Valuation of interest rate ceiling and floor based on the uncertain fractional differential equation in Caputo sense

Jin, Ting; Li, Bo; Xia, Hongxuan; Xue, Chenxi

doi:10.3233/jifs-201930

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…As a result, crude oil, corn, and gold time series are nonlinear and nonstationary [5]. erefore, determining the drivers of these commodities are demanding task [6]. Much research has been conducted into the analysis of commodity prices, and the usual methods used can be classified into two groups: data-driven approaches and structure-modeling techniques.…”

Section: Related Studiesmentioning

confidence: 99%

Determinants of Commodity Futures Prices: Decomposition Approach

Antwi

Gyamfi

Kyei

et al. 2021

Mathematical Problems in Engineering

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Developing models to analyze time series is a very sophisticated, time-consuming, but interesting experience for researchers. Commodity price component determination is challenging due to remarkable price volatility, uncertainty, and complexity in the futures market. This study aims to determine the components that drive the market price of commodity futures. This study utilized the decomposition methods, empirical mode decomposition (EMD), and variational mode decomposition (VMD), to analyze three commodity futures prices data: corn from agricultural products, crude oil from energy, and gold from industrial metal. We applied these techniques to decompose the daily data of each commodity price from different periods and frequencies into individual intrinsic mode functions for EMD and modes for VMD. We used the hierarchical clustering method and Euclidean distance approach to classify the IMFs and modes into high-frequency, low-frequency, and trend. Next, applying statistical measures, particularly, the Pearson product-moment correlation coefficient, Kendall rank correlation, and Spearman rank correlation coefficient, we observed that the trend and low-frequency parts of the market price are the main drivers of commodity futures markets’ price fluctuations. The low-frequencies are caused by special events. In a nutshell, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary disequilibrium of supply-demand.

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Section: Related Studiesmentioning

confidence: 99%

Determinants of Commodity Futures Prices: Decomposition Approach

Antwi

Gyamfi

Kyei

et al. 2021

Mathematical Problems in Engineering

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“…e meaning of each coefficient in the formula is the same as formula (8). Shear strength requirements: For T-section or I-section simply supported beams,…”

Section: Constraints Flexural Strength Requirementsmentioning

confidence: 99%

“…A method of boarding gate allocation based on IPOQEA is proposed, and the effectiveness of the proposed method is verified by the actual operation data of Baiyun Airport [6]. In addition, Jin et al used mathematical models to optimize the design of financial market models and circuit control systems [7][8][9][10]. Seeking the lowest cost design under the premise of meeting and application is to optimize the design.…”

Section: Introductionmentioning

confidence: 99%

New One‐Dimensional Search Iteration Algorithm and Engineering Application

Luo

Meng

Wang

et al. 2021

Shock and Vibration

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In structural optimization design, obtaining the optimal solution of the objective function is the key to optimal design, and one-dimensional search is one of the important methods for function optimization. The Golden Section method is the main method of one-dimensional search, which has better convergence and stability. Based on the solution of the Golden Section method, this paper proposes an efficient one-dimensional search algorithm, which has the advantages of fast convergence and good stability. An objective function calculation formula is introduced to compare and analyse this method with the Golden Section method, Newton method, and Fibonacci method. It is concluded that when the accuracy is set to 0.1, the new algorithm needs 3 iterations to obtain the target value. The Golden Section method takes 11 iterations, and the Fibonacci method requires 11 iterations. The Newton method cannot obtain the target value. When the accuracy is set to 0.01, the number of iterations of the new method is still the least. The optimized design of the T-section beam is introduced for engineering application research. When the accuracy is set to 0.1, the new method needs 3 iterations to obtain the target value and the Golden Section method requires 13 iterations. When the accuracy is set to 0.01, the new method requires 4 iterations and the Golden Section method requires 18 iterations. The new method has significant advantages in the one-dimensional search optimization problem.

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“…Zhang [33] analyzed a coupled bi-stable SR system under Lévy noise. In addition, some combination methods with other algorithms have been proposed in recent years [34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

et al. 2021

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In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

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Valuation of interest rate ceiling and floor based on the uncertain fractional differential equation in Caputo sense

Cited by 20 publications

References 30 publications

Determinants of Commodity Futures Prices: Decomposition Approach

Determinants of Commodity Futures Prices: Decomposition Approach

New One‐Dimensional Search Iteration Algorithm and Engineering Application

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

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