Financial Derivatives Research Articles

Research on Enterprise Using Futures to Hedge Operational Risks

Enterprises are faced with various business risks, such as market risk, credit risk, and raw material price fluctuation risk, and futures, as a kind of financial derivative with delayed settlement, can be used to hedge the risks faced by enterprises. This study aims to explore the methods and strategies for enterprises to use futures to hedge business risks, as well as the effect analysis, so as to provide references and suggestions for enterprises to carry out risk management. This study adopts the methods of literature research and case analysis. Firstly, the basic concepts and operation methods of the futures market are introduced, as are the basic principles and methods of enterprise risk management are introduced. Secondly, it discusses the methods and strategies for enterprises to use futures to hedge risks, and makes an empirical analysis of their effects. It is found that the methods and strategies of hedging risks by using futures can effectively reduce the risks faced by enterprises, especially the risk of raw material price fluctuations. However, there are certain costs and technical thresholds for hedging risks by using futures, so enterprises should use them carefully according to their own conditions.

Subprime Credit Crisis: Cause, Consequence & Response

The 2008 financial crisis had significant impacts on the global economy, leading to the collapse of several large financial institutions, massive government bailouts, and a global recession that lasted for several years. This paper discusses the causes and consequences of the crisis, highlighting the importance of understanding the mechanisms that underlie financial crises and the role that banks and financial institutions play in them. The recent Nobel Prize in Economics, given to the research of systematic risk and the role of banks in financial crises, is also covered in this essay. The crisis was brought on by a number of causes, including loose monetary policy, a housing bubble, and the growing use of sophisticated financial derivatives. The collapse of the subprime mortgage market, the high level of unemployment, and the collapse of many large companies and banks were among the consequences of the crisis. The paper underscores the importance of continued research into the causes and mechanisms of financial crises.

Research on the Pricing Model of DerivativesBased on the Black-Scholes Model and Binomial Model

The pricing of financial derivatives plays a significant role in risk management and financial transactions. The models of pricing are mainly divided into Black-Scholes Model and Binomial Model. The two models have different characteristics and distinguishing the models is helpful for price makers. The paper aims to analyse the distinction between the two models on the Huaxia Stock Exchange 50ETF by using Python. This paper selected the stock price, the volatility and the option price from the Eastmoney database. First, edit the formulas of two models and then introduce the parameters into the formula by Python and get the model calculated price. Comparison between the calculated prices helps to compare the two pricing models. The result is that Black-Scholes Model is more accurate and Binomial Model is more simplified which is suitable for new officers.

Auditing the Derivative Usage of Bank-Holding Companies

SYNOPSIS Auditing financial derivatives is complex and contentious. Our study examines the impact of derivative use on the risk premium charged by auditors of bank-holding companies (BHCs). We find that audit fees are higher regardless of whether derivative instruments are used for hedging and trading. This implies that auditors charge BHCs that use derivatives a risk premium to compensate for additional risk related to these instruments. We also find that trading derivatives tend to have a higher risk premium than hedging derivatives. Our results suggest that auditors price risks related to derivatives, and trading derivatives are perceived to be higher risk than hedging derivatives. Data Availability: All data are available from public databases identified in the paper. JEL Classifications: G21; M40; M41.

The Impact of the Federal Reserve's Interest Rate Hike on Gold Futures Prices: The Dynamic Trend

This article aims to study how the increase in the Federal Reserve’s Interest Rate is going to impact the gold futures prices under a global economic recession period. Through collecting historical gold prices in chronological order from 2010, the data are then adapted into ARIMA model. It is to investigate the predicted correlation between the macro indicators and performance of the financial derivatives. The result includes that the policy change will have more dominant effects in the long run than short run. The real-world analysis should also incorporate multiple factors that range from qualitative to quantitative to facilitate a more precise prediction of future derivative’s price. The significance of the study is to suggest the time value of policy. Investors who hope to yield long-term return, should pick historical data, and learn the resultant change that happen only a few months after the policy announcement. The authorities should also take note of the intensity of the policy change, trying their best to achieve its economic goal at once, which would avoid rippling vicious effect and panic among the public.

Options in Derivative Market: A Study on Working and Trends in Option Contracts

Indian derivative market is very vast market. Many assets,securities, products and services are offered in derivative market to the various traders and investors. In India derivative products are offered by both BSE (Bombay stock exchange) and NSE (national stock exchange). Derivatives are classified into two parts Commodity derivatives and financial derivatives. Commodity derivatives are dealt in commodities like soybean, turmeric, oats corns. Financial derivatives include securities, bonds, treasure, foreign exchange, stock index etc. All financial derivatives are performed with the help of futures; options .this paper manly concentrates option contracts. NSE and BSE are also dealt in futures and options. This paper only considers the trends and working of option contracts in BSE. Option contracts are a right to buy or sell security on or before expiry of period at a predetermined price which is called strike price. Options are not an obligation. Call option and put option are two basic positions which are used by trader to manage the risk of buying and selling of underlying asset or security. This paper shows the working and trends of options as indexed options and equity options in BSE.

Risk Management Case Analysis of ICBC's "Crude Oil Treasure" Incident

Driven by the marketization of interest rates and technological innovation, commercial banks in China continue to innovate various financial services businesses and devote more attention to attracting customers to purchase various investment and wealth management products in order to expand sources of bank performance and gain more benefits. In 2018, paper crude oil-linked wealth management products emerged. Paper crude oil products are linked to the prices of international crude oil futures contracts. The products feature controllable risks and relatively high returns, which are very popular among large numbers of customers. However, despite the continuous expansion of China's financial derivatives trading volume, the categories of derivative products and regulation still have many limitations. The development of innovative paper crude oil-linked products is not perfect and has many potential risks, which are highly susceptible to market fluctuations. Taking China Bank's "Crude Oil Treasure" product as the research focus, this paper analyzes the massive loss incident that occurred in late April 2020 through case study analysis and qualitative analysis methods. It is divided into four parts. The introduction part explains the research background, significance, content, methods and innovations and inadequacies of this paper. The first part sorts out the background and process of the "Crude Oil Treasure" case at China Bank and elaborates on the operating principles of this product. The second part analyzes the "Crude Oil Treasure" case in detail. It first focuses on the problems in the product's trading liquidity, domestic and international time differences and product promotion. Then it focuses on the internal and external causes of the case, discussing external factors such as international crude oil market turmoil and futures trading characteristics, and internal factors such as product design, risk control and lack of experience. It also evaluates the hedging strategy, contract design and compliance of the "Crude Oil Treasure" product. The third part mainly analyzes the impacts of the "Crude Oil Treasure" rollover incident and China Bank's subsequent response measures. The fourth part puts forward certain countermeasures and suggestions from the perspectives of bank management, investors and regulatory agencies to prevent such incidents from recurring based on the case analysis.

The Financial Derivatives Market and the Pandemic: BioNTech and Moderna Volatility

Global society’s comfort and well-established certainties have been unpredictably and foundationally undermined by the emergence of the COVID-19 virus. The announcement of the pandemic by the WHO has halted global economic activities, and the financial markets have recorded drastic losses. In this context of uncertainty and economic downturn, many traditional companies have been negatively impacted, but the biotechnology sector, which has already been growing for some years, registered high growth rates and earnings. In particular, this study focused on the two most significant biotech companies, BioNTech and Moderna, the two start-ups that first commercialized COVID-19 vaccines. The GARCH (1,1) model examines the relation of two stock prices and the volatility of derivatives markets before and after the outbreak of the pandemic. The variables used in the analysis are the U.S. technologic market index, the market volatility, and Brent future prices. The results suggest a different reaction of market volatility and Brent future prices on the return of both companies. Additionally, during the COVID-19 period, a contagion effect between both companies and the technological market was observed.

Attention-based CNN–LSTM for high-frequency multiple cryptocurrency trend prediction

With the price of Bitcoin, Ethereum, and many other cryptocurrencies climbing, the cryptocurrency market has become the most popular investment area in recent years. Unlike other relatively more stable financial derivatives, the cryptocurrency market has high volatility which requires a high-frequency prediction model for quantitative trading. However, the excessive number of trading becomes a critical issue due to the instability of the prediction results and high error rate. To relieve such a problem, based on the observation of high-frequency data, we use local minimum series to replace the original series and propose a more stable triple trend labeling method that reduces the number of trades by potentially influencing the training of the model. In addition, a new attention-based CNN–LSTM model for multiple cryptocurrencies (ACLMC) is proposed to optimize model effects by exploiting correlations across frequencies and currencies, and to smooth out the investment risk associated with prediction errors by supporting simultaneous multi-currency predictions. Experiments show that our labeling method with ACLMC can achieve much better financial metrics and fewer number of transactions than traditional baselines.

Positive Affect of Financial Derivatives onThe Stock Market

The derivatives market is widely recognized in financial markets, and it has developed rapidly, but there is little evidence that it contributed to the development of financial and economic markets. This article investigates the dynamic relationship between the Indian derivatives market and the stock market to determine how it affects market pricing. This paper use the DCC-garCH model to examine the dynamics of India from the third quarter of 2018 to 2022, I find that the financial derivatives market is more financially contagious than the equity market. Therefore, I can analyse that the development of derivatives has a positive impact on economic growth.

Review on Three New Value at Risk (VaR) Models

The emergence of financial derivatives complicates traditional financial products and increases financial market volatility. Individuals and financial institutions are both exposed to more complex and uncontrollable risks in this environment. Because of the risk's uncertainty, we must use reasonable methods to predict and estimate it in order to achieve the goal of risk control. This paper discusses three new VaR (Value at Risk) models that have emerged in recent years based on the ARCH family model using a method of literature review. The ARMA-EGARCH model, for example, combines the ARMA model to describe constant variance time series and the EGARCH model to describe heteroscedasticity phenomena, and theoretically can better describe the fluctuations of financial time series and obtain an independent time series with the same distribution. The sequence is processed using extreme value theory, which is the ARMA-EGARCH-GPPD model, in conjunction with the GPD model. We used the ARMA-EGARCH-semi-parametric method in conjunction with the historical simulation method and the parameter method to avoid cumbersome quantile calculation because the model algorithm is more complex. The generalized EWMA risk value prediction model has more advantages for financial data with large peaks.

Research on the Development of Implied Volatility in Option Pricing

Option, as an important financial derivative, has received more and more attention from investors in recent years. This paper includes three parts, the first part introduces the concept of options, including the birth and history of options, the trading of options, and different types of options. Then it discusses the history of option pricing and two typical and classic option pricing models and then introduces implied volatility. The second part of this paper introduces the specific development of the option pricing models, the correction process of option pricing models, and volatility models. Different types of volatility, and their comparisons are included. And this paper also discusses the two functions of implied volatility, one is predicting the future and the other is for risk management and portfolio management. The third part concludes the first two-part and discusses the recent progress of option pricing and its volatility.

OptionNet: A multiscale residual deep learning model with confidence interval to predict option price

Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their performance. To solve this problem, we propose a novel deep learning model named as MRC-LSTM-CI. It contains three modules, including Multi-scale Residual CNN module (MRC), Long Short-Term Memory neural network module (LSTM) and confidence interval output module (CI). The proposed model can effectively extract multi-scale features from real market option data, and make interval prediction to provide more information to the decision maker. In addition, the proposed model is further improved using the residual prediction strategy, where the output value is chosen as the residual value between BS theory price and actual market price. Experimental results show that our model has better prediction accuracy than other deep learning models and achieves the state-of-the-art performance.

Derivatives in Banking: A Platform for Teaching

Risk management is the core business of a bank. The transformation of a riskless (to the customer) deposit account into a risky (to the bank) asset is how a bank adds value and earns a profit. Successfully managing this is risk is therefore vital to the success of a bank. Banks are subject to many forms of risk, including interest rate risk, credit risk, liquidity risk, funding risk, and others. While banks cannot eliminate risk, they can control certain areas of their exposure. This project describes an exercise that demonstrates a method of managing interest rate risk in an uncertain environment and the risk-reward tradeoff of using financial derivatives to reduce risk. The exercise is beneficial to students in the banking field as well as those interested in investments as it provides a method to actually see the advantages and disadvantages of the hedging process. This guided experiential learning process should better prepare both groups of students as they begin their chosen careers.

Pricing inconsistency between the futures and Financial Transmission Right markets in North America

An empirical comparison of prices for two categories of financial derivatives of the NYISO, PJM and ISO-NE power markets, namely Financial Transmission Right (FTR) and futures contracts, is performed. The objective is to assess whether these two categories of derivatives are priced consistently, as their payoffs partially overlap. Statistical metrics reveal that implied prices for the loss component of the power price, which are obtained by combining information provided by both FTR and futures prices, are more volatile than corresponding realized loss values. This contradicts the idea of a price being a probability-weighted average of possible realized values, and therefore indicates the presence of the pricing misalignment between the FTR and futures markets of the three power markets during the period covered by the data sample.

Analytical solution of time fractional Black-Scholes equation with two assets through new Sumudu Transform iterative method

There is a scopious rise in the study of financial derivatives over the past two or three decades. Mathematical model proposed by Black and Scholes expounds financial derivatives in a more momentous way. The Black-Scholes model on a single asset is a partial differential equation characterizing the behavior of European options. In this article, we introduce the new Sumudu transform iterative method (NSTIM) as a new technique to obtain the analytical solution of time fractional Black-Scholes model involving European options with two assets. The proposed model is the advanced version of the regular Black-Scholes model. Explicit solution of the problem has been obtained with the help of generalized Mittag-Leffer function. The numerical analysis prove that this method is efficacious in solving various problems of financial theory.

Theoretical and practical research on mathematical modeling of economy and finance based on artificial intelligence

Abstract In the context of the rapid development of big data technology, developing an artificial intelligence economy is beneficial to China’s economic transformation, industrial upgrading, and enhancing international competitiveness. This paper first constructs a financial mathematical model of the AI economy based on the theoretical foundation of financial mathematics. The structure of the financial mathematical model mainly consists of three parts: portfolio model, capital asset pricing model, and financial derivatives pricing model. To address the problem of the unstable development of China’s AI economy, the theoretical framework of investment risk management is constructed, which can help investors intelligently implement investment risk management plans and implement investment risk management countermeasures and measures. Finally, to verify the performance of the risk investment optimization model and intelligent algorithm proposed in this chapter, three investment scales will verify the investment risk management capability based on the portfolio model. The results show that the convergence performance of the portfolio model is 0.2025, 0.4021, and 0.4391 for three investment sizes of 2, 4, and 9, and the penalty coefficients are 1.5, 28, and 0.2, respectively. At the beginning of the iteration, the investment risk is high, and the convergence speed of the portfolio model is higher than the capital asset pricing model and the financial derivatives pricing model. This study applies it to solve financial optimization problems, including portfolio, stock forecasting, risk management, and many other fields, and provides a better solution to the optimization decision problem of the portfolio.

An Empirical Study on the Logarithmic Return of Securities

Objective: Previous studies have shown that the distribution of security returns has the characteristics of peak and fat tail, and it is not realistic to use logarithmic normal distribution to describe security returns. This paper mainly studies the specific form of security returns distribution and conducts empirical research. Methods: We first assume that the security returns follow a logarithmic t-distribution. Then, the maximum likelihood estimation and moment estimation are used to explore the degree of freedom of the distribution. Finally, the goodness of fit test is used to conduct an empirical analysis about Shanghai Securities Composite Index. Results: The specific expression formulas for the estimator of the degree of freedom are obtained. Empirical testing shows that the security returns follow a logarithmic t-distribution. Conclusion: The probability distribution of security returns is unrealistic to assume that the security returns follow the logarithmic normal distribution. It is more practical to use the logarithmic t-distribution to characterize the distribution of security returns. Our results provide theoretical assistance for exploring the pricing of financial derivatives.

Distributed Least-Squares Monte Carlo for American Option Pricing

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.

Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures

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.