Pub Date : 2023-11-01DOI: 10.1142/s0219024923500206
Robert A. Jarrow, Rinald Murataj, Martin T. Wells, Liao Zhu
{"title":"The Low-volatility Anomaly and the Adaptive Multi-Factor Model","authors":"Robert A. Jarrow, Rinald Murataj, Martin T. Wells, Liao Zhu","doi":"10.1142/s0219024923500206","DOIUrl":"https://doi.org/10.1142/s0219024923500206","url":null,"abstract":"","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"80 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135221255","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 : 2023-10-20DOI: 10.1142/s0219024923500188
Lukas Mueller, Dirk Schiereck, Marc Ringel
{"title":"Is decarbonization priced in? - Evidence on the carbon risk hypothesis from the European Green Deal leakage shock","authors":"Lukas Mueller, Dirk Schiereck, Marc Ringel","doi":"10.1142/s0219024923500188","DOIUrl":"https://doi.org/10.1142/s0219024923500188","url":null,"abstract":"","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567743","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 : 2023-10-20DOI: 10.1142/s0219024923500139
Martin Keller-Ressel, Felix Sachse
In this paper, we analyze the shapes of forward curves and yield curves that can be attained in the two-factor Vasicek model. We show how to partition the state space of the model, such that each partition is associated to a particular shape (normal, inverse, humped, etc.). The partitions and the corresponding shapes are determined by the winding number of a single curve with possible singularities and self-intersections, which can be constructed as the envelope of a family of lines. Building on these results, we classify possible transitions between term structure shapes, give results on attainability of shapes conditional on the level of the short rate, and propose a simple method to determine the relative frequency of different shapes of the forward curve and the yield curve.
{"title":"State Space Decomposition and Classification of Term Structure Shapes in the Two-Factor Vasicek Model","authors":"Martin Keller-Ressel, Felix Sachse","doi":"10.1142/s0219024923500139","DOIUrl":"https://doi.org/10.1142/s0219024923500139","url":null,"abstract":"In this paper, we analyze the shapes of forward curves and yield curves that can be attained in the two-factor Vasicek model. We show how to partition the state space of the model, such that each partition is associated to a particular shape (normal, inverse, humped, etc.). The partitions and the corresponding shapes are determined by the winding number of a single curve with possible singularities and self-intersections, which can be constructed as the envelope of a family of lines. Building on these results, we classify possible transitions between term structure shapes, give results on attainability of shapes conditional on the level of the short rate, and propose a simple method to determine the relative frequency of different shapes of the forward curve and the yield curve.","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"92 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513522","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}
In the Markowitz mean–variance portfolio optimization problem, the estimation of the inverse covariance matrix is not trivial and can even be intractable, especially when the dimension is very high. In this paper, we propose a linear-programming portfolio optimizer (LPO) to solve the Markowitz optimization problem in both low-dimensional and high-dimensional settings. Instead of directly estimating the inverse covariance matrix [Formula: see text], the LPO method estimates the portfolio weights [Formula: see text] through solving an [Formula: see text]-constrained optimization problem. Moreover, we further prove that the LPO estimator asymptotically yields the maximum expected return while preserving the risk constraint. To offer a practical insight into the LPO approach, we provide a comprehensive implementation procedure of estimating portfolio weights via the Dantzig selector with sequential optimization (DASSO) algorithm and selecting the sparsity parameter through cross-validation. Simulations on both synthetic data and empirical data from Fama–French and the Center for Research in Security Prices (CRSP) databases validate the performance of the proposed method in comparison with other existing proposals.
{"title":"A LINEAR-PROGRAMMING PORTFOLIO OPTIMIZER TO MEAN–VARIANCE OPTIMIZATION","authors":"Xiaoyue Liu, Zhenzhong Huang, Biwei Song, Zhen Zhang","doi":"10.1142/s0219024923500127","DOIUrl":"https://doi.org/10.1142/s0219024923500127","url":null,"abstract":"In the Markowitz mean–variance portfolio optimization problem, the estimation of the inverse covariance matrix is not trivial and can even be intractable, especially when the dimension is very high. In this paper, we propose a linear-programming portfolio optimizer (LPO) to solve the Markowitz optimization problem in both low-dimensional and high-dimensional settings. Instead of directly estimating the inverse covariance matrix [Formula: see text], the LPO method estimates the portfolio weights [Formula: see text] through solving an [Formula: see text]-constrained optimization problem. Moreover, we further prove that the LPO estimator asymptotically yields the maximum expected return while preserving the risk constraint. To offer a practical insight into the LPO approach, we provide a comprehensive implementation procedure of estimating portfolio weights via the Dantzig selector with sequential optimization (DASSO) algorithm and selecting the sparsity parameter through cross-validation. Simulations on both synthetic data and empirical data from Fama–French and the Center for Research in Security Prices (CRSP) databases validate the performance of the proposed method in comparison with other existing proposals.","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302905","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 : 2023-09-29DOI: 10.1142/s0219024923500152
Markus Hess
{"title":"VIX Modeling for a Market Insider","authors":"Markus Hess","doi":"10.1142/s0219024923500152","DOIUrl":"https://doi.org/10.1142/s0219024923500152","url":null,"abstract":"","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135132711","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 : 2023-07-04DOI: 10.1142/s0219024923500115
P. Forsyth, Pieter M. van Staden, Yuying Li
1 We determine a simple dynamic benchmark for asset allocation by solving an optimal stochas- 2 tic control problem for outperforming the traditional constant proportion benchmark. An ob- 3 jective function based on a time averaged quadratic deviation from an elevated benchmark is 4 proposed. We argue that this objective function combines the best features of tracking error and 5 tracking difference. Assuming parametric models of the stock and bond processes, a closed form 6 solution for the optimal control is obtained. The closed form optimal control is then clipped to 7 prevent use of excessive leverage, and to prevent trading if insolvent. Monte Carlo computations 8 using this clipped control are presented which show that for modest levels of outperformance 9 (i.e. 80-170 bps per year), this easily implementable strategy outperforms the traditional con- 10 stant proportion benchmark with high probability. We advocate this clipped optimal strategy 11 as a suitable benchmark for active asset allocation. 12
{"title":"Beating a constant weight benchmark: easier done than said","authors":"P. Forsyth, Pieter M. van Staden, Yuying Li","doi":"10.1142/s0219024923500115","DOIUrl":"https://doi.org/10.1142/s0219024923500115","url":null,"abstract":"1 We determine a simple dynamic benchmark for asset allocation by solving an optimal stochas- 2 tic control problem for outperforming the traditional constant proportion benchmark. An ob- 3 jective function based on a time averaged quadratic deviation from an elevated benchmark is 4 proposed. We argue that this objective function combines the best features of tracking error and 5 tracking difference. Assuming parametric models of the stock and bond processes, a closed form 6 solution for the optimal control is obtained. The closed form optimal control is then clipped to 7 prevent use of excessive leverage, and to prevent trading if insolvent. Monte Carlo computations 8 using this clipped control are presented which show that for modest levels of outperformance 9 (i.e. 80-170 bps per year), this easily implementable strategy outperforms the traditional con- 10 stant proportion benchmark with high probability. We advocate this clipped optimal strategy 11 as a suitable benchmark for active asset allocation. 12","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45328186","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 : 2023-05-19DOI: 10.1142/s0219024923500073
M. Forde, Benjamin S. Smith
{"title":"Markovian stochastic volatility with stochastic correlation : joint calibration and consistency of SPX/VIX short-maturity smiles","authors":"M. Forde, Benjamin S. Smith","doi":"10.1142/s0219024923500073","DOIUrl":"https://doi.org/10.1142/s0219024923500073","url":null,"abstract":"","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43785612","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 : 2023-02-18DOI: 10.1142/s021902492350005x
D. Pirjol
{"title":"Subleading Correction to the Asian Options Volatility in the Black-Scholes Model","authors":"D. Pirjol","doi":"10.1142/s021902492350005x","DOIUrl":"https://doi.org/10.1142/s021902492350005x","url":null,"abstract":"","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47401951","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 : 2023-02-10DOI: 10.1142/s0219024923500048
Jae-Yun Jun, Yves Rakotondratsimba
{"title":"Approximating option prices under large changes of underlying asset prices","authors":"Jae-Yun Jun, Yves Rakotondratsimba","doi":"10.1142/s0219024923500048","DOIUrl":"https://doi.org/10.1142/s0219024923500048","url":null,"abstract":"","PeriodicalId":47022,"journal":{"name":"International Journal of Theoretical and Applied Finance","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136092258","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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