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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 .

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.

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 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.

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)).

Functions and processes with irregular behaviour in time are ubiquitous in physics, engineering, and finance and have been the focus of various pathwise theories of integration in stochastic analysis, in which the degree of 'roughness' of the function plays an important role. This thesis focuses on various concepts of 'roughness' for continuous functions and processes and their interplay with pathwise integration. We first explore these issues using the concept of pathwise quadratic variation, then expand results to the more general setting of p-th order variation. The first chapter discusses some motivations and background for the questions explored in the thesis and provides an overview of the results. In the second chapter, we study quadratic variation along a sequence of partitions and its dependence with respect to the choice of the partition sequence. We introduce a property which we call quadratic roughness, and show that for H ̈older-continuous paths satisfying this roughness condition, the quadratic variation along 'balanced' partitions is invariant with respect to the choice of the partition sequence. Typical paths of Brownian motion satisfy this quadratic roughness property almost-surely along partitions with fine enough mesh. Using these results we derive a formulation of the pathwise F ̈ollmer-Itˆo calculus which is invariant with respect to the partition sequences. Furthermore, we provide an invariance result for local time under quadratic roughness. In the third chapter, instead of balanced partition sequences (which is a key condition in Chapter 2) we consider (finitely) refining partition sequences, without any bound on mesh size. We construct a generalized Haar basis along any such finite refining sequence of partitions. We provide a closed-form representation of quadratic variation in terms of Faber-Schauder coefficients along this basis. Further, we construct a class of continuous processes with linear and prescribed quadratic variations along any given finitely refining partition sequence. We provide an example of a rough class of continuous processes with invariant quadratic variations along finitely refining sequences of partitions. Brownian motion belongs to this 'rough' class, but we also give examples of processes with 1/2 -H ̈older continuity in this class. Finally, we extend these constructions to higher dimensions. In the fourth chapter of the thesis, we consider a more general concept of roughness based on p-th variation and the associated notions of variation and roughness index of a continuous function. We define the normalized p-th variation of a path and use it to introduce a pathwise estimator to estimate the order of roughness of a signal. We investigate the finite sample performance of our estimator for measuring the roughness of sample paths of stochastic processes using detailed numerical experiments based on sample paths of fractional Brownian motion and Takagi-Landsberg functions. In the final chapter we use our 'roughness' estimator (discussed in Chapter 4) to investigate the statistical evidence for the use of 'rough' fractional processes with Hurst exponent H < 0.5 for the modelling of volatility of financial assets, using a non-parametric, model-free approach. Detailed numerical experiments based on stochastic volatility models show that, even when the instantaneous volatility has diffusive dynamics with the same roughness as Brownian motion, the realized volatility exhibits rough behaviour corresponding to a Hurst exponent significantly smaller than 0.5, which suggests that the origin of the roughness observed in realized volatility time-series lies in the estimation error rather than the volatility process itself. Comparison of roughness estimates for realized and instantaneous volatility in fractional volatility models with different values of Hurst exponent shows that, irrespective of the value of H, realized volatility always exhibits 'rough' behaviour with an apparent Hurst index ˆH < 0.5 but this is not necessarily indicative of a similar rough behaviour of the spot volatility process which may have H ≥ 1/2.

GARCH-MIDAS model of Engle et al. (2013) describes the volatility of daily returns as the product of a short-term volatility component, modelled by a Unit GARCH(1,1), and long-term component volatility which is modelled by a macroeconomic variable(s) which are observed at a lower frequency. This model has been applied extensively in volatility modelling using the Maximum Likelihood Estimation (MLE) Method despite that little is known about its finite sample properties. In this thesis, we fill this gap and extend it to other models such as EGARCH-MIDAS, and stochastic volatility models such as SVL-MIDAS and Heston-MIDAS models and their jump augmented versions to capture the leverage effect and the impact of rare events. Results of our first contribution indicate that in-sample and out-sample performance of GARCH type MIDAS models depend on the specification gt whereas τˆt is not sensitive to the choice of the short-term component of the volatility; our simulation and empirical studies suggest that whenever EGARCH(1,1), say, outperforms GARCH(1,1), EGARCH-MIDAS outperforms GARCH-MIDAS; and MLE estimate of GARCH-MIDAS, and EGARCH-MIDAS are not consistent when the returns series contain spikes or its volatility is highly persistent. These results led us to our second main contribution of estimating their parameters and those of their Jump augmented versions using Bayesian approach by overcoming the complexity of their posterior distributions by applying Metropolis Hasting simulation method. Our simulation and empirical studies indicate that our MCMC algorithms successfully capture the jump component and produce accurate estimates when MLE fails. Based on the findings of our first two contributions and the recognized out-performance of stochastic volatility models over GARCH type models, we developed SV-MIDAS, SVL-MIDAS, and Heston-MIDAS with their jump augmented extensions. Our MCMC algorithms can be extended to more complex multi-component volatility models to be considered in our future work.

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.

This paper investigates whether realized and implied volatilities of individual stocks can predict the cross-sectional variation in expected returns. Although the levels of volatilities from the physical and risk-neutral distributions cannot predict future returns, there is a significant relation between volatility spreads and expected stock returns. Portfolio level analyses and firm-level cross-sectional regressions indicate a negative and significant relation between expected returns and the realized-implied volatility spread that can be viewed as a proxy for volatility risk. The results also provide evidence for a significantly positive link between expected returns and the call-put options' implied volatility spread that can be considered as a proxy for jump risk. The parameter estimates from the VAR-bivariate-GARCH model indicate significant information flow from individual equity options to individual stocks, implying informed trading in options by investors with private information.