A <scp>GARCH</scp> approach to model short‐term interest rates: Evidence from Spanish economy

García, Javier Sánchez; Rambaud, Salvador Cruz

doi:10.1002/ijfe.2234

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…, kt ) is the vector of errors, such that t ∼ N (0, Σ ), where Σ = E( t t ) is the variance-covariance matrix. These errors may include a conditional heteroskedastic process to capture volatilities, not only in high frequency financial frameworks but also in macrofinancial settings [26].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs

García

Rambaud

2022

Mathematics

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Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs

García

Rambaud

2022

Mathematics

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“…Firstly, by virtue of the generalized approximate cross validation method, we have 16 parameter combinations by the combination of λ = [100, 500, 1000, 1500] and σ 2 K = [1, 5,10,15]. The optimal combination of parameters is selected by minimizing GACV λ, σ 2 K .…”

Section: Svqr Estimation and Analysis Of Banks' Jump Diffusion Varmentioning

confidence: 99%

“…At present, the commonly used methods to measure and estimate volatility are historical volatility, calculated by static average and dynamic moving average; time-varying volatility, estimated by the continuous generalized autoregressive conditional heteroscedasticity (GARCH) model [5]; implied volatility, calculated by six variable option pricing system; random volatility, generated by stochastic simulation; and realized volatility, calculated by high-frequency data. These methods all assume that the return on assets is a continuous process, and the jump component is not fully considered or is completely ignored.…”

Section: Introductionmentioning

confidence: 99%

Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk

et al. 2020

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Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression.

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Volatility spillovers between oil and financial markets during economic and financial crises: A dynamic approach

García

Rambaud

2023

J Econ Finan

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Volatility in international oil markets is a recurrent phenomenon which causes spillovers on financial markets. However, a bidirectional comparison between the dynamic behavior of economic and financial crises has not been documented yet. This paper addresses this research question by employing a combined GARCH-VAR-Spillover Index methodology to measure the volatility of the WTI and the Brent in order to analyze its relationship with the SP500, NASDAQ, DAX and the IBEX for a sample corresponding to the period [2000:01, 2021:04]. Specifically, this paper compares the dynamic behavior of spillovers during the 2007-2009 financial crisis (taken as the financial crisis) and the COVID-19 pandemic (taken as the economic crisis). It has been found that there exists statistically significant net volatility shocks and spillovers from oil to stocks markets in the majority of cases and periods. These spillovers are bidirectional, and the net relationship is reversed depending on the market and the time period. The dynamic behavior of such spillovers depends on the region, being the USA more reactive to the source of the crisis than Europe. In particular, in the USA when the crisis is originated by financial shocks, financial markets are net transmitters of volatility to oil markets. When the source of the shock is economic, oil markets are net transmitters of volatility to stocks markets. This is not the case for Europe, where financial markets seem to be either transmitters or receivers of volatility no matter the source of the shock. In general, our study offers new evidence to understand macro-financial linkages.

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A GARCH approach to model short‐term interest rates: Evidence from Spanish economy

Cited by 5 publications

References 32 publications

Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs

Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs

Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk

Volatility spillovers between oil and financial markets during economic and financial crises: A dynamic approach

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