Abstract: This paper develops a novel approach to assess liquidity-adjusted Value-at-Risk (LVaR) optimization of multi-asset portfolios based on vine copulas and LVaR models. This framework is applied to stock markets of the G-7 countries, gold, commodities and Bitcoin. The results show that our approach is superior to the classical mean–variance Markowitz portfolio technique in terms of the optimal portfolio selection under a number of realistic operational and budget constraints. We find that both Bitcoin and gold improves the risk-return performance of the G-7 stock portfolio. However, Bitcoin (gold) performs better under a scenario of only long-positions (when short-selling is allowed).

Abstract: A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. We use neural network to find the embeddings for the best possible vine model and generate a structure. Throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of loglikelihood. Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.

Abstract: In this paper, we propose a regular vine copula based methodology for the fusion of correlated decisions. Regular vine copula is an extremely flexible and powerful graphical model to characterize complex dependence among multiple modalities. It can express a multivariate copula by using a cascade of bivariate copulas, the so-called pair copulas. Assuming that local detectors are single threshold binary quantizers and taking complex dependence among sensor decisions into account, we design an optimal fusion rule using a regular vine copula under the Neyman–Pearson framework. In order to reduce the computational complexity resulting from the complex dependence, we propose an efficient and computationally light regular vine copula based optimal fusion algorithm. Numerical experiments are conducted to demonstrate the effectiveness of our approach.

Abstract: Vine copulas, or pair-copula constructions, have become an important tool in high-dimensional dependence modeling. Commonly, it is assumed that the data generating copula can be represented by a simplified vine copula (SVC). In this paper, we study the simplifying assumption and investigate the approximation of multivariate copulas by SVCs. We introduce the partial vine copula (PVC) which is a particular SVC where to any edge a $j$-th order partial copula is assigned. The PVC generalizes the partial correlation matrix and plays a major role in the approximation of copulas by SVCs. We investigate to what extent the PVC describes the dependence structure of the underlying copula. We show that, in general, the PVC does not minimize the Kullback-Leibler divergence from the true copula if the simplifying assumption does not hold. However, under regularity conditions, stepwise estimators of pair-copula constructions converge to the PVC irrespective of whether the simplifying assumption holds or not. Moreover, we elucidate why the PVC is often the best feasible SVC approximation in practice.

Abstract: We introduce a new goodness-of-fit test for regular vine (R-vine) copula models, a very flexible class of multivariate copulas based on a pair-copula construction (PCC). The test arises from White’s information matrix test and extends an existing goodness-of-fit test for copulas. The corresponding critical value can be approximated by asymptotic theory or simulation. The simulation based test shows excellent performance with regard to observed size and power in an extensive simulation study, while the asymptotic theory based test is inadequate for n≤10,000 for a 5-dimensional model (in d = 8 even 20,000 are not enough). The simulation based test is applied to select among different R-vine specifications modeling the dependency among exchange rates.

Abstract: Based on the canonical vine (C-vine) copula approach, this paper examines the interdependence between the exchange rates of the Chinese Yuan (CNY) and the currencies of major Association of Southeast Asian Nations (ASEAN) countries. The differences in the dependence structure and degree between currencies before and after the Belt and Road (B&R) Initiative were compared in order to investigate the changing role of the Renminbi (RMB) in the ASEAN foreign exchange markets. The results indicate a positive dependence between the exchange rate returns of CNY and the currencies of ASEAN countries and show the rising power of RMB in the regional currency markets after the B&R Initiative was launched. Besides this, the Malaysian Ringgit proved to be most relevant to the other ASEAN currencies, thus playing an important role in the stability of regional financial markets. Moreover, evidence of tail dependence was found in the returns of three currency pairs after the B&R Initiative, which implies the presence of asymmetric dependence between exchange rates. The results from time-varying C-vine copulas further confirmed the robustness of the results from the static C-vine copulas.

Abstract: This paper proposes a boosting vine copula-based dependence description (BVCDD) method for multivariate and multimode process monitoring. The BVCDD aims to improve the standard vine copula-based de ...

Abstract: Seismic vulnerability of highway bridges is crucial to the seismic risk assessment of highway transportation networks. A new framework for developing the fragility function at the system level utilizing the D-vine copula theory is proposed. In this methodology, first, the joint probabilistic seismic demand model (JPSDM), which represents the conditional joint distribution of various engineering demand parameters (EDPs) under each intensity measure (IM) level, is constructed using the D-vine approach; then, sample realizations of both the established JPSDM and capacity models of these considered components are compared to derive the system-level fragility curves. This methodology is illustrated in a typical two-span RC continuous girder-box bridge case, where pier columns, elastomeric pad bearings, the sliding bearings, and the abutments in both passive and active directions are considered as the major components. The results demonstrate that the performance of the optimal D-vine for characterizing the dependence structure among various EDPs is relatively superior in comparison with commonly-utilized multivariate standard copulas. The combination of the quantitatively-identified optimal D-vine copula and their conditional lognormal marginal distributions is adequate to construct the JPSDM of these five EDPs. Furthermore, the effects of different copula selections on the overall system vulnerability are well captured, which highlight the necessity for quantitative identification of the optimal D-vine copula for modeling the correlations among various EDPs.

Abstract: We propose a novel structure selection method for high-dimensional ( $$d > 100$$ ) sparse vine copulas Current sequential greedy approaches for structure selection require calculating spanning trees in hundreds of dimensions and fitting the pair copulas and their parameters iteratively throughout the structure selection process Our method uses a connection between the vine and structural equation models The later can be estimated very fast using the Lasso, also in very high dimensions, to obtain sparse models Thus, we obtain a structure estimate independently of the chosen pair copulas and parameters Additionally, we define the novel concept of regularization paths for R-vine matrices It relates sparsity of the vine copula model in terms of independence copulas to a penalization coefficient in the structural equation models We illustrate our approach and provide many numerical examples These include simulations and data applications in high dimensions, showing the superiority of our approach to other existing methods

Abstract: Multivariate copulas have been widely used to handle risk in the financial market. This paper aimed to adopt two novel multivariate copulas, Vine copulas and Factor copulas, to measure and compare the financial risks of the emerging economy, developed economy, and global economy. In this paper, we used data from three groups (BRICS, which stands for emerging markets, specifically, those of Brazil, Russia, India, China, and South Africa; G7, which refers to developed countries; and G20, which represents the global market), separated into three periods (pre-crisis, crisis, and post-crisis) and weighed Value at Risk (VaR) and Expected Shortfall (ES) (based on their market capitalization) to compare among three copulas, C-Vine, D-Vine, and Factor copulas. Also, real financial data demonstrated that Factor copulas have stronger stability and perform better than the other two copulas in high-dimensional data. Moreover, we showed that BRICS has the highest risk and G20 has the lowest risk of the three groups.

Abstract: Location of wind power plants and demand centres are not always close by; hence, the transmission of energy puts a burden on existing grid infrastructure. This unwanted burden necessitates transmission lines to operate more and more frequently close to their operating limits. To alleviate such situations, this research addresses the advantages of modelling spatio-temporal dependence of load and wind power using vine copula. Probabilistic AC optimal power flow is performed on a modified IEEE 39-bus system with significant wind penetration. Real load and wind power data from a U.S. utility is mapped onto the test-case to achieve realistic results. Load flow calculation can help in performing steady-state voltage and overload evaluations for post-disturbance system conditions. Because the security level of a power system is determined by the likelihood and severity of security violation. In this research, the probability of line overload is calculated from load flow and the severity function describes the risk of line overloading. Two case studies depicting future operating conditions of massive wind power penetration with reduced fossil fuel and nuclear power generation are considered. Simulation results prove the advantage of addressing spatio-temporal dependency to quantify the overload risk index, which is treated as a security indicator.

Abstract: State Key Laboratory of Eco‐hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, China The Post‐doctoral Research Station of Xi'an Chan‐Ba National Ecological District, Xi'an, China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Yangling, China Correspondence Kun‐xia Yu and Peng Li, State Key Laboratory of Eco‐hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, China. Email:[email protected]; [email protected] Funding information National Natural Science Foundations of China, Grant/Award Numbers: 51509203, 51679184, 51779204 and 51879281; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Foundation, Grant/Award Number: SKL2018CG04

Abstract: With the increasing size and complexity of modern infrastructure networks rises the challenge of devising efficient and accurate methods for the reliability analysis of these systems. Special care must be taken in order to include any possible interdependencies between networks and to properly treat all uncertainties. This work presents a new approach for the reliability analysis of complex interconnected networks through Monte Carlo simulation and survival signature. Application of the survival signature is key in overcoming limitations imposed by classical analysis techniques and facilitating the inclusion of competing failure modes. The (inter)dependencies are modeled using vine copulas while the uncertainties are handled by applying probability boxes and imprecise copulas. The proposed method is tested on a complex scenario based on the IEEE reliability test system, proving its effectiveness and highlighting the ability to model complicated scenarios subject to a variety of dependent failure mechanisms.

Abstract: Monte Carlo tree search (MCTS) has been widely adopted in various game and planning problems. It can efficiently explore a search space with guided random sampling. In statistics, vine copulas are flexible multivariate dependence models that adopt vine structures, which are based on a hierarchy of trees to express conditional dependence, and bivariate copulas on the edges of the trees. The vine structure learning problem has been challenging due to the large search space. To tackle this problem, we propose a novel approach to learning vine structures using MCTS. The proposed method has significantly better performance over the existing methods under various experimental setups.

Abstract: We propose a family of goodness-of-fit tests for copulas. The tests use generalizations of the information matrix (IM) equality of White and so relate to the copula test proposed by Huang and Prokh...

Abstract: In Europe gas is sold according to two main methods: long-term contract (LTCs) and hub pricing. Europe is moving towards a mix of long term and spot markets, but the eventual outcome is still unknown. The fall of the European gas demand combined with the increase of the US shale gas exports and the rise of Liquefied Natural Gas availability on international markets have led to a reduction of the European gas hub prices. On the other side, oil-indexed LTCs failed to promptly adjust their positions, implying significant losses for European gas mid-streamers that asked for a re-negotiation of their existing contracts and obtained new contracts linked also to hub spot prices. The debate over the necessity of the oil-indexed pricing is still on-going. The supporters of the gas-indexation state that nowadays the European gas industry is mature enough to adopt hub-based pricing system. With the aim of analyzing this situation and determining whether oil-indexation can still be convenient for the European gas market, we consider both spot gas prices traded at the hub and oil-based commodities as possible underlyings of the LTCs. We investigates the dependence risk and the optimal resource allocation of the underlying assets of a gas LTC through pair-vine copulas and portfolio optimization methods with respect to five risk measures. Our results show that European LTCs will most likely remain indexed to oil-based commodities, even though a partial dependence on spot hub prices is conceded.

Abstract: Gardening products, like apple, are exposed to a variety of risks caused by unfavorable weather conditions. This kind of risk is unavoidable, but manageable. Agricultural insurance is an effective scheme in weather risk management. Nevertheless, current insurance schemes have challenges, such as high transaction costs, and problems caused by asymmetric information, i.e. adverse selection and moral hazard. Therefore, this study aimed to present an appropriate insurance scheme for apple production in Damavand, the so-called “weather-based index insurance”. In this regard, the information on apple yield and weather variables was collected between 1987-2016, from Iranian Agriculture Jihad Organization and the local meteorological station. The dependency structure between apple yield and weather variables was investigated by C-Vine Copula as a joint distribution to compute the expected loss. Then, according to the expected loss, weatherbased index insurance premium was measured. The premium amount was equal to Thousand Rials 32,546.11 in the crop year 2016-17, which is different from the current insurance premium. This difference is because of the distinct nature of the two insurance schemes and the imperative and official mode of current insurance scheme.

Abstract: Soft-sensing methods have been widely used in recent years to predict key variables that are difficult to measure or involve costly and time-consuming in chemical processes. Owing to the increasing complexity of industrial processes, industrial data often exhibit strong nonlinearities and self-correlation, and the data distribution fails to satisfy the Gaussian assumption. To address these problems, a vine copula-based soft-sensor model combined with the rolling pin method is proposed. This approach uses a D-vine copula to model a joint probability distribution of auxiliary variables and a key variable. In accordance with the joint distribution, the predicted value of the key variable can be determined by weighting the training samples. During modeling, the Bayesian information criterion is adopted to select the best-fitted copula pairs. Given the nonmonotonicity between variables of actual industrial data, the rolling pin monotonic transformation is simultaneously introduced to improve the suitability of...

Abstract: This paper presents an intuitive model for default dependencies in supply networks and its application in firms’ capital management. Modern supply chain networks are characterised by horizontal tie...