Search Results - subject:"Kernel"

Hierarchical time-varying estimation of asset pricing models

Baillie, Richard; Calonaci, Fabio; Kapetanios, George - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-26

approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the …

Persistent link: https://www.econbiz.de/10012813375

Conflict prediction using kernel density estimation

Tapsoba, Augustin - 2022

Persistent link: https://www.econbiz.de/10012813801

Kernel regression coefficients for practical significance

Vinod, Hrishikesh D. - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-13

suggest reporting estimates using nonlinear kernel regressions and the standardization of all variables to avoid p-hacking. We … are filling an essential gap in the literature because p-hacking-related papers do not even mention kernel regressions or … cross-section of firms and financial management in macroeconomic time series. We estimate nonlinear, nonparametric kernel …

Persistent link: https://www.econbiz.de/10012814147

Machine-learning-based semiparametric time series conditional variance : estimation and forecasting

Dang, Justin; Ullah, Aman - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-12

parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm …, known as the kernel-regularized least squares estimator, is used to estimate the nonparametric component. We discuss how to …

Persistent link: https://www.econbiz.de/10012814196

When is TSLS Actually LATE?

Blandhol, Christine; Bonney, John; Mogstad, Magne; … - National Bureau of Economic Research - 2022

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types...

Persistent link: https://www.econbiz.de/10012814484

Volatility and the pricing kernel

Schreindorfer, David; Sichert, Tobias - 2022 - This draft: January 31, 2022

Persistent link: https://www.econbiz.de/10012816005

From inexact optimization to learning via gradient concentration

Stankewitz, Bernhard; Mücke, Nicole; Rosasco, Lorenzo - In: Computational Optimization and Applications 84 (2022) 1, pp. 265-294

Optimization in machine learning typically deals with the minimization of empirical objectives defined by training data. The ultimate goal of learning, however, is to minimize the error on future data (test error), for which the training data provides only partial information. In this view, the...

Persistent link: https://www.econbiz.de/10015327608

When is TSLS Actually LATE?

Blandhol, Christine; Bonney, John; Mogstad, Magne; … - 2022

Persistent link: https://www.econbiz.de/10013306072

A generalized entropy approach to portfolio selection under a hidden markov model

MacLean, Leonard C.; Yu, Lijun; Zhao, Yonggan - In: Journal of risk and financial management : JRFM 15 (2022) 8, pp. 1-25

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk...

Persistent link: https://www.econbiz.de/10013375264

Where is the consumer centre? : a case of St. Petersburg

Kholodilin, Konstantin; Koroleva, Irina; Kryutchenko, Darya - In: Regional science policy and practice : RSPP 14 (2022) 4, pp. 916-938

Persistent link: https://www.econbiz.de/10013401854