The model confidence set package for R

Bernardi, Mauro; Catania, Leopoldo

doi:10.1504/ijcee.2018.091037

Cited by 50 publications

(29 citation statements)

References 42 publications

(55 reference statements)

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“…In particular, we refer to the algorithmic implementation byBernardi and Catania (2018) included in the MCS R library. By default, the MSC procedure is undertaken with a value of α = 0.15 but this quantity can be changed to run a sensitivity analysis.…”

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confidence: 99%

Forecasting recovery rates on non-performing loans with machine learning

Bellotti

Brigo

Gambetti

et al. 2021

International Journal of Forecasting

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We compare the performances of a wide set of regression techniques and machine learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees and random forests perform significantly better than other approaches. In addition to loan contract specificities, the predictors referring to the bank recovery process-prior to the portfolio's sale to the debt collector-are also proven to strongly enhance forecasting performances. These variables, derived from the time-series of contacts to defaulted clients and clients' reimbursements to the bank, help all algorithms to better identify debtors with different repayment ability and/or commitment, and in general with different recovery potential.

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confidence: 99%

Forecasting recovery rates on non-performing loans with machine learning

Bellotti

Brigo

Gambetti

et al. 2021

International Journal of Forecasting

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“…After poorly performing models have been eliminated, the number of models in SSM is smaller than or equal to the original model set. According to and Bernardi and Catania (2018), the best scenario is that SSM consists of only a single model. Furthermore, the EPA statistics are constructed and the null hypothesis is tested based on a user-specified loss function.…”

Section: Methodsmentioning

confidence: 99%

“…Following and Bernardi and Catania (2018), we set the confidence level at 90% and carry out 5,000 bootstrap replications to obtain the range statistic of MCS test (TR) and Tmax statistic. We adopt R as the software to carry out all computations in this research.…”

Section: Methodsmentioning

confidence: 99%

An empirical study on the role of trading volume and data frequency in volatility forecasting

Liu

Choo

2020

Journal of Forecasting

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This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed-Data Sampling (GARCH-MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and(3) high-frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH-MIDAS in volatility forecasting.

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“…Even though the MCS procedure does not require a natural benchmark model as in the case of multiple comparison procedure with controls, the procedure can still rank the models in order of superiority after selecting the superior set of models. One main advantage of this procedure is that after a set of superior models have been selected, the models can be aggregated and used to forecast future volatility levels, predict future levels of observations, conditioned on past information [11] or forecast value at risk levels [12].…”

Section: E Model Comparison Toolmentioning

confidence: 99%

The Use of Mutual Information to Improve Value-at-Risk Forecasts for Exchange Rates

2020

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In this paper, we show a simple but novel approach in an attempt to improve value-at-risk forecasts. We use mutually dependent covariate returns to create exogenous break variables and jointly use the variables to augment GARCH models to account for time-variations and breaks in the unconditional volatility processes simultaneously. A study of hypothetical mutual dependencies between volatility and the covariates is first carried out to investigate the levels of the shared mutual information among the variables before using the augmented models to forecast 1% and 5% value-at-risks. The results provide evidence of some substantial exchange of information between volatility and the lagged exogenous covariates. In addition, the results show that the estimated augmented models have lower volatility persistence, reduced information leakages, and improved explanatory powers. Furthermore, there is evidence that our approach leads to fewer violations, improved 1% value-at-risk forecasts, and optimal daily capital requirements for all the models. There is, however evidence of relative superiority of the majority of the models for the 5% value-at-risks forecasts from our approach, although they have relatively higher failure rates. Based on these results, we recommend the incorporation of our approach to existing risk modeling frameworks. It is believed that such models may lead to fewer bank failures, expose banks to optimal market risks, and assist them in computing optimal regulatory capital requirements and minimize penalties from regulators. INDEX TERMS Exogenous break, mutual information, value-at-risk, volatility.

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The model confidence set package for R

Cited by 50 publications

References 42 publications

Forecasting recovery rates on non-performing loans with machine learning

Forecasting recovery rates on non-performing loans with machine learning

An empirical study on the role of trading volume and data frequency in volatility forecasting

The Use of Mutual Information to Improve Value-at-Risk Forecasts for Exchange Rates

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