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Mostrando postagens com marcador Previsão econômica. Mostrar todas as postagens
Mostrando postagens com marcador Previsão econômica. Mostrar todas as postagens

19 abril 2019

Previsão: sem aprender com o erro



Yes, the market is expecting rate cuts (forward rate) but the market has been exactly wrong about everything for 10 years (and longer) first forecasting the recovery that never came, then forecasting much slower interest rate rises than actually happened. Survey expectations seem to match the forward curves well except perhaps at the very end.

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16 novembro 2016

Previsão de volatilidade via machine learning

Resumo:

The support vector regression (SVR) is a supervised machine learning technique that has been successfully employed to forecast financial volatility. As the SVR is a kernel-based technique, the choice of the kernel has a great impact on its forecasting accuracy. Empirical results show that SVRs with hybrid kernels tend to beat single-kernel models in terms of forecasting accuracy. Nevertheless, no application of hybrid kernel SVR to financial volatility forecasting has been performed in previous researches. Given that the empirical evidence shows that the stock market oscillates between several possible regimes, in which the overall distribution of returns it is a mixture of normals, we attempt to find the optimal number of mixture of Gaussian kernels that improve the one-period-ahead volatility forecasting of SVR based on GARCH(1,1). The forecast performance of a mixture of one, two, three and four Gaussian kernels are evaluated on the daily returns of Nikkei and Ibovespa indexes and compared with SVR–GARCH with Morlet wavelet kernel, standard GARCH, Glosten–Jagannathan–Runkle (GJR) and nonlinear EGARCH models with normal, student-t, skew-student-t and generalized error distribution (GED) innovations by using mean absolute error (MAE), root mean squared error (RMSE) and robust Diebold–Mariano test. The results of the out-of-sample forecasts suggest that the SVR–GARCH with a mixture of Gaussian kernels can improve the volatility forecasts and capture the regime-switching behavior.

Bezerra, P.C.S. & Albuquerque, P.H.M. Comput Manag Sci (2016). doi:10.1007/s10287-016-0267-0
Computational Management Science