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Latin American market asset volatility analysis: a comparison of garch model, artificial neural networks and support vector regression

Chung Víctor, Espinoza Jenny

Applied Computer Science

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2023

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Vol. 19, no 3

1--16

EN

The objective of this research was to compare the effectiveness of the GARCH method with machine learning techniques in predicting asset volatility in the main Latin American markets. The daily squared return was utilized as a volatility indicator, and the accuracy of the predictions was assessed using root mean square error (RMSE) and mean absolute error (MAE) metrics. The findings consistently demonstrated that the linear SVR-GARCH models outperformed other approaches, exhibiting the lowest MAE and MSE values across various assets in the test sample. Specifically, the SVR-GARCH RBF model achieved the most accurate results for the IPC asset. It was observed that GARCH models tended to produce higher volatility forecasts during periods of heightened volatility due to their responsiveness to significant past changes. Consequently, this led to larger squared prediction errors for GARCH models compared to SVR models. This suggests that incorporating machine learning techniques can provide improved volatility forecasting capabilities compared to the traditional GARCH models.

2

Contagion effects on capital and forex markets around GFC and COVID-19 crises : a comparative study

Brania Krzysztof, Gurgul Henryk

Operations Research and Decisions

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2021

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Vol. 31, No. 2

59--92

EN

The spread of crises across the financial and capital markets of different countries has been studied. The standard method of contagion detection is based on the evolution of the correlation matrix for the example of exchange rates or returns, usually after removing univariate dynamics with the GARCH model. It is a common observation that crises that have occurred in one financial market are usually transmitted to other financial markets/countries simultaneously and that they are visible in different financial variables such as returns and volatility which determine probability distribution. The changes in distributions can be detected through changes in the descriptive statistics of, e.g., returns characterised by expected value, variance, skewness, kurtosis, and other statistics. They determine the shape of the distribution function of returns. These descriptive statistics display dynamics over time. Moreover, they can interreact within the given financial or capital market and among markets. We use the FX currency cluster represented by some of the major currencies and currencies of the Višegrad group. In analysing capital markets in terms of equity indexes, we chose developed markets, such as DAX 30, AEX 25, CAC 40, EURSTOXX 50, FTSE 100, ASX 200, SPX 500, NASDAQ 100, and RUSSEL 2000. We aim to check the changes in descriptive statistics, matrices of correlation concerning exchange rates, returns and volatility based on the data listed above, surrounding two crises: the global financial crisis (GFC) in 2007–2009 and Covid 2019.

3

Zwrot z inwestycji w gospodarstwach produkujących mleko a ograniczenia wynikające z kwoty mlecznej

Borowski P. F.

Inżynieria Rolnicza

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2008

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R. 12, nr 1(99)

33-39

PL

Rozwój gospodarstw produkujących mleko jest odgórnie ograniczony wielkością przyznanej kwoty mlecznej. Środki finansowe zainwestowane w rozwój gospodarstwa zwracają się po dłuższym okresie, gdyż rolnik nie może zwiększyć produkcji mleka bez ponoszenia dodatkowych kosztów. Obliczenia wskazują, że funkcjonowanie kwoty mlecznej zmniejsza atrakcyjność inwestycji.

EN

The development of milk-producing farms is top-down limited by the amount of allocated milk quota. Financial means invested in farm development will be returned after longer period of time, since farmer cannot increase milk production without incurring extra costs. Calculations indicate that milk quota functioning lowers an investment attractiveness.