Volatility Modeling and Dependence Structure of ESG and Conventional Investments

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

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Dynamic dependence structure between energy markets and the Italian stock index

Investment Management and Financial Innovations ◽

10.21511/imfi.15(2).2018.06 ◽

2018 ◽

Vol 15 (2) ◽

pp. 60-67

Author(s):

Giovanni Masala

Keyword(s):

Extreme Values ◽

Tail Dependence ◽

Energy Markets ◽

Stock Index ◽

Dependence Structure ◽

Time Varying ◽

Empirical Distributions ◽

Proposed Model ◽

Log Likelihood ◽

Tail Dependence Coefficient

The dependence structure between the main energy markets (such as crude oil, natural gas, and coal) and the main stock index plays a crucial role in the economy of a given country. As the dependence structure between these series is dramatically complex and it appears to change over time, time-varying dependence structure given by a class of dynamic copulas is taken into account.To this end, each pair of time series returns with a dynamic t-Student copula is modelled, which takes as input the time-varying correlation. The correlation evolves with the DCC(1,1) equation developed by Engle.The model is tested through a simulation by employing empirical data issued from the Italian Stock Market and the main connected energy markets. The author considers empirical distributions for each marginal series returns in order to focus on the dependence structure. The model’s parameters are estimated by maximization of the log-likelihood. Also evidence is found that the proposed model fits correctly, for each pair of series, the left tail dependence coefficient and it is then compared with a static copula dependence structure which clearly underperforms the number of joint extreme values at a given confidence level.

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EXTREMAL DEPENDENCIES ON COMMODITY FUTURES MARKETS

Annals of the Polish Association of Agricultural and Agribusiness Economists ◽

10.5604/01.3001.0013.5628 ◽

2019 ◽

Vol XXI (4) ◽

pp. 163-171

Author(s):

Małgorzata Just

Keyword(s):

Portfolio Management ◽

Extreme Values ◽

Tail Dependence ◽

Commodity Futures ◽

Futures Contracts ◽

Subprime Crisis ◽

Rates Of Return ◽

Commodity Futures Markets ◽

Agricultural Futures ◽

Heating Oil

The aim of this study was to assess dependencies between extreme rates of return from commodity futures contracts on selected markets in the years 2000-2018. In periods of upheavals and turbulences, in markets for investors and portfolio management, it is crucial to estimate the probability of risk factors simultaneously taking extreme values. The analyses were conducted on dependencies between extreme rates of return (asymptotic dependencies) on markets of futures contracts for energy, metals and agricultural products in the years 2000-2018, applying the Copula-ARMA-GARCH models and tail dependence coefficients. Relatively strong and permanent asymptotic dependencies were found for pairs of futures contracts for crude oil and heating oil, while either no such dependencies were observed or only appeared during the subprime crisis and assumed very low values for other energy pairs of futures contracts and pairs of agricultural futures contracts, in which at least one of the contracts was concluded for soft commodities.

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Testing constancy of unconditional variance in volatility models by misspecification and specification tests

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2015-0033 ◽

2016 ◽

Vol 20 (4) ◽

Cited By ~ 3

Author(s):

Annastiina Silvennoinen ◽

Timo Teräsvirta

Keyword(s):

Garch Model ◽

Garch Models ◽

Specification Tests ◽

Size Distortion ◽

Short Time Series ◽

Conditional Mean ◽

Constant Variance ◽

Volatility Models ◽

Short Time ◽

Unconditional Variance

AbstractThe topic of this paper is testing the hypothesis of constant unconditional variance in GARCH models against the alternative that the unconditional variance changes deterministically over time. Tests of this hypothesis have previously been performed as misspecification tests after fitting a GARCH model to the original series. It is found by simulation that the positive size distortion present in these tests is a function of the kurtosis of the GARCH process. Adjusting the size by numerical methods is considered. The possibility of testing the constancy of the unconditional variance before fitting a GARCH model to the data is discussed. The power of the ensuing test is vastly superior to that of the misspecification test and the size distortion minimal. The test has reasonable power already in very short time series. It would thus serve as a test of constant variance in conditional mean models. An application to exchange rate returns is included.

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Tail dependence structure between carbon emission allowances returns based on Copulas

Manufacture Engineering and Environment Engineering ◽

10.2495/meee131752 ◽

2013 ◽

Author(s):

Dedong Zhuang

Keyword(s):

Carbon Emission ◽

Tail Dependence ◽

Dependence Structure

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A strategy for meta-analysis of short time series microarray datasets

Frontiers in Bioscience ◽

10.2741/3512 ◽

2009 ◽

Vol Volume (14) ◽

pp. 4058 ◽

Cited By ~ 2

Author(s):

Ruping Sun

Keyword(s):

Time Series ◽

Meta Analysis ◽

Short Time Series ◽

Microarray Datasets ◽

Short Time ◽

Time Series Microarray

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Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00872-w ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Tie Liang ◽

Qingyu Zhang ◽

Xiaoguang Liu ◽

Bin Dong ◽

Xiuling Liu ◽

...

Keyword(s):

Time Series ◽

Information Flow ◽

Gamma Band ◽

Motor Dysfunction ◽

Beta Band ◽

Short Time Series ◽

Information Interaction ◽

Stroke Group ◽

Short Time ◽

Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

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Dependence structure between money and economic activity: A Markov-switching copula VEC approach

Macroeconomic Dynamics ◽

10.1017/s1365100521000250 ◽

2021 ◽

pp. 1-17

Author(s):

Apostolos Serletis ◽

Libo Xu

Keyword(s):

Error Correction ◽

Economic Activity ◽

Regime Switching ◽

Markov Switching ◽

Tail Dependence ◽

Error Correction Model ◽

Dependence Structure ◽

Copula Models ◽

Correction Model ◽

Dependence Structures

Abstract This paper examines correlation and dependence structures between money and the level of economic activity in the USA in the context of a Markov-switching copula vector error correction model. We use the error correction model to focus on the short-run dynamics between money and output while accounting for their long-run equilibrium relationship. We use the Markov regime-switching model to account for instabilities in the relationship between money and output, and also consider different copula models with different dependence structures to investigate (upper and lower) tail dependence.

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Improved Lempel-Ziv Algorithm Based on Complexity Measurement of Short Time Series

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) ◽

10.1109/fskd.2007.357 ◽

2007 ◽

Cited By ~ 4

Author(s):

Feng-tao Liu ◽

Yong Tang

Keyword(s):

Time Series ◽

Short Time Series ◽

Complexity Measurement ◽

Short Time

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Extremal behavior of Archimedean copulas

Advances in Applied Probability ◽

10.1239/aap/1300198519 ◽

2011 ◽

Vol 43 (1) ◽

pp. 195-216 ◽

Cited By ~ 20

Author(s):

Martin Larsson ◽

Johanna Nešlehová

Keyword(s):

Domain Of Attraction ◽

Tail Dependence ◽

Dependence Structure ◽

Tail Behavior ◽

Archimedean Copulas ◽

Radial Part ◽

Stochastic Representation ◽

Lower Tail ◽

Extremal Behavior ◽

Practical Implications

We show how the extremal behavior of d-variate Archimedean copulas can be deduced from their stochastic representation as the survival dependence structure of an ℓ1-symmetric distribution (see McNeil and Nešlehová (2009)). We show that the extremal behavior of the radial part of the representation is determined by its Williamson d-transform. This leads in turn to simple proofs and extensions of recent results characterizing the domain of attraction of Archimedean copulas, their upper and lower tail-dependence indices, as well as their associated threshold copulas. We outline some of the practical implications of their results for the construction of Archimedean models with specific tail behavior and give counterexamples of Archimedean copulas whose coefficient of lower tail dependence does not exist.

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