This study aims to reveal the network connectedness between the volatilities of Emerging and Growth-Leading Economies (EAGLEs) stock exchanges with the frequency-based TVP-VAR connectedness approach. Connectedness results were obtained in short (1–5 days) and long (5-inf) period frequencies among the volatilities obtained with the Garman–Klass volatility estimator. According to the dynamic TCI results, connectivity peaked during the COVID-19 and Russia–Ukraine War periods. BVSP is the most dominant transmitter of the network and spreads the most effect to the emerging markets. As a result of the pairwise metrics, SSE has the lowest values and is positioned as a relatively independent market in the network. In particular, SSE has almost no connection with BIST in the short term, while it has a more significant effect on BIST in the long term. Moreover, the connectedness metrics show that MOEX is in a neutral position in the network and is largely affected by its internal dynamics.

In agricultural economics, fluctuations in food prices and the factors affecting these fluctuations have always been an important research topic. From production to delivery to consumers, the supply chain of agricultural products has a dynamic structure with continuous changes. In this dynamic process, analyzing the intensive use of energy at each stage has gained more importance with its deepening effects in comparison to the past. This study will empirically explore the volatility spillovers between energy price index and fruit-vegetables price index in the period of 2007-2020 in Turkey using the Kanas and Diebold-Yilmaz approaches. According to the results obtained from the Kanas approach in the study, it has been observed that there is a statistically significant volatility spillover from the energy price index to the vegetable price index, whereas there is no statistically significant volatility spillover to the fruit price index. This finding was supported by the results obtained from the Diebold-Yilmaz approach showing that there is a volatility spillover of 13.52% to the vegetable price index and 0.86% to the fruit price index from the energy price index.

The volatility connectedness among fertilisers and agricultural crop prices: Evidence from selected main agricultural products | Harun Uçak, Yakup Ari, Esin Yelgen | Agricultural Journals

The aim of the study is to determine the most appropriate discrete model for the volatility of Bitcoin returns using the discrete-time GARCH model and its extensions and compare it with the Levy-driven continuous-time GARCH model. For this purpose, the volatility of Bitcoin returns is modeled using daily data of the Bitcoin / United States Dollar exchange rate. By comparing discrete-time models according to information criteria and likelihood values, the All-GARCH model with Johnson's-SU innovations is found as the most adequate model. The persistence of the volatility and half-life of the volatility of the returns are calculated according to the estimation of the discrete model.This discrete model has been compared with the continuous model in which the Levy increments are derived from the compound Poisson process using various error measurements. In conclusion, it is found that the continuous-time GARCH model shows a better performance in predicting volatility.

Research background: The main purpose of monetary integration between EU countries is to eliminate excessive fluctuations in exchange rates. High volatility in exchange rates can cause various negative economic and financial effects, especially during periods of economic shocks. In addition, estimating the volatility between currencies and their interactions is of great importance for effective portfolio management. Purpose: The objective of this research is to scrutinize the transmission of volatility between the currencies of those European Union nations that do not participate in the EURO area, focusing on the exchange rate parity of the US Dollar with seven non-EURO zone currencies. Research methodology: Daily volatility in exchange rates was calculated using the Garman-Klass-Yang-Zhang (GK-YZ) method. To investigate the connectedness between these volatilities, we used the Time-Varying Parameter Vector Autoregression (TVP-VAR) frequency connectedness approach. Results: The Average Total Connectedness Index exhibits a significant degree of connectedness of approximately 71.84%. The Net Total Directional Connectedness Index indicates that the CZK, DKK and RON exchange rates are net beneficiaries in aggregate and in a longer term perspective, whereas the DKK, HUF and PLN exchange rates are net beneficiaries in a shorter term horizon. In the context of major global events such as the onset of the COVID-19 outbreak in March 2020 and the start of the Russia-Ukraine conflict in February 2022, it could be observed that the dynamic Total Connectedness Index exhibited a substantial increase, both overall and from a long-term perspective, corroborating theoretical expectations. According to the Net Pairwise Directional Connectedness index, the highest bilateral connectedness overall and in the short run was between DKK and RON, while in the long run between BGN and DKK. Novelty: Examining the connectedness of currencies is of great importance for investors doing business with foreign currency, international cooperation and policies, risk management and portfolio management. Determining the connectedness in different frequency (short and long-term) ranges provides important information for hedging risk. In addition, the bilateral connectedness between currencies is a guide for effective portfolio diversification.

This paper aims to show the application of range-based volatility in connectedness analysis. For this purpose, we compare the volatility estimators Parkinson, Yang-Zhang, Garman-Klass, Rogers-Satchell, and modified Garman- Klass by Yang and Zhang methods. As an example, we calculated the range-based stock prices’ volatility of four defense industry companies quoted in Borsa Istanbul. We compared the forecast performance of volatility against Heteroskedastic Root Mean Square Error statistics. We include the best performing volatility series in the spillover analysis. Instead of the Cholesky decomposition VAR and generalized VAR approaches used in the calculation of the Diebold-Yılmaz connectedness index, we apply the TVP-VAR-based connectedness approach. The comparison results show that Rogers-Satchell for ASELSAN, KATMERLER, and PAPIL, and Parkinson volatility estimator for OTOKAR have the smallest error, respectively. The empirical findings of TVP-VAR connectedness show that the average forecast error variance of the network is 34.35%.