Search Results (6)

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers. Full article

Price volatility is a significant risk factor affecting the income of farmers in the agriculture sector, especially for international trade in products such as coffee in Thailand. This study proposes an alternative model to analyze the major factors in terms of internal and external factors, which are expected to affect price volatility simultaneously, by applying multiple exogenous Bayesian GARCH-X models. The empirical results of the comparison between the multiple exogenous Bayesian GARCH-X model and the standard Bayesian GARCH-X model, which estimated the impact of individual exogenous variables separately, show that the standard error of the first model is the smallest compared to the others, which means the multiple exogenous Bayesian GARCH-X model is more fitted to the data than the others. The results indicate that the increase in demand for manufactories for coffee beans in Thailand (TDD) and coffee bean export volume in Indonesia (INEX) leads to an increase in the volatility of raw coffee prices. On the other hand, if coffee bean export volume in Brazil (BEX) increases, this will cause a decrease in the volatility of raw coffee bean prices. Therefore, the Thai government should carefully consider the changes in the production and marketing policies of those countries in the formulation of the coffee policy. The appropriate policy on coffee price volatility in Thailand should not concern only reduce the uncertainty in the coffee bean market but also consider the impact on the long-term income and livelihoods of coffee growers. Therefore, external factors of the competing countries should be taken into account in the coffee production policy. Full article

In this paper, for the data set of the Iberian Electricity Market for the period 1 January 2015 to 30 June 2019, 19 different models are considered from econometrics, statistics, and artificial intelligence to explain how electricity markets work. This survey allows us to obtain a more complete, critical view of the most cited models. The machine learning models appear to be very good at selecting the best explanatory variables for the price. They provide an interesting insight into how much the price depends on each variable under a nonlinear perspective. Notwithstanding, it might be necessary to make the results understandable. Both the autoregressive models and the linear regression models can provide clear explanations for each explanatory variable, with special attention given to GARCHX and LASSO regression, which provide a cleaner linear result by removing variables that have a minimal linear impact. Full article

Thailand is a significant global exporter of cassava, of which cassava chips are the main export products. Moreover, China was the most important export market for Thailand from 2000 to 2020. However, during that period, Thailand confronted fluctuations in the cassava product price, and cassava chips were a product with significant price volatility, adapting to changes in export volumes. This study aims to analyze the volatility of the price of cassava chips in Thailand from 2010 to 2020. The data were collected monthly from 2010 to 2020, including the price of cassava chips in Thailand (Y), the volume of cassava China imported from Thailand (X1), the price of the cassava chips that China imported from Thailand (X2), the price of the cassava starch that China imported from Thailand (X3), the substitute crop price for maize (X4), the substitute crop price for wheat (X5), and Thailand’s cassava product export volume (X6). The volatility and the factors affecting the volatility in the price of cassava chips were calculated using Bayesian GARCH-X. The results indicate that the increase in X1, X2, X3, X4, and X6 led to an increase in the rate of change in cassava chip price volatility. On the other hand, if the substitute crop price for wheat (X5) increases, then the rate of change in the volatility of the cassava chip price decreases. Therefore, the government’s formulation of an appropriate cassava policy should take volatility and the factors affecting price volatility into account. Additionally, the government’s formulation of agricultural policy needs to consider Thailand’s macro-environmental factors and its key trading partners, especially when these environmental factors signal changes in the price volatility of cassava. Full article

This paper aims to investigate the impact of various COVID-19 pandemic waves on real estate stock returns and their volatility in developed (US, Australia), emerging (Turkey, Poland), and frontier (Morocco, Jordan) markets. A study using a GJR-GARCHX model revealed that the pandemic outbreak had a limited impact on real estate company stocks. The first pandemic wave only in the US caused a decline in stock returns. In turn, this was the case in Poland and Jordan during the second and third waves. Furthermore, in the aftermath of the pandemic development, an increase in the volatility of stock returns can be observed in the Polish financial market. However, this effect mainly applies to the period of the first disease wave. Full article

A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model implements the yield curve differentials between EUR and the US as exogenous factors. Functional principal component analysis allows us to use the information of basically the whole yield curve in a parsimonious way for exchange rate risk prediction. The data analyzed in our empirical study consist of the EURUSD exchange rate and the EUR- and US-yield curves from 15 August 2005–30 September 2016. As a benchmark, we take an ARMA-GARCH and an ARMAX-GARCHX with the 2y-yield difference as the exogenous variable and compare the forecasting performance via likelihood ratio tests. However, while our model performs better in one situation, it does not seem to improve the performance in other setups compared to its competitors. Full article