vincenzocandila.r-universe.dev

[vincenzocandila] rumidas 0.1.3

vcandila@unisa.it (Vincenzo Candila) — Tue, 18 Mar 2025 13:40:15 GMT

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) ) components to a variety of GARCH and MEM (Engle (2002) , Engle and Gallo (2006) , and Amendola et al. (2024) ) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

[vincenzocandila] welo 0.1.4

vcandila@unisa.it (Vincenzo Candila) — Wed, 20 Mar 2024 02:32:37 GMT

Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 ) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.

[vincenzocandila] dccmidas 0.1.2

vcandila@unisa.it (Vincenzo Candila) — Thu, 22 Feb 2024 02:29:50 GMT

Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) , the DCC-MIDAS of Colacito et al. (2011) , the Asymmetric DCC of Cappiello et al. , and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) . 'dccmidas' offers the possibility of including standard GARCH , GARCH-MIDAS and Double Asymmetric GARCH-MIDAS models in the univariate estimation. Moreover, also the scalar and diagonal BEKK models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.