This paper examines the dependence structure between crude oil benchmark prices using copulas. By considering several copula models with different conditional dependence structures and time-varying ...dependence parameters, we find evidence of significant symmetric upper and lower tail dependence between crude oil prices. These findings suggest that crude oil prices are linked with the same intensity during bull and bear markets, thus supporting the hypothesis that the oil market is 'one great pool'--in contrast with the hypothesis that states that the oil market is regionalized. Our findings on crude oil price co-movements also have implications for risk management, hedging strategies and asset pricing.
We study the impact of exchange rate volatility on cost efficiency and market structure in a cross-section of banks that have non-trivial exposures to foreign currency (FX) operations. We use unique ...data on quarterly revaluations of FX assets and liabilities (Revals) that Russian banks were reporting between 2004 Q1 and 2020 Q2. First, we document that Revals constitute the largest part of the banks’ total costs, 26.5% on average, with considerable variation across banks. Second, we find that stochastic estimates of cost efficiency are both severely downward biased – by 30% on average – and generally not rank preserving when Revals are ignored, except for the tails, as our nonparametric copulas reveal. To ensure generalizability to other emerging market economies, we suggest a two-stage approach that does not rely on Revals but is able to shrink the downward bias in cost efficiency estimates by two-thirds. Third, we show that Revals are triggered by the mismatch in the banks’ FX operations, which, in turn, is driven by household FX deposits and the instability of Ruble’s exchange rate. Fourth, we find that the failure to account for Revals leads to the erroneous conclusion that the credit market is inefficient, which is driven by the upper quartile of the banks’ distribution by total assets. Revals have considerable negative implications for financial stability which can be attenuated by the cross-border diversification of bank assets.
This paper investigates the dependence structure between the equity market and the foreign exchange market by using copulas. In particular, several copulas with different dependence structure are ...compared and used to directly model the underlying dependence structure. We find that there exists significant symmetric upper and lower tail dependence between the two financial markets, and the dependence remains significant but weaker after the launch of the euro. Our findings have important implications for both global investment risk management and international asset pricing by taking into account joint tail risk.
The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining ...given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union. Besides this, the Delegated Regulation by the European Commission requires all insurance companies under supervision to consider different risk scenarios in their risk management system for the company’s own risk assessment. Since it is unreasonable to assume that the potential worst case scenario will materialize in the company, we think that a modelling of various unfavourable scenarios as described in this paper is likewise appropriate. Our explicit copula approach can be considered as a special case of ordinal sums, which in two dimensions even leads to the technically worst VaR scenario.
Bivariate Fréchet (BF) copulas characterize dependence as a mixture of three simple structures: comonotonicity, independence and countermonotonicity. They are easily interpretable but have ...limitations when used as approximations to general dependence structures. To improve the approximation property of the BF copulas and keep the advantage of easy interpretation, we develop a new copula approximation scheme by using BF copulas locally and patching the local pieces together. Error bounds and a probabilistic interpretation of this approximation scheme are developed. The new approximation scheme is compared with several existing copula approximations, including shuffle of min, checkmin, checkerboard and Bernstein approximations and exhibits better performance, especially in characterizing the local dependence. The utility of the new approximation scheme in insurance and finance is illustrated in the computation of the rainbow option prices and stop-loss premiums.
•Multivariate Extreme value models based on Gumbel Copulas are used to estimate crash frequency-by-severity from traffic conflict indicators.•Traffic conflict indicators are extracted from videos by ...applying an automated computer vision technique.•The accuracy and precision of crash predictions are not proportional to the number of conflict indicators used in the extreme value models.•Modified Time to Collision (MTTC) and Deceleration Rate to Avoid a Crash (DRAC) is the best combination of indicators for rear-end crash frequency estimation.•A trivariate model with MTTC, DRAC and Delta-V efficiently estimates crash frequency-by-severity.
Traffic conflict techniques are a viable alternative to crash-based safety assessments and are particularly well suited to evaluating emerging technologies such as connected and automated vehicles for which crash data are sparsely available. Recently, the use of multiple traffic conflict indicators has become common in methodological studies, yet it is often difficult to determine which conflict indicators are appropriate given the application context, and the net benefit, in terms of improved crash prediction accuracy, of considering additional conflict indicators. Addressing these concerns, this study investigates the potential benefits of multiple conflict indicators for conflict-based crash estimation models by using a multivariate extreme value modeling framework (with Gumbel-Hougaard copulas) to estimate crash frequency by severity. The selected conflict indicators include Modified Time-To-Collision (MTTC), Deceleration Rate to Avoid a Collision (DRAC), Proportion of Stopping Distance (PSD) and expected post-collision change in velocity (Delta-V). The proposed framework was applied to estimate the total, severe (Maximum Abbreviated Injury Scale ≥ 3; MAIS3+), and non-severe (MAIS < 3) rear-end crash frequencies at three four-legged signalized intersections in Brisbane, Australia. Rear-end traffic conflicts were extracted from video data using state-of-the-art Computer Vision analytics. Results show that the prediction performance improvements are not necessarily proportional to the number of conflict indicators used in extreme value models. MTTC and DRAC, combined with the severity indicator Delta-V, were the most suitable predictors of rear-end crashes at signalized intersections. Results suggest that instead of adding more and more conflict indicators, careful selection of compatible conflict indicators (considering their functional differences and empirical correlations) is the best way to enhance the predictive performance of conflict-based models.
This paper attempts to model both static and dynamic dependence structures and measure impacts of energy consumptions (both renewable (
EC
) and non-renewable (
REN
) energies), economic ...globalization (
GLO
), and economic growth (
GDP
) on carbon dioxide (
CO
2
) emissions in Argentina over the period 1970–2020. For analyses purpose, the current research deploys the novel static and dynamic copula-based ARIMA-fGARCH with different submodels. The static bivariate copula results show that the growth rates of the pairs
EC
-
CO
2
and
GDP
-
CO
2
are asymmetrically positive co-movements and have high left tail (extreme) dependencies, implying that the increase in non-renewable energy and economic growth can critically contribute to the environmental degradation, and the decrease in the consumption of non-renewable energy at a high level will consequently reduce the CO
2
emissions at the same level. Based on several copula-based dependence measures, we document that between the two factors, the non-renewable energy has a stronger impact than the economic growth regarding the CO
2
emissions. On the other hand, the growth rates of both economic globalization and renewable energy symmetrically negatively co-move with the growth rates of the CO
2
emissions, but they have no extreme dependencies, indicating that these factors contribute to Argentina’s environmental quality, in which the factor of renewable energy has a greater impact. Furthermore, the dynamic copula outcomes show that the (tail) dependencies of CO
2
emissions on the non-renewable energy and economic growth are time-varying, while the pairs
REN
-
CO
2
and
GLO
-
CO
2
possess only dynamic dependencies, but no dynamic tail dependencies. Moreover, through the dynamic copula-based dependence, the environmental Kuznets curve (EKC) hypothesis can be estimated and illustrated explicitly. In addition, we leverage multivariate vine copulas for modelling dependence structures of the five variables simultaneously, which can reveal rich information regarding conditional associations among the relevant variables. Some policy implications are also provided to mitigate CO
2
emissions.
Density estimation is a fundamental technique employed in various fields to model and to understand the underlying distribution of data. The primary objective of density estimation is to estimate the ...probability density function of a random variable. This process is particularly valuable when dealing with univariate or multivariate data and is essential for tasks such as clustering, anomaly detection, and generative modeling. In this paper we propose the monovariate approximation of the density using spline quasi interpolation and we apply it in the context of clustering modeling. The used clustering technique is based on the construction of suitable multivariate distributions which rely on the estimation of the monovariate empirical densities (marginals). Such an approximation is achieved by using the proposed spline quasi-interpolation, while the joint distributions to model the sought clustering partition is constructed with the use of copulas functions. In particular, since copulas can capture the dependence between the features of the data independently from the marginal distributions, a finite mixture copula model is proposed. The presented algorithm is validated on artificial and real datasets.
An approach to modelling volatile financial return series using stationary d-vine copula processes combined with Lebesgue-measure-preserving transformations known as v-transforms is proposed. By ...developing a method of stochastically inverting v-transforms, models are constructed that can describe both stochastic volatility in the magnitude of price movements and serial correlation in their directions. In combination with parametric marginal distributions it is shown that these models can rival and sometimes outperform well-known models in the extended GARCH family.11The analyses were carried out using R and the tscopula CRAN package, c.f (McNeil and Bladt, 2021), also available at https://github.com/ajmcneil/tscopula. In particular, it uses C++ code for vine copulas from the rvinecopulib package (Nagler and Vatter, 2020).
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