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Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

We identify networks of volatility spillovers and examine time-varying spillover intensities with daily implied volatilities of U.S. Treasury bonds, global stock indices, and commodities. The U.S. stock market is the center of the international volatility spillover network, and its volatility spillover to other markets has intensified since 2008. Moreover, U.S. quantitative easing alone explains 40%–55% of intensifying spillover from the United States. The addition of interest rate and currency factors does not diminish the dominant role of quantitative easing. Our findings highlight the primary contribution of U.S. unconventional monetary policy to volatility spillovers and potential global systemic risk. This paper was accepted by Neng Wang, finance .

This paper investigates the forecasting ability of several volatility specifications that aim to quantify market risk. Using an options’ trading strategy on volatility the comparison is implemented in a dynamic approach, applying the standardized prediction error criterion. The empirical findings of the paper suggest that the SPEC criterion outperforms all volatility models that assume normality on the data and exhibits similar forecasting ability with most of the models that assume skewed distributions of asset returns.

Finansal yatırım kararı alınırken ve risk yönetimi kapsamında politikalar belirlenirken göz önünde bulundurulması gereken en önemli kavram “risk” kavramıdır. Gelecekte karşılaşılabilecek farklı risk düzeylerinin uygun yöntemle öngörülmesi, bu risklere karşı hazırlıklı olunması ve doğru kararlar alınması açısından büyük öneme sahiptir. Doğru öngörülerde bulunabilmek ise ancak, istatistiksel performansı en yüksek modellerin belirlenmesiyle münkündür. Çalışmada, 3 Ocak 2011 – 24 Temmuz 2020 dönemi BIST100 endeksi haftalık verilerine dayalı olarak endeks getiri volatilitesi tahminlerini elde etmek ve istatistiksel performansı en yüksek modeli belirlemek amacıyla simetrik ve asimetrik modeller arasından seçilen getiri bazlı “ARCH (1) Modeli” ve değişim genişliği bazlı “Koşullu Otoregresif Değişim Genişliği Modelleri (KODGM)” tahmin edilmiştir. Yapılan değerlendirmeler sonucunda, BIST100 getiri volatilitesi tahmininde kullanılabilecek en uygun modelin, hataların Weibull dağılımı izlediği, kaldıraç etkisinin dikkate alındığı ve aşırı değerlere karşı dirençli olan “Kartiller Arası Değişim Genişliği” ölçüsüne dayalı olarak tahmin edilen “WKODGX (1,1) Modeli” olduğu tespit edilmiştir. Tüm bulgular birlikte değerlendirildiğinde, değişim genişliği bazlı modellerin, BIST100 endeks getirisi volatilite modellemesinde istatistiksel performansı belirgin bir biçimde iyileştirdiği sonucuna varılmıştır.

This paper analyses the volatility transmission between European Global Systemically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe’s GSIBs and the world’s most prominent measure of market “fear”, the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile–where extreme negative bank returns are present–jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics.

This paper examines the effect of geopolitical risk (GPR) on return and volatility dynamics in Middle East and North African (MENA) countries by using an ADCC-GARCH model and a spillover approach. Unlike previous studies, we include the GPR index to capture risk associated with wars, terrorist acts, and political tensions. Moreover, we test for both static and dynamic analysis using a rolling window. In brief, the findings highlight that GPR does not contribute to the return spillovers among MENA financial markets. However, the dynamic analysis provides evidence of the high level of responsiveness of the total spillover index to major political events (e.g., the Arab Spring uprising and political tension between Qatar and other Gulf Cooperation Council countries). More interestingly, Qatar, Kingdom of Saudi Arabia, and the United Arab Emirates are identified as the main transmitters of return spillovers to the rest of the MENA markets. Overall, our results are essential in understanding the impact of the GPR on return spillover among MENA countries, and are of particular importance to policymakers, market regulators, portfolio managers and investors.

This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

This paper deals with the issue of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, we employ a maturity-dependent damping term to account for the Samuelson effect. We give the conditions under which the volatility dynamics are well defined and obtain the joint characteristic function of a pair of futures prices. We then derive the state-space representation of our model in order to use the Kalman filter algorithm for estimation and prediction. The empirical analysis is carried out using daily futures data from 2007 to 2019 for corn, cotton, soybeans, sugar and wheat. In-sample, the seasonal models clearly outperform the nested non-seasonal models in all five markets. Out-of-sample, we predict volatility peaks with high accuracy for four of these five commodities.

In “Recent Advances in Dye Sensitized Solar Cells,” there is an error in page 8 (Section 3, first column, point (iv)). The error is “low volatility”: it creates a conflict with the statement “developing low volatile” on the same page 8 (Section 3, second column, point (iii)).