Financial Derivatives Research Articles

Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter

ABSTRACTDeveloping and employing practically useful and easy to calibrate models for prediction of exchange rates remains a challenging task, especially for highly volatile emerging market currencies. In this paper, we propose a novel approach for joint prediction of correlated exchange rates for two different currencies with respect to the same base currency. For this purpose, we reformulate a generalized version of a bivariate ARMA model into a state space model and use the Kalman filter for estimation and forecasting of the underlying exchange rates as latent variables. With extensive numerical experiments spanning 18 different exchange rates (across both emerging markets, developing and developed economies), we demonstrate that our approach consistently outperforms univariate ARMA models as well as the random walk model in short term out‐of‐sample prediction for various exchange rate pairs. Our study fills a gap in the empirical finance literature in terms of robust, explainable, accurate, and easy to calibrate models for forecasting correlated exchange rates. The proposed methodology has applications in exchange rate risk management as well as pricing of financial derivatives based on two exchange rates.

Practical assessments on the use of financial derivatives as risk management tools

This study examines the risk management strategies of non-financial corporations in less efficient financial markets, such as Portugal and Spain. These markets have liquidity constraints and limited availability of derivatives to hedge corporate risk exposures. The study analyzes how these companies dealt with these challenges and how their hedging strategies affected their performance. The results show a complex relationship between the use of derivatives, earnings volatility, and the market value of companies. The study contributes to business practice by helping companies assess the effectiveness of their hedging strategies and may also influence regulators to adapt rules for less liquid markets.

Investing: The Concept and Classification of Schemes with Legal Significance

Introduction: the theme of investment and investing invisibly but tangibly accompanies a person in modern life. The desire to increase their funds is becoming an urgent need of the population. It is carried out through doing business as well as increasing the personal income of citizens. Investment opportunities in the economy as a whole, in relevant markets, and in people’s behavior are expanding. There is an interest in training in this field, and in this regard, the importance of knowledge of the theoretical foundations of investment activity at the level of the main groups of investors and the mechanism of investment in various forms of its implementation increases. The purpose of the paper is to study issues related to the concept of “investment,” the relationship between the concepts of “investment” and “investing,” the legal structures of investment, features of legal forms of investment and their differences from other similar forms of capital investment, investment as a transaction and a type of entrepreneurship, as well as investment schemes and their classification. Methods: the paper uses both the general scientific and specific scientific methods of scientific cognition, among which there are: systematic, dialectical, structural and functional, etc. The methods of formal legal, comparative law and legal forecasting allow taking into account the specifics of the legal nature of the concepts under study, comparing homogeneous legal phenomena, and identifying prospects for the development of legal relations in dynamics. Results: the author considers the main economic and legislative concepts related to investment and investment activity. The features of investing in various market assets are identified and analyzed. The approaches to the analysis and management of the investment portfolio of stocks and bonds are being investigated. Two legal forms of investment have been studied: contractual investment and corporate investment. The author explores new and alternative forms of investment, the main economic and financial categories, factors determining the investment climate, ways to form a portfolio of stocks, bonds, and their management, derivative financial instruments, and explores classifications and investment schemes. Conclusions: it is concluded that an investment represents a certain amount of capital, and the process of their implementation is defined as investment activity (investing). It is proved that the legal structure of investment includes a wide range of rights, duties, and responsibilities of business entities, which includes, in addition to the norms of the civil legislation, the norms of various branches of law. It is revealed that the legal status of an investor is intermediate between a “business entity” and an ordinary “individual.” It is argued that in both legal forms of investment (contractual and corporate), the investor transfers the investment to the other party. In the case of a contractual investment, the investor transfers them to another party (the organizer), who uses them for the purpose specified in the contract (intended purpose). As for the corporate form, the investment is carried out, for example, in the authorized capital of a company, and the investor acquires the rights of a participant in this company. If contractual investment is based on the “classical norms” governing civil law contracts, then corporate investment has a number of features related to the regulation of corporate relations. Investment is classified into main groups, as well as long-term real investment, taking into account planning and accounting practices. From a practical standpoint and based on the current legislation, investment schemes are outlined.

DETERMINANTS OF TAX AGGRESSIVENESS WITH CORPORATE GOVERNANCE AS A MODERATION VARIABLE IN PUBLIC COMPANIES IN INDONESIA

This research examines how corporate governance moderates the impact of financial derivatives, executive pay, executive traits, and family ownership on tax aggressiveness in manufacturing firms listed on the Indonesia Stock Exchange between 2021 and 2023. Independent commissioners are used as a proxy for corporate governance. A total of 86 companies were included in the study using purposive sampling. The data analysis method employed was moderated regression analysis (MRA). The findings indicated that tax aggressiveness was impacted by financial derivatives, executive compensation, and executive behaviour but not by family ownership. These findings suggest that the level of family ownership does not dictate the degree of tax avoidance. Corporate governance as a moderating factor can influence executive compensation and executive behaviour but does not influence financial derivatives and family ownership concerning tax aggressiveness.

Short-Term Electricity Futures Investment Strategies for Power Producers Based on Multi-Agent Deep Reinforcement Learning

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability.

Ethereum futures and the efficiency of cryptocurrency spot markets

This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.

Physical information-guided multidirectional gated recurrent unit network fusing attention to solve the Black-Scholes equation

Reasonable option pricing is crucial in the financial derivatives market. Finding analytical solutions for the Black-Scholes (BS) equation, particularly for American options or with fluctuating volatility and interest rates, is challenging. BS equations exhibit strong time-series characteristics, with asset prices typically adhering to geometric Brownian motion. To address the BS equations, we propose a sequence-to-sequence model guided by physical information (PI), called PiMGA. The PiMGA fuses a multidirectional gated recurrent unit (GRU) network with an attention module, where multidirectional GRU enhances the coding performance of the input sequences and the attention module balances the feature weights of the hidden variables. Prior physical knowledge in BS equations is jointly used as a constraint, forming the penalty function for objective optimization. This allows PiMGA to serve as an efficient approximation function in the learning paradigm of physically informed machine learning to solve BS equations. BS equations with various complexities illustrate the accuracy and feasibility of PiMGA for numerical solutions. Furthermore, the out-of-distribution generalization ability of PiMGA is verified by predicting the Nasdaq 100 index.

New control variates for pricing basket options

Abstract Accepted by: Giorgio Consigli Accurate pricing of basket options, which are financial derivatives on multiple underlying assets, is a challenging and practically important task for financial institutions. We propose several new control variates for accurate, fast and efficient pricing of basket options. The first approach to deriving new control variates is the use of Hermite polynomial approximation of appropriate function of the underlying asset prices, which leads to a Black–Scholes-like analytic solution. This approach is new in the option pricing context and opens up new possibilities in derivative pricing. Further control variates are analytically derived using Jensen’s inequality in one case, and distributional properties of multivariate Wiener processes in other cases. All the newly proposed control variates are shown to lead to excellent variance reduction in numerical experiments based on realistic data. The proposed methods are novel, computationally simple and have a strong potential to replace more conventional methods, such as the geometric lower bound in simulation-based pricing of basket options and similar products used in financial risk management.

European vulnerable options pricing under sub-mixed fractional jump-diffusion model with stochastic interest rate

With the development of financial markets, the trading of financial derivatives has become more flexible. To meet the demands of investors, over-the-counter (OTC) trading of options has also become increasingly frequent, raising concerns about accurately pricing vulnerable options. This study investigates the pricing problem of European vulnerable options under the sub-mixed fractional jump-diffusion (SMFJ) model. Firstly, the underlying asset price model and the counterparty company value model are established under the SMFJ model with a sub-mixed fractional Vasicek stochastic interest rate. Then, analytical solutions for the European vulnerable options are obtained using the quasi-martingale measure transformation and the risk-neutral pricing method. Additionally, the accuracy of the pricing formula is verified through the Monte Carlo simulation method. Finally, the rationality and validity of the established model are confirmed by numerical simulations, and the influence of key parameters on the pricing models is analyzed.

Optimal Market Completion through Financial Derivatives with Applications to Volatility Risk

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We first introduce an efficient and accurate simulation-based method applicable to generalized diffusion models to approximate the optimal derivatives-based portfolio strategy. We build upon a double optimization approach, i.e., expected utility maximization and risk exposure minimization, already proposed in the literature, demonstrating that strangle options are the best choices for market completion within equity options. They lead to lower investors’ risk exposure for a wide range of strikes compared to the lesser flexibility of calls, puts, and strangles. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when short-term maturity equity options are not available.

A Robust numerical technique based on the chromatic polynomials for the European options regulated by the time-fractional Black–Scholes equation

AbstractRisk mitigation and control are critical for investors in the finance sector. Purchasing significant instruments that eliminate the risk of price fluctuation helps investors manage these risks. In theory and practice, option pricing is a substantial issue among many financial derivatives. In this scenario, most investors adopt the Black–Scholes model to describe the behavior of the underlying asset in option pricing. The exceptional memory effect prevalent in fractional derivatives makes it easy to understand and explain the approximation of financial options in terms of their inherited characteristics prompted by the given reason. Finding numerical solutions that are both successful and suitably precise is crucial when working with financial fractional differential equations. Hence, this paper proposes an innovative method, designated the Chromatic polynomial collocation method (CPM), for the theoretical study of the Time fractional Black–Scholes equation (TFBSE) that regulates European call options. The newly developed numerical algorithm CPM is on a functional basis of the Chromatic polynomials of Complete graphs (Kn) and operational matrices of the basis polynomials. The CPM transforms the TFBSE into a framework of nonlinear algebraic equations with the help of operational matrices and equispaced collocation points. The fractional orders in the PDE are concerned in the Caputo sense. The CPM findings further corroborate the results of the most recent numerical schemes to show the effectiveness of the suggested numerical algorithm.

Deformation of volatility curves of the Russian stock market on the example of margined options on futures contracts on the RTS index

We reassess the Russian stock market behavior in terms of the relationship between risk parameters and return on assets in connection with global structural changes in 2022. The subject of this study is volatility curves calculated from quotes of exchange options on RTS index futures. The purpose of the study is to analyze the relationship between volatility curves and the distribution of returns in 2015–2022, to analyze changes in the structure of this relationship in 2022, to assess the applicability of the GARCH model and to develop new investment and hedge strategies on the Russian stock market. The issue of interpreting the dynamics of volatility curves is disputable however, it was theoretically developed in the theory of valuation of derivative financial instruments. As a result of the study, unrelated structures of the distribution of returns and volatility curves for the RTS composite index were found throughout the entire period concerned, regardless of the presence of external shocks. It is concluded that in the Russian stock market, investors prefer protective options rather than speculative ones, despite the negative asymmetry of the distribution of RTS index returns. And despite the greater weight of positive returns in their distribution in each period under consideration, due to the technical features of the functioning of return indicators, the negative asymmetry of the return distribution does not necessarily correlate with growing volatility curves.

Coalition of the opting – Part 1: Venturing into new alignment strategies based on financial derivatives theory

This article introduces the ‘coalition of the opting’ as a novel alignment strategy situated between non-alignment and traditional collective security. Drawing on financial derivatives theory, particularly options contracts, the approach offers a flexible alternative to rigid alliance commitments. It provides nations with the ability to secure defense capabilities through time-limited, binding contracts that allow future decisions to be deferred, thus maintaining strategic flexibility. The ‘coalition of the opting’ aims to address the predictability and commitment challenges inherent in existing alignment strategies by creating tailored agreements that ensure partner support when needed, without the full obligations of traditional alliances. While not a substitute for collective defense, this concept seeks to lower the entry threshold for binding commitments, thereby optimizing national defense with partner support. Since the ‘coalition of the opting’ is in its early stages, further theoretical investigation and counterfactual analysis are required to assess its viability.

PENGARUH KONSERVATISME AKUNTANSI, FINANCIAL DERIVATIVE DAN KOMPENSASI RUGI FISKAL TERHADAP TAX AVOIDANCE

This research aims to test and prove empirically the influence of accounting conservatism, financial derivatives and fiscal loss compensation on tax avoidance. This research was conducted on Consumer Non-cyclicals sector companies listed on the Indonesia Stock Exchange (BEI) in 2018-2022. The sample in the study used a purposive sampling method. This type of research uses a quantitative approach with an associative type, and there are 13 companies used as research samples with an observation period of 5 (five) years. The analysis technique uses multiple linear regression with a significance level of 0.05. The results of this research show that (1) accounting conservatism, financial derivatives and fiscal loss compensation have a simultaneous effect on tax avoidance, (2) Accounting conservatism has a positive effect on tax avoidance, (3) Financial derivatives have a positive effect on tax avoidance, (4) Compensation Fiscal losses have no effect on tax avoidance.

Hedging instruments to ensure the economic security of economic entities: Methodological background. Part 1

Subject. This article discusses the use of hedging instruments in the process of minimizing the risks of changes in market conditions and their impact on the economic security of economic entities. Objectives. The article aims to define hedging as a process of minimizing market risks and their distribution among participants in market transactions and describe the impact of hedging on the valuation of financial accounting items. Methods. For the study, we used the provisions of general economic theories of positive and normative economics, theories of accounting and analysis, theories of finance, the concept of sustainable development of the organization and the concept of constructive obligation. Results. The article defines the method of special accounting for hedging instruments, the use of which makes it possible to reliably reflect in the Statement of Financial Position of the organization the impact of the risk of changes in market conditions and their minimization when allocating these risks among the participants of an exchange transaction in the financial market. Conclusions. Derivative financial instruments are subject to financial accounting and reporting, and the special hedge accounting method is a business model for the use of hedging instruments that includes elements of hedging.

Aversion to the use of foreign exchange hedging in state-owned enterprises: Evidence from China

Using a sample of state-owned enterprises (SOEs) listed in China from 2006 to 2017, we examine the impact of state ownership on foreign exchange (forex) hedging. We find that SOEs engage in forex hedging both less frequently and to a lesser extent than non-SOEs. These results are robust to a series of additional tests, such as difference-in-differences analyses, instrumental variable tests, and alternative explanation tests. The effect is particularly pronounced for SOEs with weaker internal controls, fewer professional auditors, and less marketization. We further find that the use of currency derivatives by SOEs hinders the promotion of managers. Furthermore, the prevalence of subjective performance evaluation in SOEs, as opposed to objective performance measures, affects managers' decisions regarding hedging, as evidenced by negative promotion–performance sensitivity when using financial derivatives. Overall, our study sheds light on the aversion of SOEs to forex hedging, providing insight into the conservative decision-making tendencies of SOE managers under political governance.

A generalized approach for pricing american options under a regime-switching model

Abstract Regime-switching models gained their popularity over the past decade because of their distinctive advantage of modelling different financial market statuses in a discrete manner rather than a continuous manner as in stochastic volatility models. When they are used in option pricing, the clear advantage is that they enable a larger parameter space for models to be calibrated for a specific market dynamics and thus allow a better quantitative risk management in terms of utilizing financial derivatives. However, when they are used to price American-style financial derivatives, a large number of economic statuses result in a demand for improved computational efficiency. This paper provides a new algorithm of high computational efficiency supplemented with a theorem that pre-analyzes the associated matrices and their eigenvalues, without concern about the possibility of duplicated roots being mistakenly identified as simple roots due to rounding errors or the presence of two extremely closely positioned simple roots within the original system.

Series form solutions of time–space fractional Black–Scholes model via extended He-Aboodh algorithm

The objective of the current study is analyze linear and nonlinear time–space fractional Black–Scholes models via modified homotopy perturbation method (m-HPM). In current investigation, memory effects in financial markets are explored through fractional derivative in Caputo sense. The effectiveness of proposed methodology is checked numerically by finding residual errors and presented in tables. These tables also provide a benchmark for the comparison with already existing results in literature. Furthermore, solutions are graphically analyzed via 3D and contour plots across a range of parameters under varying market conditions. Analysis confirms the efficiency of m-HPM for predicting solutions of time–space fractional Black–Scholes models. The current study can contribute in understanding the applications of fractional calculus in finance, and can be a valuable computational tool for pricing financial derivatives in fractional environments.

Antithetic multilevel Monte Carlo method for approximations of SDEs with non-globally Lipschitz continuous coefficients

In the field of computational finance, one is commonly interested in the expected value of a financial derivative whose payoff depends on the solution of stochastic differential equations (SDEs). For multi-dimensional SDEs with non-commutative diffusion coefficients in the globally Lipschitz setting, a kind of one-half order truncated Milstein-type scheme without Lévy areas was recently introduced by Giles and Szpruch (2014), which combined with the antithetic multilevel Monte Carlo (MLMC) gives the optimal overall computational cost O(ϵ−2) for the required target accuracy ϵ. Nevertheless, many nonlinear SDEs in applications have non-globally Lipschitz continuous coefficients and the corresponding theoretical guarantees for antithetic MLMC are absent in the literature. In the present work, we aim to fill the gap and analyze antithetic MLMC in a non-globally Lipschitz setting. First, we propose a family of modified Milstein-type schemes without Lévy areas to approximate SDEs with non-globally Lipschitz continuous coefficients. The expected one-half order of strong convergence is recovered in a non-globally Lipschitz setting, where even the diffusion coefficients are allowed to grow superlinearly. This then helps us to analyze the relevant variance of the multilevel estimator and the optimal computational cost is finally achieved for the antithetic MLMC. Since getting rid of the Lévy areas destroys the martingale properties of the scheme, the analysis of both the convergence rate and the desired variance becomes highly non-trivial in the non-globally Lipschitz setting. By introducing an auxiliary approximation process, we develop non-standard arguments to overcome the essential difficulties. Numerical experiments are provided to confirm the theoretical findings.

A Binomial Tree-Based Empirical Study on the Biases of American Call Option

The application of financial derivatives, particularly options, has become pervasive in modern finance, offering effective tools for risk management. Among these, American options, allowing flexibility in exercising rights prior to expiration, dominate the derivatives market. Accurate pricing of American options is crucial for informed investment decisions and risk assessments. While various pricing models exist, the binomial tree model is welcomed for its simplicity and accessibility. However, inherent biases in pricing models raise questions about their efficacy across different sectors of the market. Based on the binomial tree model, this study empirically examines the pricing accuracy of American call options in both the technology sector and the industrial sector, by collecting data from top five largest companies in each sector. For the technology sector, samples are mainly from semiconductor manufacturers, software companies, etc., which are all situated in thriving fields; while those five companies from the industrial sector mainly belong to traditional industries, such as energy and engine suppliers. By analyzing prediction biases between two sectors, this research tries to give out reasons behind it and grasps a deeper understanding of option pricing nuances between sectors, and also the limitations of the binomial tree model.