Mixed frequency composite indicators for measuring public sentiment in the EU.

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19

Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important;however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS-LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high-frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers.

What drives the volatility of non-fungible tokens (NFTs): macroeconomic fundamentals or investor attention?

Non-fungible tokens (NFTs) have experienced wild market fluctuation during the past years, which leads to the high volatility of NFT's daily price. This paper examines two potential volatility drivers of NFTs: macroeconomic fundamentals and investor attention. We employ the global and local economic policy uncertainty (EPU) indices as the economic fundamentals' proxies. The investor attention is represented by the Google search volumes (GSV) or NFTs attention index. Based on the empirical results of a modified generalized autoregressive conditional heteroskedasticity –mixed-data sampling (G-M) model, we find that either economic fundamentals or investor attention can increase the volatility of NFTs significantly. The monthly global EPU index adjusted by the current GDP and weekly GSV contain complementary information. Macroeconomic fundamentals and investor attention can jointly model the volatility of NFTs better than considering only one explanatory variable, as suggested by the G-M model with two explanatory variables. The results remain robust to alternative Twitter-based EPU indices and the ongoing COVID-19 pandemic period. © 2023 Informa UK Limited, trading as Taylor & Francis Group.