scholarly journals Piecewise Constant Modeling and Tracking of Systematic Risk in Financial Market

2021 ◽  
Author(s):  
Triloke Rajbhandary
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Capital Market ◽  
Significant Risk ◽  
Systematic Risk ◽  
Efficient Market Hypothesis ◽  
Statistical Hypothesis ◽  
Time Varying ◽  
Statistical Hypothesis Testing ◽  
Piecewise Constant

The objective of this thesis is to study the time-varying systematic risk in capital market represented by beta. By using statistical hypothesis testing, we show that beta changes in a piecewise constant pattern in which the changes are governed by triggering economic events. This pattern of beta is different from previously modeled time-varying patterns in literature, such as random walk and mean-reverting models and is consistent with the efficient market hypothesis. We also present a new modeling technique based on Poisson process to represent piecewise constant beta. We develop a new tracking algorithm based on Kalman Filter in which Bayes' selection criteria is incorporated to track piecewise constant beta. Our simulation results show that our proposed tracking method outperforms the traditional random walk and mean reverting model based Kalman Filter tracking. Our empirical case studies also show that our method is efficient in capturing the significant risk changes which are attributed to economic events.

scholarly journals Piecewise Constant Modeling and Tracking of Systematic Risk in Financial Market

2021 ◽  
Author(s):  
Triloke Rajbhandary
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Capital Market ◽  
Significant Risk ◽  
Systematic Risk ◽  
Efficient Market Hypothesis ◽  
Statistical Hypothesis ◽  
Time Varying ◽  
Statistical Hypothesis Testing ◽  
Piecewise Constant

The objective of this thesis is to study the time-varying systematic risk in capital market represented by beta. By using statistical hypothesis testing, we show that beta changes in a piecewise constant pattern in which the changes are governed by triggering economic events. This pattern of beta is different from previously modeled time-varying patterns in literature, such as random walk and mean-reverting models and is consistent with the efficient market hypothesis. We also present a new modeling technique based on Poisson process to represent piecewise constant beta. We develop a new tracking algorithm based on Kalman Filter in which Bayes' selection criteria is incorporated to track piecewise constant beta. Our simulation results show that our proposed tracking method outperforms the traditional random walk and mean reverting model based Kalman Filter tracking. Our empirical case studies also show that our method is efficient in capturing the significant risk changes which are attributed to economic events.

scholarly journals Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

2021 ◽  
Author(s):  
Luan Vo
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Random Walk ◽  
Systematic Risk ◽  
Time Varying ◽  
Random Coefficient Models ◽  
Piecewise Constant ◽  
Kalman Filter Algorithm ◽  
Empirical Tests ◽  
Piecewise Constant Model

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

scholarly journals Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

2021 ◽  
Author(s):  
Luan Vo
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Random Walk ◽  
Systematic Risk ◽  
Time Varying ◽  
Random Coefficient Models ◽  
Piecewise Constant ◽  
Kalman Filter Algorithm ◽  
Empirical Tests ◽  
Piecewise Constant Model

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

The Time-Varying Systematic Risk of Carry Trade Strategies

CFA Digest ◽  
2012 ◽  
Vol 42 (1) ◽  
pp. 49-51
Author(s):  
Andrew Boral
Keyword(s):  
Systematic Risk ◽  
Time Varying ◽  
Carry Trade

scholarly journals Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lukas Vlcek ◽  
Shize Yang ◽  
Yongji Gong ◽  
Pulickel Ajayan ◽  
Wu Zhou ◽  
...  
Keyword(s):  
Effective Interaction ◽  
Mean Field ◽  
Interaction Model ◽  
Optimization Techniques ◽  
Statistical Hypothesis ◽  
Structure Property ◽  
Statistical Hypothesis Testing ◽  
Stochastic Representation ◽  
Scanning Transmission ◽  
Complex Structural

AbstractExploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of MoxRe1-xS2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

scholarly journals Cutaneous Squamous Cell Carcinoma in Patients with Hidradenitis Suppurativa

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1153
Author(s):  
Elysia Racanelli ◽  
Abdulhadi Jfri ◽  
Amnah Gefri ◽  
Elizabeth O’Brien ◽  
Ivan Litvinov ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Hidradenitis Suppurativa ◽  
Case Reports ◽  
Rare Complication ◽  
Cox Proportional Hazards ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing

Background: Cutaneous squamous cell carcinoma (cSCC) is a rare complication of hidradenitis suppurativa (HS). Objectives: To conduct a systematic review and an individual patient data (IPD) meta-analysis to describe the clinical characteristics of HS patients developing cSCC and determine predictors of poor outcome. Methods: Medline/PubMed, Embase, and Web of Science were searched for studies reporting cSCC arising in patients with HS from inception to December 2019. A routine descriptive analysis, statistical hypothesis testing, and Kaplan–Meier survival curves/Cox proportional hazards regression models were performed. Results: A total of 34 case reports and series including 138 patients were included in the study. The majority of patients were males (81.6%), White (83.3%), and smokers (n = 22/27 reported) with a mean age of 53.5 years. Most patients had gluteal (87.8%), Hurley stage 3 HS (88.6%). The mean time from the diagnosis of HS to the development of cSCC was 24.7 years. Human papillomavirus was identified in 12/38 patients tested. Almost 50% of individuals had nodal metastasis and 31.3% had distant metastases. Half of the patients succumbed to their disease. Conclusions: cSCC is a rare but life-threatening complication seen in HS patients, mainly occurring in White males who are smokers with severe, long-standing gluteal HS. Regular clinical examination and biopsy of any suspicious lesions in high-risk patients should be considered. The use of HPV vaccination as a preventive and possibly curative method needs to be explored.

scholarly journals Time-varying cointegration and the Kalman filter

2021 ◽  
pp. 1-21
Author(s):  
Burak Alparslan Ero˜glu ◽  
J. Isaac Miller ◽  
Taner Yi˜git
Keyword(s):  
Kalman Filter ◽  
Time Varying

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

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