personal vehicles. However, the app is not limited exclusively to actuaries. Other individuals or entities, such as insurance companies, researchers, or analysts, who have access to the necessary data and require insights or analysis related to personal auto insurance, can also benefit from using the app. It is the first web-based application of its kind that is free to use and deployable from the personal computer or mobile device. AutoReserve is a software solution that caters to the needs of insurance professionals where only a few existing web-based applications are available. The application is divided into three parts: a summary of the loss data, a classical loss reserving tool, and a machine learning loss reserving tool. Each component of the application functions differently and allows for inputs from the user to analyze the provided loss data. The user, in other words, individuals or entities who utilize the Auto Reserve application, can then use the outputs for these three sections to improve his or her risk management or loss reserving process. AutoReserve is unique compared to other loss reserving tools because of its ability to employ both traditional, spreadsheet-based and modern, machine-learning-based loss reserving tools. AutoReserve is accessible on the web. The app is currently usable and is still undergoing frequent updates with new features and bug fixes. Full article