We propose consistent nonparametric tests of conditional independence for time series data. Our methods are motivated from the difference between joint conditional cumulative distribution function (CDF) and the product of conditional CDFs. The difference is transformed into a proper conditional moment restriction (CMR), which forms the basis for our testing procedure. Our test statistics are then constructed using the integrated moment restrictions that are equivalent to the CMR. We establish the asymptotic behavior of the test statistics under the null, the alternative, and the sequence of local alternatives converging to conditional independence at the parametric rate. Our tests are implemented with the assistance of a multiplier bootstrap. Monte Carlo simulations are conducted to evaluate the finite sample performance of the proposed tests. We apply our tests to examine the predictability of equity risk premium using variance risk premium for different horizons and find that there exist various degrees of nonlinear predictability at mid-run and long-run horizons.
{"title":"Nonparametric Tests of Conditional Independence for Time Series","authors":"Xiaojun Song, Haoyu Wei","doi":"10.2139/ssrn.3939952","DOIUrl":"https://doi.org/10.2139/ssrn.3939952","url":null,"abstract":"We propose consistent nonparametric tests of conditional independence for time series data. Our methods are motivated from the difference between joint conditional cumulative distribution function (CDF) and the product of conditional CDFs. The difference is transformed into a proper conditional moment restriction (CMR), which forms the basis for our testing procedure. Our test statistics are then constructed using the integrated moment restrictions that are equivalent to the CMR. We establish the asymptotic behavior of the test statistics under the null, the alternative, and the sequence of local alternatives converging to conditional independence at the parametric rate. Our tests are implemented with the assistance of a multiplier bootstrap. Monte Carlo simulations are conducted to evaluate the finite sample performance of the proposed tests. We apply our tests to examine the predictability of equity risk premium using variance risk premium for different horizons and find that there exist various degrees of nonlinear predictability at mid-run and long-run horizons.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"126 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877621","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}
We empirically investigate the relevance of multi-homing in two-sided markets. First, we build a micro-founded structural econometric model that encompasses demand for differentiated products and allows for multi-homing on both sides of themarket. We then use an original dataset on the Italian daily newspaper market that includes information on double-homing by readers to estimate readers’ and advertisers’ demand. The results show that an econometric model that does not allow for multi-homing is likely to produce biased estimates of demand on both sides of the market. In particular, on the reader side, accounting for multi-homing helps to recognize complementarity between products; on the advertising side, it allows to measure to what extent advertising demand depends on the shares of exclusive and overlapping readers.
{"title":"Estimating Demand with Multi-Homing in Two-Sided Markets","authors":"Pauline Affeldt, Elena Argentesi, L. Filistrucchi","doi":"10.2139/ssrn.3905859","DOIUrl":"https://doi.org/10.2139/ssrn.3905859","url":null,"abstract":"We empirically investigate the relevance of multi-homing in two-sided markets. First, we build a micro-founded structural econometric model that encompasses demand for differentiated products and allows for multi-homing on both sides of themarket. We then use an original dataset on the Italian daily newspaper market that includes information on double-homing by readers to estimate readers’ and advertisers’ demand. The results show that an econometric model that does not allow for multi-homing is likely to produce biased estimates of demand on both sides of the market. In particular, on the reader side, accounting for multi-homing helps to recognize complementarity between products; on the advertising side, it allows to measure to what extent advertising demand depends on the shares of exclusive and overlapping readers.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131620473","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 most judicial institutions, well-functioning courts are usually expected to process a large volume of work within demanding timelines. For courts to have played their role of enhancing access to justice, the yardstick of success is often viewed through the lens of the speed attained in rendering justice. In Kenya, despite the desirable timeline for finalizing most of the cases being ‘within 360 days’ from the date of case filing in courts, by the end of June 2020, 58 per cent of the unresolved cases had surpassed this timeline and subsequently classified as backlog. In the period 2018/19, the percentage of civil cases that were resolved within the set timeline by High Court and Magistrate Court, the two largest court types by volume of work, was 37 and 42 per cent respectively. Over the same period, the percentage of criminal cases that were resolved within the set timeline was 42 and 84 per cent for the two court types respectively. Evidently therefore, the Kenyan courts had not managed to resolve cases within the desirable timeline. To unearth the reasons that could be occasioning the delay, this study investigated the factors that were potentially affecting court speed. Specifically, the study set out to determine the variation in court speed attributable to court type, and further analyze the effect of court size and employee satisfaction on court speed. This was achieved through the use of Hierarchical Linear Modelling, cross sectional data for the period 2018/19 and estimation using Restricted Maximum Likelihood technique. The results revealed the existence of relatively high variation in court speed that is attributable to court type, and that the smaller the court size, the higher the court speed. Further, high level of employee satisfaction was found to increase timely resolution of cases. Consequently, diverse strategies and policy actions for enhancing court speed have been suggested.
{"title":"Does Court Type, Size and Employee Satisfaction Affect Court Speed?. Hierarchical Linear Modelling With Evidence from Kenya","authors":"Moses Marang’a","doi":"10.7176/jlpg/110-02","DOIUrl":"https://doi.org/10.7176/jlpg/110-02","url":null,"abstract":"In most judicial institutions, well-functioning courts are usually expected to process a large volume of work within demanding timelines. For courts to have played their role of enhancing access to justice, the yardstick of success is often viewed through the lens of the speed attained in rendering justice. In Kenya, despite the desirable timeline for finalizing most of the cases being ‘within 360 days’ from the date of case filing in courts, by the end of June 2020, 58 per cent of the unresolved cases had surpassed this timeline and subsequently classified as backlog. In the period 2018/19, the percentage of civil cases that were resolved within the set timeline by High Court and Magistrate Court, the two largest court types by volume of work, was 37 and 42 per cent respectively. Over the same period, the percentage of criminal cases that were resolved within the set timeline was 42 and 84 per cent for the two court types respectively. Evidently therefore, the Kenyan courts had not managed to resolve cases within the desirable timeline. To unearth the reasons that could be occasioning the delay, this study investigated the factors that were potentially affecting court speed. Specifically, the study set out to determine the variation in court speed attributable to court type, and further analyze the effect of court size and employee satisfaction on court speed. This was achieved through the use of Hierarchical Linear Modelling, cross sectional data for the period 2018/19 and estimation using Restricted Maximum Likelihood technique. The results revealed the existence of relatively high variation in court speed that is attributable to court type, and that the smaller the court size, the higher the court speed. Further, high level of employee satisfaction was found to increase timely resolution of cases. Consequently, diverse strategies and policy actions for enhancing court speed have been suggested.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126308064","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 : 2021-04-30DOI: 10.15587/1729-4061.2021.229160
Іgor Romanenko, A. Golovanov, V. Khoma, A. Shyshatskyi, Y. Demchenko, L. Shabanova-Kushnarenko, Tetiana Ivakhnenko, O. Prokopenko, Oleh Havaliukh, Dmitrо Stupak
The method of estimation and forecasting in intelligent decision support systems is developed. The essence of the proposed method is the ability to analyze the current state of the object under analysis and the possibility of short-term forecasting of the object state. The possibility of objective and complete analysis is achieved through the use of improved fuzzy temporal models of the object state, an improved procedure for forecasting the object state and an improved procedure for training evolving artificial neural networks. The concepts of a fuzzy cognitive model, in contrast to the known fuzzy cognitive models, are connected by subsets of fuzzy influence degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. This method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of this method is the ability to take into account the type of a priori uncertainty about the state of the analyzed object (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The ability to clarify information about the state of the monitored object is achieved through the use of an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration and indirect influence of all components of a multidimensional time series with different time shifts relative to each other under uncertainty.
{"title":"Development of Estimation and Forecasting Method in Intelligent Decision Support Systems","authors":"Іgor Romanenko, A. Golovanov, V. Khoma, A. Shyshatskyi, Y. Demchenko, L. Shabanova-Kushnarenko, Tetiana Ivakhnenko, O. Prokopenko, Oleh Havaliukh, Dmitrо Stupak","doi":"10.15587/1729-4061.2021.229160","DOIUrl":"https://doi.org/10.15587/1729-4061.2021.229160","url":null,"abstract":"The method of estimation and forecasting in intelligent decision support systems is developed. The essence of the proposed method is the ability to analyze the current state of the object under analysis and the possibility of short-term forecasting of the object state. The possibility of objective and complete analysis is achieved through the use of improved fuzzy temporal models of the object state, an improved procedure for forecasting the object state and an improved procedure for training evolving artificial neural networks. The concepts of a fuzzy cognitive model, in contrast to the known fuzzy cognitive models, are connected by subsets of fuzzy influence degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. This method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of this method is the ability to take into account the type of a priori uncertainty about the state of the analyzed object (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The ability to clarify information about the state of the monitored object is achieved through the use of an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration and indirect influence of all components of a multidimensional time series with different time shifts relative to each other under uncertainty.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128023623","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}
We develop a network-based vector autoregressive approach to uncover the interactions among financial assets by integrating multiple realized measures based on high-frequency data. Under a restricted parameter structure, our approach allows the capture of cross-sectional and time ependencies embedded in a large panel of assets through the decomposition of these two blocks of dependencies. We propose a block coordinate descent (BCD) procedure for the least square estimation and investigate its theoretical properties. By integrating realized returns, realized volume, and realized volatilities of 1095 individual U.S. stocks over fifteen years, we illustrate that our approach identifies a large array of interdependencies with a limited computational effort. As a direct consequence of the estimated model, we provide a new ranking for the systemically important financial institutions (SIFIs) and carry out an impulse-response analysis to quantify the effects of adverse shocks on the financial system.
{"title":"Estimating Financial Networks by Realized Interdependencies: A Restricted Autoregressive Approach","authors":"M. Caporin, Deniz Erdemlioglu, Stefano Nasini","doi":"10.2139/ssrn.3821566","DOIUrl":"https://doi.org/10.2139/ssrn.3821566","url":null,"abstract":"We develop a network-based vector autoregressive approach to uncover the interactions among<br>financial assets by integrating multiple realized measures based on high-frequency data. Under<br>a restricted parameter structure, our approach allows the capture of cross-sectional and time ependencies embedded in a large panel of assets through the decomposition of these two blocks of<br>dependencies. We propose a block coordinate descent (BCD) procedure for the least square estimation and investigate its theoretical properties. By integrating realized returns, realized volume, and realized volatilities of 1095 individual U.S. stocks over fifteen years, we illustrate that our approach identifies a large array of interdependencies with a limited computational effort. As a direct consequence of the estimated model, we provide a new ranking for the systemically important financial institutions (SIFIs) and carry out an impulse-response analysis to quantify the effects of adverse shocks on the financial system.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122484366","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}
For many accounting research questions, empirical researchers cannot randomly assign observations to treatment conditions or identify a quasi-experimental setting. In these cases, entropy balancing (Hainmueller 2012) is an increasingly popular statistical method for identifying a control sample that is nearly identical to the treated sample with respect to observable covariates. In this paper, we compare entropy balancing’s approach of reweighting control sample observations to ordinary least squares and propensity score matching. We demonstrate that researchers applying entropy balancing in empirical settings involving panel data with features common in accounting research may encounter implementation issues that render the resulting estimates sensitive to relatively minor changes in the control sample or the research design. Using the setting of estimating the Big-N audit fee premium, we empirically demonstrate these issues and propose solutions.
{"title":"When Good Balance Goes Bad: a Discussion of Common Pitfalls When Using Entropy Balancing","authors":"Jeff L. McMullin, B. Schonberger","doi":"10.2139/ssrn.3786224","DOIUrl":"https://doi.org/10.2139/ssrn.3786224","url":null,"abstract":"For many accounting research questions, empirical researchers cannot randomly assign observations to treatment conditions or identify a quasi-experimental setting. In these cases, entropy balancing (Hainmueller 2012) is an increasingly popular statistical method for identifying a control sample that is nearly identical to the treated sample with respect to observable covariates. In this paper, we compare entropy balancing’s approach of reweighting control sample observations to ordinary least squares and propensity score matching. We demonstrate that researchers applying entropy balancing in empirical settings involving panel data with features common in accounting research may encounter implementation issues that render the resulting estimates sensitive to relatively minor changes in the control sample or the research design. Using the setting of estimating the Big-N audit fee premium, we empirically demonstrate these issues and propose solutions.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123222124","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}
This article provides an overview and guide to implementing heteroskedaticity-based instrumental variables (HBIV) in regression models with endogeneity, i.e., one or more of the regressors are correlated with the disturbance term. We discuss the problem of implementing standard instrumental variables (IV) solutions to the endogeneity problem when external instruments are either insufficient or not readily available, and when the disturbances are heteroskedastic, present a solution to the problem. We illustrate the implementation of HBIV in the presence of strong external instruments, weak external instruments, and no external instruments, using both traditional IV and HBIV. Finally, we discuss the pros and cons of using HBIV methods to address endogeneity.
{"title":"Addressing Endogeneity Without Strong Instruments: A Practical Guide to Heteroskedasticity-Based Instrumental Variables (HBIV)","authors":"Bernardo F. Quiroga","doi":"10.2139/ssrn.3293789","DOIUrl":"https://doi.org/10.2139/ssrn.3293789","url":null,"abstract":"This article provides an overview and guide to implementing heteroskedaticity-based instrumental variables (HBIV) in regression models with endogeneity, i.e., one or more of the regressors are correlated with the disturbance term. We discuss the problem of implementing standard instrumental variables (IV) solutions to the endogeneity problem when external instruments are either insufficient or not readily available, and when the disturbances are heteroskedastic, present a solution to the problem. We illustrate the implementation of HBIV in the presence of strong external instruments, weak external instruments, and no external instruments, using both traditional IV and HBIV. Finally, we discuss the pros and cons of using HBIV methods to address endogeneity.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114673462","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}
Prediction is a forecast of an event which may happen in future. Predictions are not necessary based upon the prior knowledge or experience for an event of interest. Every person does predictions but the quality of the predictions differs from person to person and that classifies them as a successful or unsuccessful person. In order to make quality predictions it is necessary to automate the making prediction process. Machine Learning is a field where in computer machines are trained to make accurate predictions. Some of the applications of machine learning predictions are weather forecasting, disease detection, traffic prediction, email and malware detection, fraud detection. Prediction of employability for a candidate in a recruitment process is been calculated by using machine learning. Organizations are now investing in machine learning based automated systems for identifying a right skilled candidate. This research introduces a model buildout to predict the employability of a candidate by using Logistic Regression. A group of aspirants were tested in the suggested model and outcome are analyzed in this research paper.
{"title":"Employment Prediction Using Logistic Regression Algorithm","authors":"A. Dasgupta, D. Ghosh, Jatin Vyas","doi":"10.2139/ssrn.3755922","DOIUrl":"https://doi.org/10.2139/ssrn.3755922","url":null,"abstract":"Prediction is a forecast of an event which may happen in future. Predictions are not necessary based upon the prior knowledge or experience for an event of interest. Every person does predictions but the quality of the predictions differs from person to person and that classifies them as a successful or unsuccessful person. In order to make quality predictions it is necessary to automate the making prediction process. Machine Learning is a field where in computer machines are trained to make accurate predictions. Some of the applications of machine learning predictions are weather forecasting, disease detection, traffic prediction, email and malware detection, fraud detection. Prediction of employability for a candidate in a recruitment process is been calculated by using machine learning. Organizations are now investing in machine learning based automated systems for identifying a right skilled candidate. This research introduces a model buildout to predict the employability of a candidate by using Logistic Regression. A group of aspirants were tested in the suggested model and outcome are analyzed in this research paper.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116214677","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}
Since Bai (2009, Econometrica 77, 1229–1279), considerable extensions have been made to panel data models with interactive fixed effects (IFEs). However, little work has been conducted to understand the associated iterative algorithm, which, to the best of our knowledge, is the most commonly adopted approach in this line of research. In this paper, we refine the algorithm of panel data models with IFEs using the nuclear-norm penalization method and duple least-squares (DLS) iterations. Meanwhile, we allow the regression coefficients to be individual-specific and evolve over time. Accordingly, asymptotic properties are established to demonstrate the theoretical validity of the proposed approach. Furthermore, we show that the proposed methodology exhibits good finite-sample performance using simulation and real data examples.
{"title":"Nonparametric Time-Varying Panel Data Models with Heterogeneity","authors":"Fei Liu","doi":"10.2139/ssrn.3743529","DOIUrl":"https://doi.org/10.2139/ssrn.3743529","url":null,"abstract":"\u0000 Since Bai (2009, Econometrica 77, 1229–1279), considerable extensions have been made to panel data models with interactive fixed effects (IFEs). However, little work has been conducted to understand the associated iterative algorithm, which, to the best of our knowledge, is the most commonly adopted approach in this line of research. In this paper, we refine the algorithm of panel data models with IFEs using the nuclear-norm penalization method and duple least-squares (DLS) iterations. Meanwhile, we allow the regression coefficients to be individual-specific and evolve over time. Accordingly, asymptotic properties are established to demonstrate the theoretical validity of the proposed approach. Furthermore, we show that the proposed methodology exhibits good finite-sample performance using simulation and real data examples.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131577634","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}
We develop a stochastic calculus that makes it easy to capture a variety of predictable transformations of semimartingales such as changes of variables, stochastic integrals, and their compositions. The framework offers a unified treatment of real-valued and complex-valued semimartingales. The proposed calculus is a blueprint for the derivation of new relationships among stochastic processes with specific examples provided below.
{"title":"Simplified Stochastic Calculus via Semimartingale Representations","authors":"A. Černý, J. Ruf","doi":"10.2139/ssrn.3633638","DOIUrl":"https://doi.org/10.2139/ssrn.3633638","url":null,"abstract":"We develop a stochastic calculus that makes it easy to capture a variety of predictable transformations of semimartingales such as changes of variables, stochastic integrals, and their compositions. The framework offers a unified treatment of real-valued and complex-valued semimartingales. The proposed calculus is a blueprint for the derivation of new relationships among stochastic processes with specific examples provided below.","PeriodicalId":273058,"journal":{"name":"ERN: Model Construction & Estimation (Topic)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116784786","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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