Pub Date : 2023-09-05DOI: 10.1080/14697688.2023.2244531
Option Pricing, Saeed Marzban, E. Delage, Jonathan Yu-Meng Li
Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations either employ a static risk measure that violates time consistency, or are based on traditional dynamic programing solution schemes that are impracticable in problems with a large number of underlying assets (due to the curse of dimensionality) or with incomplete asset dynamics information. In this paper, we extend for the first time a famous off-policy deterministic actor-critic deep reinforcement learning (ACRL) algorithm to the problem of solving a risk averse Markov decision process that models risk using a time consistent recursive expectile risk measure. This new ACRL algorithm allows us to identify high quality time consistent hedging policies (and equal risk prices) for options, such as basket options, that cannot be handled using traditional methods, or in context where only historical trajectories of the underlying assets are available. Our numerical experiments, which involve both a simple vanilla option and a more exotic basket option, confirm that the new ACRL algorithm can produce (1) in simple environments, nearly optimal hedging policies, and highly accurate prices, simultaneously for a range of maturities (2) in complex environments, good quality policies and prices using reasonable amount of computing resources; and (3) overall, hedging strategies that actually outperform the strategies produced using static risk measures when the risk is evaluated at later points of time.
{"title":"Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures","authors":"Option Pricing, Saeed Marzban, E. Delage, Jonathan Yu-Meng Li","doi":"10.1080/14697688.2023.2244531","DOIUrl":"https://doi.org/10.1080/14697688.2023.2244531","url":null,"abstract":"Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations either employ a static risk measure that violates time consistency, or are based on traditional dynamic programing solution schemes that are impracticable in problems with a large number of underlying assets (due to the curse of dimensionality) or with incomplete asset dynamics information. In this paper, we extend for the first time a famous off-policy deterministic actor-critic deep reinforcement learning (ACRL) algorithm to the problem of solving a risk averse Markov decision process that models risk using a time consistent recursive expectile risk measure. This new ACRL algorithm allows us to identify high quality time consistent hedging policies (and equal risk prices) for options, such as basket options, that cannot be handled using traditional methods, or in context where only historical trajectories of the underlying assets are available. Our numerical experiments, which involve both a simple vanilla option and a more exotic basket option, confirm that the new ACRL algorithm can produce (1) in simple environments, nearly optimal hedging policies, and highly accurate prices, simultaneously for a range of maturities (2) in complex environments, good quality policies and prices using reasonable amount of computing resources; and (3) overall, hedging strategies that actually outperform the strategies produced using static risk measures when the risk is evaluated at later points of time.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"81 1","pages":"1411 - 1430"},"PeriodicalIF":1.3,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79246680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1080/14697688.2023.2244991
Wenjie Ding, Khelifa Mazouz, Owain ap Gwilym, Qingwei Wang
This paper explores an unexamined sentiment channel through which technical analysis can add value. We use a spectrum of technical trading strategies to build a daily market sentiment indicator that is highly correlated with other commonly used sentiment measures. This technical-analysis-based sentiment indicator positively predicts near-term returns and is inversely related to long-term returns in the cross-section. Simple trading strategies based on this sentiment indicator yield substantial abnormal returns. These results are consistent with the explanation that lack of synchronization induces rational arbitrageurs to exploit the mispricing before it is corrected.
{"title":"Technical analysis as a sentiment barometer and the cross-section of stock returns","authors":"Wenjie Ding, Khelifa Mazouz, Owain ap Gwilym, Qingwei Wang","doi":"10.1080/14697688.2023.2244991","DOIUrl":"https://doi.org/10.1080/14697688.2023.2244991","url":null,"abstract":"This paper explores an unexamined sentiment channel through which technical analysis can add value. We use a spectrum of technical trading strategies to build a daily market sentiment indicator that is highly correlated with other commonly used sentiment measures. This technical-analysis-based sentiment indicator positively predicts near-term returns and is inversely related to long-term returns in the cross-section. Simple trading strategies based on this sentiment indicator yield substantial abnormal returns. These results are consistent with the explanation that lack of synchronization induces rational arbitrageurs to exploit the mispricing before it is corrected.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136354277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-17DOI: 10.1080/14697688.2023.2243995
S. Kang, P. Létourneau
This paper investigates the effects of modifying a real option's characteristics on its holding value and optimal exercise decision using quantile-preserving spreads and stochastic dominance. We show that the change in exercise probability and timing depends on the preserved quantile, strike price, time of modification, and modification symmetry, and we significantly generalize previously obtained results to an unspecified underlying process and a general call-like payoff function. Our results offer testable predictions that contribute to the literature on climate finance, real options, and financial options and provide practical guidance for determining how to modify a real option to increase or decrease its exercise probability and timing.
{"title":"Model-free analysis of real option exercise probability and timing","authors":"S. Kang, P. Létourneau","doi":"10.1080/14697688.2023.2243995","DOIUrl":"https://doi.org/10.1080/14697688.2023.2243995","url":null,"abstract":"This paper investigates the effects of modifying a real option's characteristics on its holding value and optimal exercise decision using quantile-preserving spreads and stochastic dominance. We show that the change in exercise probability and timing depends on the preserved quantile, strike price, time of modification, and modification symmetry, and we significantly generalize previously obtained results to an unspecified underlying process and a general call-like payoff function. Our results offer testable predictions that contribute to the literature on climate finance, real options, and financial options and provide practical guidance for determining how to modify a real option to increase or decrease its exercise probability and timing.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"36 1","pages":"1531 - 1544"},"PeriodicalIF":1.3,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82536022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.1080/14697688.2023.2236148
Giorgio Costa, Garud N. Iyengar
AbstractWe propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parameter and the degree of robustness directly from data. End-to-end systems have an advantage in that information can be communicated between the prediction and decision layers during training, allowing the parameters to be trained for the final task rather than solely for predictive performance. However, existing end-to-end systems are not able to quantify and correct for the impact of model risk on the decision layer. Our proposed distributionally robust end-to-end portfolio selection system explicitly accounts for the impact of model risk. The decision layer chooses portfolios by solving a minimax problem where the distribution of the asset returns is assumed to belong to an ambiguity set centered around a nominal distribution. Using convex duality, we recast the minimax problem in a form that allows for efficient training of the end-to-end system.Keywords: Portfolio optimizationAsset allocationMachine learningDistributionally robust optimizationStatistical ambiguity Disclosure statementNo potential conflict of interest was reported by the author(s).Data availabilityThe data that support the numerical experiments in this study are available online from the following two sources below. Feature data: These data are openly available through Kenneth French's Data Library at https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.Asset data: AlphaVantage at www.alphavantage.co. Restrictions apply to the availability of these data, which were used under a free academic license for this study.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Notes1 For a description of ϕ-divergence functions and their convex conjugates, please refer to Tables 2 and 4 in Ben-Tal et al. (Citation2013).2 If ϕ is the Hellinger distance, the DR layer reduces to a second-order cone program provided the function R(X) in Proposition 2.1 is quadratic or piecewise linear. If ϕ is the Variational distance, the DR layer reduces to a linear program provided the function R(X) is piecewise linear. Otherwise, the complexity of the problem is dictated by the choice of R(X).3 Note that we have defined the Sharpe ratio using the portfolio returns rather than the portfolio excess returns (i.e. the returns in excess of the risk-free rate).Additional informationFundingThis work was supported by Natural Sciences and Engineering Research Council of Canada [PDF - 557467 - 2021].
摘要提出了一种端到端的分布鲁棒组合构建系统，该系统将资产收益预测模型与分布鲁棒组合优化模型相结合。我们还展示了如何直接从数据中学习风险容忍参数和鲁棒性程度。端到端系统的优势在于，在训练过程中，信息可以在预测层和决策层之间进行交流，允许为最终任务训练参数，而不仅仅是为了预测性能。然而，现有的端到端系统无法量化和纠正模型风险对决策层的影响。我们提出的分布式健壮的端到端投资组合选择系统明确地考虑了模型风险的影响。决策层通过解决一个极大极小问题来选择投资组合，在这个问题中，资产收益的分布被假设属于一个以名义分布为中心的模糊集。利用凸对偶性，我们将极大极小问题转化为一种允许对端到端系统进行有效训练的形式。关键词:投资组合优化资产配置机器学习分布鲁棒性优化统计歧义披露声明作者未报告潜在利益冲突数据可用性支持本研究数值实验的数据可从以下两个来源在线获得。特征数据:这些数据可以通过Kenneth French的数据库(https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.Asset data: AlphaVantage at www.alphavantage.co)获得。这些数据的可用性受到限制，这些数据在本研究的免费学术许可下使用。这篇文章经过了细微的修改。这些变化不影响文章的学术内容。注1关于ϕ-散度函数及其凸共轭的描述，请参见Ben-Tal et al. (Citation2013)中的表2和表4如果φ是海灵格距离，则假设命题2.1中的函数R(X)是二次的或分段线性的，则DR层简化为二阶锥规划。如果ϕ是变分距离，则提供函数R(X)是分段线性的，则DR层减少为线性程序。否则，问题的复杂性取决于R(X)的选择请注意，我们使用投资组合收益而不是投资组合超额收益(即超过无风险利率的收益)来定义夏普比率。本研究得到了加拿大自然科学与工程研究委员会的支持[PDF - 557467 - 2021]。
{"title":"Distributionally robust end-to-end portfolio construction","authors":"Giorgio Costa, Garud N. Iyengar","doi":"10.1080/14697688.2023.2236148","DOIUrl":"https://doi.org/10.1080/14697688.2023.2236148","url":null,"abstract":"AbstractWe propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parameter and the degree of robustness directly from data. End-to-end systems have an advantage in that information can be communicated between the prediction and decision layers during training, allowing the parameters to be trained for the final task rather than solely for predictive performance. However, existing end-to-end systems are not able to quantify and correct for the impact of model risk on the decision layer. Our proposed distributionally robust end-to-end portfolio selection system explicitly accounts for the impact of model risk. The decision layer chooses portfolios by solving a minimax problem where the distribution of the asset returns is assumed to belong to an ambiguity set centered around a nominal distribution. Using convex duality, we recast the minimax problem in a form that allows for efficient training of the end-to-end system.Keywords: Portfolio optimizationAsset allocationMachine learningDistributionally robust optimizationStatistical ambiguity Disclosure statementNo potential conflict of interest was reported by the author(s).Data availabilityThe data that support the numerical experiments in this study are available online from the following two sources below. Feature data: These data are openly available through Kenneth French's Data Library at https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.Asset data: AlphaVantage at www.alphavantage.co. Restrictions apply to the availability of these data, which were used under a free academic license for this study.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Notes1 For a description of ϕ-divergence functions and their convex conjugates, please refer to Tables 2 and 4 in Ben-Tal et al. (Citation2013).2 If ϕ is the Hellinger distance, the DR layer reduces to a second-order cone program provided the function R(X) in Proposition 2.1 is quadratic or piecewise linear. If ϕ is the Variational distance, the DR layer reduces to a linear program provided the function R(X) is piecewise linear. Otherwise, the complexity of the problem is dictated by the choice of R(X).3 Note that we have defined the Sharpe ratio using the portfolio returns rather than the portfolio excess returns (i.e. the returns in excess of the risk-free rate).Additional informationFundingThis work was supported by Natural Sciences and Engineering Research Council of Canada [PDF - 557467 - 2021].","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135796155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-02DOI: 10.1080/14697688.2023.2236158
Fangquan Shi, L. Shu, Yiling Luo, X. Huo
Both convex and non-convex penalties have been widely proposed to tackle the sparse index tracking problem. Owing to their good property of generating sparse solutions, penalties based on the least absolute shrinkage and selection operator (LASSO) and its variations are often suggested in the stream of convex penalties. However, the LASSO-type penalty is often shown to have poor out-of-sample performance, due to the relatively large biases introduced in the estimates of tracking portfolio weights by shrinking the parameter estimates toward to zero. On the other hand, non-convex penalties could be used to improve the bias issue of LASSO-type penalty. However, the resulting problem is non-convex optimization and thus is computationally intensive, especially in high-dimensional settings. Aimed at ameliorating bias introduced by LASSO-type penalty while preserving computational efficiency, this paper proposes a multi-step convex optimization approach based on the multi-step weighted LASSO (MSW-LASSO) for sparse index tracking. Empirical results show that the proposed method can achieve smaller out-of-sample tracking errors than those based on LASSO-type penalties and have performance competitive to those based on non-convex penalties.
{"title":"High-dimensional sparse index tracking based on a multi-step convex optimization approach","authors":"Fangquan Shi, L. Shu, Yiling Luo, X. Huo","doi":"10.1080/14697688.2023.2236158","DOIUrl":"https://doi.org/10.1080/14697688.2023.2236158","url":null,"abstract":"Both convex and non-convex penalties have been widely proposed to tackle the sparse index tracking problem. Owing to their good property of generating sparse solutions, penalties based on the least absolute shrinkage and selection operator (LASSO) and its variations are often suggested in the stream of convex penalties. However, the LASSO-type penalty is often shown to have poor out-of-sample performance, due to the relatively large biases introduced in the estimates of tracking portfolio weights by shrinking the parameter estimates toward to zero. On the other hand, non-convex penalties could be used to improve the bias issue of LASSO-type penalty. However, the resulting problem is non-convex optimization and thus is computationally intensive, especially in high-dimensional settings. Aimed at ameliorating bias introduced by LASSO-type penalty while preserving computational efficiency, this paper proposes a multi-step convex optimization approach based on the multi-step weighted LASSO (MSW-LASSO) for sparse index tracking. Empirical results show that the proposed method can achieve smaller out-of-sample tracking errors than those based on LASSO-type penalties and have performance competitive to those based on non-convex penalties.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"135 1","pages":"1361 - 1372"},"PeriodicalIF":1.3,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84231356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-26DOI: 10.1080/14697688.2023.2230264
Fang Zhao, Gang Li, Yanxia Lyu, Hong-Dong Ma, Xiaoqian Zhu
Credit fraud detection modeling helps prevent default risks and reduce economic losses, and increasingly sophisticated methods have been designed for predicting the default probability of clients. In such problems, the fact that the class of fraud clients is much smaller than the class of good clients makes it a challenge to detect the fraud class. To minimize the financial losses in extremely imbalanced datasets, this paper delivers a novel cost-sensitive ensemble model under the framework of deep forest. The model first introduces a cost-sensitive strategy to assign a higher cost to the fraud class, thereby improving the attention of the model to the fraud samples. As everyone knows, for the basic classifiers of ensemble learning, the greater their differences, the better the performance after ensemble. So the model adds superior cost-sensitive base classifiers into the cascade structure to improve the overall performance. The model also introduces Type II error as the convergence index to automatically adjust the depth of the cascade structure. The experiments conducted on the European credit dataset and a private electronic transaction dataset are presented to demonstrate the performance of the proposed method. The results indicate that the proposed model outperforms most benchmarks in detecting fraud samples.
{"title":"A cost-sensitive ensemble deep forest approach for extremely imbalanced credit fraud detection","authors":"Fang Zhao, Gang Li, Yanxia Lyu, Hong-Dong Ma, Xiaoqian Zhu","doi":"10.1080/14697688.2023.2230264","DOIUrl":"https://doi.org/10.1080/14697688.2023.2230264","url":null,"abstract":"Credit fraud detection modeling helps prevent default risks and reduce economic losses, and increasingly sophisticated methods have been designed for predicting the default probability of clients. In such problems, the fact that the class of fraud clients is much smaller than the class of good clients makes it a challenge to detect the fraud class. To minimize the financial losses in extremely imbalanced datasets, this paper delivers a novel cost-sensitive ensemble model under the framework of deep forest. The model first introduces a cost-sensitive strategy to assign a higher cost to the fraud class, thereby improving the attention of the model to the fraud samples. As everyone knows, for the basic classifiers of ensemble learning, the greater their differences, the better the performance after ensemble. So the model adds superior cost-sensitive base classifiers into the cascade structure to improve the overall performance. The model also introduces Type II error as the convergence index to automatically adjust the depth of the cascade structure. The experiments conducted on the European credit dataset and a private electronic transaction dataset are presented to demonstrate the performance of the proposed method. The results indicate that the proposed model outperforms most benchmarks in detecting fraud samples.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"15 1","pages":"1397 - 1409"},"PeriodicalIF":1.3,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85625705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-25DOI: 10.1080/14697688.2023.2229375
Antonio Figueiredo, P. Jain, Suchi Mishra
We employ NASDAQ order level data to analyze intraday trading at option expirations and cross-market price pressure spillover. We observe more fleeting orders in optionable stocks on option expiration versus non-expiration days. The relation between NBBO proximity to strike prices and fleeting order direction, the relation between option Open Interest and fleeting order direction, as well as their placement outside NBBO suggest spoofing and price manipulation rather than a simple search for latent liquidity. We show that fleeting orders impact subsequent NBBO and increase likelihood of stock prices crossing option strike prices on option expiration days.
{"title":"The role of fleeting orders on option expiration days","authors":"Antonio Figueiredo, P. Jain, Suchi Mishra","doi":"10.1080/14697688.2023.2229375","DOIUrl":"https://doi.org/10.1080/14697688.2023.2229375","url":null,"abstract":"We employ NASDAQ order level data to analyze intraday trading at option expirations and cross-market price pressure spillover. We observe more fleeting orders in optionable stocks on option expiration versus non-expiration days. The relation between NBBO proximity to strike prices and fleeting order direction, the relation between option Open Interest and fleeting order direction, as well as their placement outside NBBO suggest spoofing and price manipulation rather than a simple search for latent liquidity. We show that fleeting orders impact subsequent NBBO and increase likelihood of stock prices crossing option strike prices on option expiration days.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"90 1","pages":"1511 - 1529"},"PeriodicalIF":1.3,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80700022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.1080/14697688.2023.2223649
Tianran Dai, Liang‐Chih Liu, Sharon S. Yang
Prepayment options can be exercised to terminate reverse mortgages (RM hereafter) early and receive house prices, minus loan balances, at the expense of future annuity proceeds. Prior RM evaluation studies use probability or intensity models to calibrate option exercise policies with historical prepayment records and may not apply to countries without sufficient historical records. In addition, these models may fail to capture time-varying policies due to changing market conditions. Accordingly, insurers may run the risk of undervaluing option premiums and overestimating fair annuity rates. To find optimal exercise policies that maximize option premiums and establish the most conservative annuity rates, we propose a three-dimensional tree for modeling stochastic house prices, interest rates, and mortality risks. We analyze the gain and loss to exercise the option in each scenario to determine the optimal policy. Fair annuity rates are evaluated to ensure that expected insurer losses (i.e. loan balances exceeding house values) equal gains (i.e. insurance premiums plus house values exceeding loan balances). We find that such non-optimal exercise policies undervalue option premiums and overestimate fair annuity rates. Increasing upfront premiums, insurance premium rates, and early redemption charges reduce prepayment incentives and increase fair annuity rates. We also analyze influences from factors such as the policyholder's age and volatilities of house prices and interest rates.
{"title":"Pricing tenure payment reverse mortgages with optimal exercised prepayment options by accounting for house prices, interest rates, and mortality risk","authors":"Tianran Dai, Liang‐Chih Liu, Sharon S. Yang","doi":"10.1080/14697688.2023.2223649","DOIUrl":"https://doi.org/10.1080/14697688.2023.2223649","url":null,"abstract":"Prepayment options can be exercised to terminate reverse mortgages (RM hereafter) early and receive house prices, minus loan balances, at the expense of future annuity proceeds. Prior RM evaluation studies use probability or intensity models to calibrate option exercise policies with historical prepayment records and may not apply to countries without sufficient historical records. In addition, these models may fail to capture time-varying policies due to changing market conditions. Accordingly, insurers may run the risk of undervaluing option premiums and overestimating fair annuity rates. To find optimal exercise policies that maximize option premiums and establish the most conservative annuity rates, we propose a three-dimensional tree for modeling stochastic house prices, interest rates, and mortality risks. We analyze the gain and loss to exercise the option in each scenario to determine the optimal policy. Fair annuity rates are evaluated to ensure that expected insurer losses (i.e. loan balances exceeding house values) equal gains (i.e. insurance premiums plus house values exceeding loan balances). We find that such non-optimal exercise policies undervalue option premiums and overestimate fair annuity rates. Increasing upfront premiums, insurance premium rates, and early redemption charges reduce prepayment incentives and increase fair annuity rates. We also analyze influences from factors such as the policyholder's age and volatilities of house prices and interest rates.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"33 1","pages":"1325 - 1339"},"PeriodicalIF":1.3,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86311579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.1080/14697688.2023.2230241
Christakis Charalambous, Spiros H. Martzoukos, Zenon Taoushianis
We develop a framework to simultaneously compute the unobservable parameters underlying the structural-parametric models for bankruptcy prediction. More specifically, we compute the unobservable parameters such as, asset value and asset volatility, through learning by embedding in the structural models a neural network that maps the neural network’s input space (e.g. companies’ observable financial and market data) to the unobservable parameter space. Within such a ‘neuro-structural’ framework, the neural network and the structural model work together as a one unit during the learning phase by providing to each other forward and backward information, respectively, until the weights of the neural network are optimized according to a merit function. Empirical results show that structural models, like the Black-Scholes-Merton and the Down-and-Out option models, with parameters computed with our approach, perform better than alternative specifications of the structural models, out of sample, in terms of discriminatory power, information content and economic impact. Importantly, they also perform better than a standard neural network, suggesting that the co-joint dynamics between the neural network and the structural model are useful during the learning phase and can improve the prediction performance (and the training efficiency) of neural networks. Finally, our approach provides methodological (and empirical) enhancements over logit specifications such as, Campbell et al. [In search of distress risk. J Finance, 2008, 63, 2899–2939]. There, financial and market data are the inputs, and the output is the probability of bankruptcy whereas our approach includes an intermediary step to obtain the unobservable parameters and subsequently the probability of bankruptcy.
{"title":"A neuro-structural framework for bankruptcy prediction","authors":"Christakis Charalambous, Spiros H. Martzoukos, Zenon Taoushianis","doi":"10.1080/14697688.2023.2230241","DOIUrl":"https://doi.org/10.1080/14697688.2023.2230241","url":null,"abstract":"<p>We develop a framework to simultaneously compute the unobservable parameters underlying the structural-parametric models for bankruptcy prediction. More specifically, we compute the unobservable parameters such as, asset value and asset volatility, through learning by embedding in the structural models a neural network that maps the neural network’s input space (e.g. companies’ observable financial and market data) to the unobservable parameter space. Within such a ‘neuro-structural’ framework, the neural network and the structural model work together as a one unit during the learning phase by providing to each other forward and backward information, respectively, until the weights of the neural network are optimized according to a merit function. Empirical results show that structural models, like the Black-Scholes-Merton and the Down-and-Out option models, with parameters computed with our approach, perform better than alternative specifications of the structural models, out of sample, in terms of discriminatory power, information content and economic impact. Importantly, they also perform better than a standard neural network, suggesting that the co-joint dynamics between the neural network and the structural model are useful during the learning phase and can improve the prediction performance (and the training efficiency) of neural networks. Finally, our approach provides methodological (and empirical) enhancements over logit specifications such as, Campbell <i>et al.</i> [In search of distress risk. <i>J Finance</i>, 2008, <b>63</b>, 2899–2939]. There, financial and market data are the inputs, and the output is the probability of bankruptcy whereas our approach includes an intermediary step to obtain the unobservable parameters and subsequently the probability of bankruptcy.</p>","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"76 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.1080/14697688.2023.2221296
J. Lee, Do-Gyun Kwon, Yongjae Lee, J. Kim, W. Kim
The multi-stage stochastic programming (MSP) approach is widely used to solve financial planning problems owing to its flexibility. However, the size of an MSP problem grows exponentially with the number of stages, and such problem can easily become computationally intractable. Financial planning problems often consider planning horizons of several decades, and thus, the curse of dimensionality can become a critical issue. Stochastic dual dynamic programming (SDDP), a sampling-based decomposition algorithm, has emerged to resolve this issue. While SDDP has been successfully implemented in the energy domain, few applications of SDDP are found in the finance domain. In this study, we identify the major obstacle in using SDDP to solve financial planning problems to be the stagewise independence assumption and propose a partially observable SDDP (PO-SDDP) framework to overcome such limitations. We argue that the PO-SDDP framework, which models uncertainties using discrete-valued partially observable Markov states and introduces feasibility cuts, can properly address large-scale financial planning problems.
{"title":"Large-scale financial planning via a partially observable stochastic dual dynamic programming framework","authors":"J. Lee, Do-Gyun Kwon, Yongjae Lee, J. Kim, W. Kim","doi":"10.1080/14697688.2023.2221296","DOIUrl":"https://doi.org/10.1080/14697688.2023.2221296","url":null,"abstract":"The multi-stage stochastic programming (MSP) approach is widely used to solve financial planning problems owing to its flexibility. However, the size of an MSP problem grows exponentially with the number of stages, and such problem can easily become computationally intractable. Financial planning problems often consider planning horizons of several decades, and thus, the curse of dimensionality can become a critical issue. Stochastic dual dynamic programming (SDDP), a sampling-based decomposition algorithm, has emerged to resolve this issue. While SDDP has been successfully implemented in the energy domain, few applications of SDDP are found in the finance domain. In this study, we identify the major obstacle in using SDDP to solve financial planning problems to be the stagewise independence assumption and propose a partially observable SDDP (PO-SDDP) framework to overcome such limitations. We argue that the PO-SDDP framework, which models uncertainties using discrete-valued partially observable Markov states and introduces feasibility cuts, can properly address large-scale financial planning problems.","PeriodicalId":20747,"journal":{"name":"Quantitative Finance","volume":"2 1","pages":"1341 - 1360"},"PeriodicalIF":1.3,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88846043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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