Pub Date : 2022-07-20DOI: 10.1142/s2424786322500177
V. Sangeetha, K. Thirusangu, P. Elumalai
Transportation problem has been employed in different range of operations amd the optimization of such problem is significant in many areas. A simple penalty and rapid process is used in this paper in order to obtain the lowest shipping cost for the transportation problems. A new method is proposed to extract the optimal solution of transportation problem. First, we solve a transportation problem employing a new way, and we compare this new proposed method solution to the VAM and MODI methods. Following that, we obtain that the transportation solution of the proposed method is equal to the MODI solution. This result demonstrates that by utilizing the novel proposed strategy, we can identify the straight optimal solution to the majority of transportation problems. Finally, the solution process is mathematically presented.
{"title":"A simple concept with minimum steps for solving the transportation problem to obtain the lowest shipping cost","authors":"V. Sangeetha, K. Thirusangu, P. Elumalai","doi":"10.1142/s2424786322500177","DOIUrl":"https://doi.org/10.1142/s2424786322500177","url":null,"abstract":"Transportation problem has been employed in different range of operations amd the optimization of such problem is significant in many areas. A simple penalty and rapid process is used in this paper in order to obtain the lowest shipping cost for the transportation problems. A new method is proposed to extract the optimal solution of transportation problem. First, we solve a transportation problem employing a new way, and we compare this new proposed method solution to the VAM and MODI methods. Following that, we obtain that the transportation solution of the proposed method is equal to the MODI solution. This result demonstrates that by utilizing the novel proposed strategy, we can identify the straight optimal solution to the majority of transportation problems. Finally, the solution process is mathematically presented.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46371865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-30DOI: 10.1142/s2424786322500153
N. Chai, Baofeng Shi
The existing default discrimination models based on evaluation indicators are difficult to achieve higher credit risk identification performance of farmers’ default status under the situation of insufficient credit information and low correlation between indicators and default risk. Those models are difficult to find out the fundamental causes of farmers’ default risk. A credit risk discrimination model based on credit features strongly with default status is established to evaluate the farmer’s credit risk. Term frequency inverse document frequency and sentiment dictionary analysis method are used to quantify long text indicators, then the K-means method is used to Boolean the numerical data. The APRIORI algorithm is used to mine the credit features strongly associated with the default status. Finally, the default status of farmers is judged based on those credit features. The model is detailed using actual bank data from 2044 farmers within China. According to the five-evaluation criterion of AUC, F1-score, Type II-error, Balance error rate and G-mean, the empirical results show that the ability of the credit risk discrimination model with credit features is higher than that of the model based on evaluation indicators. This finding provides a new idea for commercial banks to measure the default risk of farmers, and provides a reference for the formulation of strategies to enhance farmers’ credit.
{"title":"Evaluating farmers’ credit risk: A decision combination approach based on credit feature","authors":"N. Chai, Baofeng Shi","doi":"10.1142/s2424786322500153","DOIUrl":"https://doi.org/10.1142/s2424786322500153","url":null,"abstract":"The existing default discrimination models based on evaluation indicators are difficult to achieve higher credit risk identification performance of farmers’ default status under the situation of insufficient credit information and low correlation between indicators and default risk. Those models are difficult to find out the fundamental causes of farmers’ default risk. A credit risk discrimination model based on credit features strongly with default status is established to evaluate the farmer’s credit risk. Term frequency inverse document frequency and sentiment dictionary analysis method are used to quantify long text indicators, then the K-means method is used to Boolean the numerical data. The APRIORI algorithm is used to mine the credit features strongly associated with the default status. Finally, the default status of farmers is judged based on those credit features. The model is detailed using actual bank data from 2044 farmers within China. According to the five-evaluation criterion of AUC, F1-score, Type II-error, Balance error rate and G-mean, the empirical results show that the ability of the credit risk discrimination model with credit features is higher than that of the model based on evaluation indicators. This finding provides a new idea for commercial banks to measure the default risk of farmers, and provides a reference for the formulation of strategies to enhance farmers’ credit.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43550430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-14DOI: 10.1142/s242478632250013x
Dejun Xie, David A. Edwards, Xiaoxia Wu
In this paper, a novel algorithm for determining the free exercise boundary for high-dimensional Bermudan option problems is presented. First, a rough estimate of the boundary is constructed on a fine (daily) time grid. This rough estimate is used to generate a more accurate estimate on a coarse time grid (exercise opportunities). Antithetic branching is used to reduce the computational workload. The method is validated by comparing it with other methods of solving the standard Black–Scholes problem. Finally, the method is applied to two cases of Bermudan options with a second stochastic variable: a stochastic interest rate and a stochastic volatility.
{"title":"Optimal exercise frontier of Bermudan options by simulation methods","authors":"Dejun Xie, David A. Edwards, Xiaoxia Wu","doi":"10.1142/s242478632250013x","DOIUrl":"https://doi.org/10.1142/s242478632250013x","url":null,"abstract":"In this paper, a novel algorithm for determining the free exercise boundary for high-dimensional Bermudan option problems is presented. First, a rough estimate of the boundary is constructed on a fine (daily) time grid. This rough estimate is used to generate a more accurate estimate on a coarse time grid (exercise opportunities). Antithetic branching is used to reduce the computational workload. The method is validated by comparing it with other methods of solving the standard Black–Scholes problem. Finally, the method is applied to two cases of Bermudan options with a second stochastic variable: a stochastic interest rate and a stochastic volatility.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49111097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-13DOI: 10.1142/s2424786322500141
Yuyang Lin, Qi Huang, Qiyin Zhong, Muyang Li, Yan Li, Fei Ma
Financial time-series prediction has been a demanding and popular subject in many fields. Latest progress in the deep learning technique, especially the deep neural network, shows great potentials in accomplishing this difficult task. This study explores the possible neural networks to improve the accuracy of the financial time-series prediction, while the main focus is to predict the closing price for next trading day. In this paper, we propose a new attention-based LSTM model (AT-LSTM) by combining the Long Short-Term Memory (LSTM) networks with the attention mechanism. Six stock markets indices with four features were used as the input to the model. We evaluate the model performance in terms of MSE, RMSE and MAE. The results for these three metrics are 0.4537, 0.6736 and 0.4858, respectively. The results suggest that our model is skillful in capturing financial time series, and the predictions are robust and stable. Furthermore, we compared our results with the previous work. As a result, our proposed AT-LSTM exhibits a significant performance improvement and outperforms other methods.
{"title":"A new attention-based LSTM model for closing stock price prediction","authors":"Yuyang Lin, Qi Huang, Qiyin Zhong, Muyang Li, Yan Li, Fei Ma","doi":"10.1142/s2424786322500141","DOIUrl":"https://doi.org/10.1142/s2424786322500141","url":null,"abstract":"Financial time-series prediction has been a demanding and popular subject in many fields. Latest progress in the deep learning technique, especially the deep neural network, shows great potentials in accomplishing this difficult task. This study explores the possible neural networks to improve the accuracy of the financial time-series prediction, while the main focus is to predict the closing price for next trading day. In this paper, we propose a new attention-based LSTM model (AT-LSTM) by combining the Long Short-Term Memory (LSTM) networks with the attention mechanism. Six stock markets indices with four features were used as the input to the model. We evaluate the model performance in terms of MSE, RMSE and MAE. The results for these three metrics are 0.4537, 0.6736 and 0.4858, respectively. The results suggest that our model is skillful in capturing financial time series, and the predictions are robust and stable. Furthermore, we compared our results with the previous work. As a result, our proposed AT-LSTM exhibits a significant performance improvement and outperforms other methods.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44623098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-30DOI: 10.1142/s2424786322500128
H. H. Mirza, H. Hussain, Warda Gull
The primary focus of this study is to investigate the impact of advertising expenditure on firm performance. In light of the existing literature, this study considers four proxies of firm performance i.e., Sales (SLS), Return on Assets (ROA), Market-to-Book Ratio (MBR) and Market Capitalization (MC). The sample data for the purpose of estimation consists of 100 listed companies selected randomly from Pakistan Stock Exchange (PSX) during 2005–2018. The results show that advertising spending has a significantly positive impact on firm’s performance. This is also true for lagged value of advertising, where the results show significant positive relationship of lagged advertising on firm performance. This study supports the signaling effect of advertising expenditure on performance of Pakistani firms.
{"title":"Impact of advertising expenditure on firm performance: Evidence from listed companies of Pakistan","authors":"H. H. Mirza, H. Hussain, Warda Gull","doi":"10.1142/s2424786322500128","DOIUrl":"https://doi.org/10.1142/s2424786322500128","url":null,"abstract":"The primary focus of this study is to investigate the impact of advertising expenditure on firm performance. In light of the existing literature, this study considers four proxies of firm performance i.e., Sales (SLS), Return on Assets (ROA), Market-to-Book Ratio (MBR) and Market Capitalization (MC). The sample data for the purpose of estimation consists of 100 listed companies selected randomly from Pakistan Stock Exchange (PSX) during 2005–2018. The results show that advertising spending has a significantly positive impact on firm’s performance. This is also true for lagged value of advertising, where the results show significant positive relationship of lagged advertising on firm performance. This study supports the signaling effect of advertising expenditure on performance of Pakistani firms.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47127353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-30DOI: 10.1142/s2424786322500116
Abhijit Chakraborty, A. Das
The development of global economy has pushed up the importance of insurance industry in the growth of an economy. This paper intends to study the non-life insurance sector with an objective to identify the macro-economic factors that influence its growth. Time series data of 37 years is considered using Johansen & Engle Granger Cointegration and Ordinary Least Square method. It was found that Final Consumption Expenditure plays a negatively significant role in influencing non-life insurance sector in India. The practical implication of this study lies in controlling the responsible factors through appropriate policy measures to ensure a sustainable growth of the non-life Insurance sector.
{"title":"Impact analysis of macro-economic factors on non-life insurance sector in India","authors":"Abhijit Chakraborty, A. Das","doi":"10.1142/s2424786322500116","DOIUrl":"https://doi.org/10.1142/s2424786322500116","url":null,"abstract":"The development of global economy has pushed up the importance of insurance industry in the growth of an economy. This paper intends to study the non-life insurance sector with an objective to identify the macro-economic factors that influence its growth. Time series data of 37 years is considered using Johansen & Engle Granger Cointegration and Ordinary Least Square method. It was found that Final Consumption Expenditure plays a negatively significant role in influencing non-life insurance sector in India. The practical implication of this study lies in controlling the responsible factors through appropriate policy measures to ensure a sustainable growth of the non-life Insurance sector.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48103585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-26DOI: 10.1142/s2424786322500104
Nawaf Almaskati
This paper provides a summary of the current literature related to applying machine learning algorithms in the field of finance with a focus on three main areas: asset pricing, bankruptcy prediction and detection of financial reporting anomalies. The paper also briefly discusses the most popular machine learning techniques used in finance and provides a general overview of some important concepts such as generalization and over- and under-fitting as well as a discussion of potential remedies. Last, the paper summarizes the various indicators and metrics available to evaluate and compare the performance of regression and classification machine learning models before discussing general research trends and potential future research.
{"title":"Machine learning in finance: Major applications, issues, metrics, and future trends","authors":"Nawaf Almaskati","doi":"10.1142/s2424786322500104","DOIUrl":"https://doi.org/10.1142/s2424786322500104","url":null,"abstract":"This paper provides a summary of the current literature related to applying machine learning algorithms in the field of finance with a focus on three main areas: asset pricing, bankruptcy prediction and detection of financial reporting anomalies. The paper also briefly discusses the most popular machine learning techniques used in finance and provides a general overview of some important concepts such as generalization and over- and under-fitting as well as a discussion of potential remedies. Last, the paper summarizes the various indicators and metrics available to evaluate and compare the performance of regression and classification machine learning models before discussing general research trends and potential future research.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47724861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1142/s2424786322500098
A. Kaleemullah, A. Suresh
Data clustering is a technique for analyzing the data that is incurred in various fields such as data processing, pattern recognition, knowledge discovery and machine learning. Feature clustering is an important paradigm for different types of feature selection techniques that aims to reduce redundant and irrelevant features from a given set of features in order to maintain load balance on the classification algorithm. The work proposed a PSO–GSO–MST, a hybrid approach that combines Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO). The work performs efficient feature selection with improved classification accuracy. Clustering analysis plays an important role in knowledge discovery and data mining. It adopts the unsupervised learning method, and the results of clustering are similar within the class and are different between the classes. Aiming at some shortcomings of traditional clustering algorithms, some techniques for clustering using natural heuristic algorithms have emerged. The proposed work performs cluster using optimized Minimum Spanning Tree (MST). The work aims to perform optimization of MST with the help of two renowned techniques such as PSO and GSO. The proposed PSO–GSO–MST is compared with state-of-the-art algorithms such as Clustering-based Feature Selection (CFS) and PSO–MST. The results show that the classification accuracy for the proposed PSO–GSO–MST performs better by 16.9% than CFS and by 4.7% than PSO–MST optimized CFS, respectively. The outcome of the work proves that the proposed algorithm achieves improved performance than the currently available algorithms and can be used for clustering applications.
{"title":"Reactive search-MST optimized clustering-based feature selection","authors":"A. Kaleemullah, A. Suresh","doi":"10.1142/s2424786322500098","DOIUrl":"https://doi.org/10.1142/s2424786322500098","url":null,"abstract":"Data clustering is a technique for analyzing the data that is incurred in various fields such as data processing, pattern recognition, knowledge discovery and machine learning. Feature clustering is an important paradigm for different types of feature selection techniques that aims to reduce redundant and irrelevant features from a given set of features in order to maintain load balance on the classification algorithm. The work proposed a PSO–GSO–MST, a hybrid approach that combines Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO). The work performs efficient feature selection with improved classification accuracy. Clustering analysis plays an important role in knowledge discovery and data mining. It adopts the unsupervised learning method, and the results of clustering are similar within the class and are different between the classes. Aiming at some shortcomings of traditional clustering algorithms, some techniques for clustering using natural heuristic algorithms have emerged. The proposed work performs cluster using optimized Minimum Spanning Tree (MST). The work aims to perform optimization of MST with the help of two renowned techniques such as PSO and GSO. The proposed PSO–GSO–MST is compared with state-of-the-art algorithms such as Clustering-based Feature Selection (CFS) and PSO–MST. The results show that the classification accuracy for the proposed PSO–GSO–MST performs better by 16.9% than CFS and by 4.7% than PSO–MST optimized CFS, respectively. The outcome of the work proves that the proposed algorithm achieves improved performance than the currently available algorithms and can be used for clustering applications.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46378100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-23DOI: 10.1142/s2424786321410139
Shuai Shao, Zhanzhong Shi, Yirong Shi
Artificial intelligence (AI) is the most significant technological revolution since we entered the 21st century. It has become a new focus of public attention and international competition. Industrial integration with AI technology not only brings vast opportunities for transformation and upgrading of enterprises but also has an impact on employment structure. Focusing on the fusion of the manufacturing industry integrating AI, we analyze the integration progress of AI and segmented manufacturing industries, describe a supply-and-demand situation of labor market with different skills, and discuss the impact of AI technology on manufacturing employment theoretically. Then we construct the propensity score matching–difference-in-difference model, divide intelligent manufacturing enterprises into various categories, and inspect the influences on the employment structure of different segmented manufacturing enterprises before and after integrating AI technology. Finally, we put forward efficient methods of transformation and upgrading of manufacturing enterprises and practical suggestions to solve problems on employment structure.
{"title":"Impact of AI on employment in manufacturing industry","authors":"Shuai Shao, Zhanzhong Shi, Yirong Shi","doi":"10.1142/s2424786321410139","DOIUrl":"https://doi.org/10.1142/s2424786321410139","url":null,"abstract":"Artificial intelligence (AI) is the most significant technological revolution since we entered the 21st century. It has become a new focus of public attention and international competition. Industrial integration with AI technology not only brings vast opportunities for transformation and upgrading of enterprises but also has an impact on employment structure. Focusing on the fusion of the manufacturing industry integrating AI, we analyze the integration progress of AI and segmented manufacturing industries, describe a supply-and-demand situation of labor market with different skills, and discuss the impact of AI technology on manufacturing employment theoretically. Then we construct the propensity score matching–difference-in-difference model, divide intelligent manufacturing enterprises into various categories, and inspect the influences on the employment structure of different segmented manufacturing enterprises before and after integrating AI technology. Finally, we put forward efficient methods of transformation and upgrading of manufacturing enterprises and practical suggestions to solve problems on employment structure.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43244813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-17DOI: 10.1142/s2424786322500062
Hua Wang, Junjun Zhu
The offshore RMB market has been showing a momentum of rapid development. However, the impact of this market on the RMB exchange rate has been less studied. This paper constructs an error correction model with structural mutations and focuses on the turning point for RMB Forward Rate, during the time before and after September 2011. The model is constructed considering the micro-institutional differences between domestic and offshore RMB forward exchange rates, the impact of spreads, the impact of spot exchange rates and international financial market shocks such as the VIX index of the US dollar index. We also apply the error correction model with abrupt changes method to select the model which is innovative. According to the results of the model, the domestic RMB foreign exchange derivatives market and the offshore RMB foreign exchange derivatives market jointly played a leading role in price discovery in the determination of both short-term and long-term RMB forward exchange rates, and we also found September 2011 was an important structural change point for the RMB forward curves both domestic and abroad. Before that period, interest rate parity did not play a positive role and the NDF exchange rate occupied the dominant role in price discovery; the domestic RMB forward exchange rate and the overseas NDF exchange rate were both driven by speculative factors.
{"title":"Investigation on transition of RMB forward exchange rate pricing mechanism based on error correction model with structural mutation","authors":"Hua Wang, Junjun Zhu","doi":"10.1142/s2424786322500062","DOIUrl":"https://doi.org/10.1142/s2424786322500062","url":null,"abstract":"The offshore RMB market has been showing a momentum of rapid development. However, the impact of this market on the RMB exchange rate has been less studied. This paper constructs an error correction model with structural mutations and focuses on the turning point for RMB Forward Rate, during the time before and after September 2011. The model is constructed considering the micro-institutional differences between domestic and offshore RMB forward exchange rates, the impact of spreads, the impact of spot exchange rates and international financial market shocks such as the VIX index of the US dollar index. We also apply the error correction model with abrupt changes method to select the model which is innovative. According to the results of the model, the domestic RMB foreign exchange derivatives market and the offshore RMB foreign exchange derivatives market jointly played a leading role in price discovery in the determination of both short-term and long-term RMB forward exchange rates, and we also found September 2011 was an important structural change point for the RMB forward curves both domestic and abroad. Before that period, interest rate parity did not play a positive role and the NDF exchange rate occupied the dominant role in price discovery; the domestic RMB forward exchange rate and the overseas NDF exchange rate were both driven by speculative factors.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43324327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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