Pub Date : 2019-06-01DOI: 10.1142/S0219091519500139
Hong-Yi Chen, Cheng-Few Lee, Tzu Tai
We develop a simultaneous determination model of capital structure and stock returns. Specifically, we incorporate the managerial investment autonomy theory into the structural equation modeling with confirmatory factor analysis to jointly determine the capital structure and stock return. Besides attributes introduced in previous studies, we introduce indicators affecting a firm’s financing decision, such as managerial entrenchment, macroeconomic factors, government financial policy, and pricing factors. Empirical results show that stock returns, asset structure, growth, industry classification, uniqueness, volatility, financial rating, profitability, government financial policy, and managerial entrenchment are major factors of the capital structure.
{"title":"The Joint Determinants of Capital Structure and Stock Rate of Return: A LISREL Model Approach","authors":"Hong-Yi Chen, Cheng-Few Lee, Tzu Tai","doi":"10.1142/S0219091519500139","DOIUrl":"https://doi.org/10.1142/S0219091519500139","url":null,"abstract":"We develop a simultaneous determination model of capital structure and stock returns. Specifically, we incorporate the managerial investment autonomy theory into the structural equation modeling with confirmatory factor analysis to jointly determine the capital structure and stock return. Besides attributes introduced in previous studies, we introduce indicators affecting a firm’s financing decision, such as managerial entrenchment, macroeconomic factors, government financial policy, and pricing factors. Empirical results show that stock returns, asset structure, growth, industry classification, uniqueness, volatility, financial rating, profitability, government financial policy, and managerial entrenchment are major factors of the capital structure.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130667797","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 : 2015-06-24DOI: 10.1142/S0219091515500101
Wen-Ming Szu, Yi-Chen Wang, Wan-Ru Yang
This paper investigates the characteristics of implied risk-neutral distributions separately derived from Taiwan stock index call and put options prices. Differences in risk-neutral skewness and kurtosis between call and put options indicate deviations from put-call parity. We find that the sentiment effect is significantly related to differences between call and put option prices. Our results suggest the differential impact of investor sentiment and consumer sentiment on call and put option traders' expectations about underlying asset prices. Moreover, rational and irrational sentiment components have different influences on call and put option traders' beliefs.
{"title":"How Does Investor Sentiment Affect Implied Risk-Neutral Distributions of Call and Put Options?","authors":"Wen-Ming Szu, Yi-Chen Wang, Wan-Ru Yang","doi":"10.1142/S0219091515500101","DOIUrl":"https://doi.org/10.1142/S0219091515500101","url":null,"abstract":"This paper investigates the characteristics of implied risk-neutral distributions separately derived from Taiwan stock index call and put options prices. Differences in risk-neutral skewness and kurtosis between call and put options indicate deviations from put-call parity. We find that the sentiment effect is significantly related to differences between call and put option prices. Our results suggest the differential impact of investor sentiment and consumer sentiment on call and put option traders' expectations about underlying asset prices. Moreover, rational and irrational sentiment components have different influences on call and put option traders' beliefs.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124926655","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 : 2013-04-01DOI: 10.1016/J.JBANKFIN.2012.11.019
Hong-Yi Chen, Manak C. Gupta, Alice C. Lee, Cheng-Few Lee
{"title":"Sustainable Growth Rate, Optimal Growth Rate, and Optimal Payout Ratio: A Joint Optimization Approach","authors":"Hong-Yi Chen, Manak C. Gupta, Alice C. Lee, Cheng-Few Lee","doi":"10.1016/J.JBANKFIN.2012.11.019","DOIUrl":"https://doi.org/10.1016/J.JBANKFIN.2012.11.019","url":null,"abstract":"","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"118274830","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 : 2009-06-01DOI: 10.1142/S0219091509001605
Ren‐Raw Chen, Cheng-Few Lee, Han-Hsing Lee
In this essay, we empirically test the Constant–Elasticity-of-Variance (CEV) option pricing model by Cox (1975, 1996) and Cox and Ross (1976), and compare the performances of the CEV and alternative option pricing models, mainly the stochastic volatility model, in terms of European option pricing and cost-accuracy based analysis of their numerical procedures.In European-style option pricing, we have tested the empirical pricing performance of the CEV model and compared the results with those by Bakshi et al. (1997). The CEV model, introducing only one more parameter compared with Black-Scholes formula, improves the performance notably in all of the tests of in-sample, out-of-sample and the stability of implied volatility. Furthermore, with a much simpler model, the CEV model can still perform better than the stochastic volatility model in short term and out-of-the-money categories. When applied to American option pricing, high-dimensional lattice models are prohibitively expensive. Our numerical experiments clearly show that the CEV model performs much better in terms of the speed of convergence to its closed form solution, while the implementation cost of the stochastic volatility model is too high and practically infeasible for empirical work.In summary, with a much less implementation cost and faster computational speed, the CEV option pricing model could be a better candidate than more complex option pricing models, especially when one wants to apply the CEV process for pricing more complicated path-dependent options or credit risk models.
本文对Cox(1975,1996)和Cox and Ross(1976)提出的恒弹性方差(CEV)期权定价模型进行了实证检验，并比较了CEV和替代期权定价模型(主要是随机波动率模型)在欧式期权定价和基于成本准确性的数值过程分析方面的表现。在欧式期权定价中，我们检验了CEV模型的实证定价表现，并与Bakshi et al.(1997)的结果进行了比较。与Black-Scholes公式相比，CEV模型只多引入了一个参数，在样本内、样本外和隐含波动率稳定性的所有检验中，CEV模型的性能都得到了显著提高。此外，使用一个更简单的模型，CEV模型在短期和货币外类别中仍然比随机波动率模型表现更好。当应用于美式期权定价时，高维晶格模型的成本高得令人望而却步。我们的数值实验清楚地表明，CEV模型在收敛到其封闭形式解的速度方面表现得更好，而随机波动率模型的实施成本太高，实际上不适合实证工作。综上所述，与更复杂的期权定价模型相比，CEV期权定价模型具有更低的实施成本和更快的计算速度，特别是当人们希望将CEV过程应用于更复杂的路径依赖期权或信用风险模型的定价时。
{"title":"Empirical Performance of the Constant Elasticity Variance Option Pricing Model","authors":"Ren‐Raw Chen, Cheng-Few Lee, Han-Hsing Lee","doi":"10.1142/S0219091509001605","DOIUrl":"https://doi.org/10.1142/S0219091509001605","url":null,"abstract":"In this essay, we empirically test the Constant–Elasticity-of-Variance (CEV) option pricing model by Cox (1975, 1996) and Cox and Ross (1976), and compare the performances of the CEV and alternative option pricing models, mainly the stochastic volatility model, in terms of European option pricing and cost-accuracy based analysis of their numerical procedures.In European-style option pricing, we have tested the empirical pricing performance of the CEV model and compared the results with those by Bakshi et al. (1997). The CEV model, introducing only one more parameter compared with Black-Scholes formula, improves the performance notably in all of the tests of in-sample, out-of-sample and the stability of implied volatility. Furthermore, with a much simpler model, the CEV model can still perform better than the stochastic volatility model in short term and out-of-the-money categories. When applied to American option pricing, high-dimensional lattice models are prohibitively expensive. Our numerical experiments clearly show that the CEV model performs much better in terms of the speed of convergence to its closed form solution, while the implementation cost of the stochastic volatility model is too high and practically infeasible for empirical work.In summary, with a much less implementation cost and faster computational speed, the CEV option pricing model could be a better candidate than more complex option pricing models, especially when one wants to apply the CEV process for pricing more complicated path-dependent options or credit risk models.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115377602","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 : 1900-01-01DOI: 10.1142/9789811202391_0001
Cheng-Few Lee
The main purpose of this introductory chapter is to give an overview of the following 130 papers, which discuss financial econometrics, mathematics, statistics, and machine learning. There are eight sections in this introductory chapter. Section 1 is the introduction, Section 2 discusses financial econometrics, Section 3 explores financial mathematics, and Section 4 discusses financial statistics. Section 5 of this introductory chapter discusses financial technology and machine learning, Section 6 explores applications of financial econometrics, mathematics, statistics, and machine learning, and Section 7 gives an overview in terms of chapter and keyword classification of the handbook. Finally, Section 8 is a summary and includes some remarks.
{"title":"Introduction to Financial Econometrics, Mathematics, Statistics, and Machine Learning","authors":"Cheng-Few Lee","doi":"10.1142/9789811202391_0001","DOIUrl":"https://doi.org/10.1142/9789811202391_0001","url":null,"abstract":"The main purpose of this introductory chapter is to give an overview of the following 130 papers, which discuss financial econometrics, mathematics, statistics, and machine learning. There are eight sections in this introductory chapter. Section 1 is the introduction, Section 2 discusses financial econometrics, Section 3 explores financial mathematics, and Section 4 discusses financial statistics. Section 5 of this introductory chapter discusses financial technology and machine learning, Section 6 explores applications of financial econometrics, mathematics, statistics, and machine learning, and Section 7 gives an overview in terms of chapter and keyword classification of the handbook. Finally, Section 8 is a summary and includes some remarks.","PeriodicalId":188545,"journal":{"name":"Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125080622","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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