Conditional Value At Risk Approach Research Articles

Identifying Systemically Important Banks in Indonesia: CoVaR Approach

The Global Financial Crisis of 2008 had a significant impact on banks' bailout decisions due to its high cost of Rp. 6.76 trillion (USD 499.5 million). That shifted the focus from analyzing bank size to a broader notion of systemic risk. Therefore, this research aims to systemically identify important banks in Indonesia using Conditional Value-at-Risk (CoVaR) approach. We conduct the following three steps measurement for population-based on all commercial banks data. Firstly, this study uses Merton Model to gauge the probability of default of commercial banks. Secondly, this study quantifies the value at risk of each bank, including its contribution to the whole banking systemic risk. Finally, this study measures financial linkage among banks and the individual bank value at risk contribution, conditional on other banks being in financial distress, i.e., at its Value at Risk (ΔCoVaR A|B). A threshold of 20% ΔCoVaR (A|B) was used to determine 12 out of 119 Indonesian commercial banks that are systemically important and the size ability to affect CoVaR positively. The novelty of the research is to integrate Probability of Default derived from the Merton Model into the CoVaR Model developed by Adrian & Brunnermeier.

Measuring systemic risk with a dynamic copula-based approach

ABSTRACT This study examines the extent of systemic risk embedded in the credit and equity markets using a conditional value-at-risk (CoVaR) measure. We implement a copula-based CoVaR approach with different perspectives of a dependence structure based on a generalized autoregressive score model. In parallel, we select the credit default swap spread and stock price data of five companies in the financial sector – American Express, BBVA, Goldman Sachs, Morgan Stanley, and Wells Fargo – from 2001 to 2013, and include data on the global financial crisis of 2007–2008. We then divide the data into three time periods: pre-crisis, during the crisis, and post-crisis. We conduct time-varying marginal modelling, and copula parameter estimation, and then compute CoVaR values with the best-fit copula model. Comparative empirical tests provide financial implications for systemic risk management.

A new optimization algorithm for HDR brachytherapy that improves DVH-based planning: Truncated Conditional Value-at-Risk (TCVaR)

Purpose: To introduce a new optimization algorithm that improves DVH results and is designed for the type of heterogeneous dose distributions that occur in brachytherapy. Methods: The new optimization algorithm is based on a prior mathematical approach that uses mean doses of the DVH metric tails. The prior mean dose approach is referred to as conditional value-at-risk (CVaR), and unfortunately produces noticeably worse DVH metric results than gradient-based approaches. We have improved upon the CVaR approach, using the so-called Truncated CVaR (TCVaR), by excluding the hottest or coldest voxels in the structure from the calculations of the mean dose of the tail. Our approach applies an iterative sequence of convex approximations to improve the selection of the excluded voxels. Data Envelopment Analysis was used to quantify the sensitivity of TCVaR results to parameter choice and to compare the quality of a library of 256 TCVaR plans created for each of prostate, breast, and cervix treatment sites with commercially-generated plans. Results: In terms of traditional DVH metrics, TCVaR outperformed CVaR and the improvements increased monotonically as more iterations were used to identify and exclude the hottest/coldest voxels from the optimization problem. TCVaR also outperformed the Eclipse-Brachyvision TPS, with an improvement in PTVD95% (for equivalent organ-at-risk doses) of up to 5% (prostate), 3% (breast), and 1% (cervix). Conclusions: A novel optimization algorithm for HDR treatment planning produced plans with superior DVH metrics compared with a prior convex optimization algorithm as well as Eclipse-Brachyvision. The algorithm is computationally efficient and has potential applications as a primary optimization algorithm or quality assurance for existing optimization approaches.

A conditional value-at-risk-based dispatch approach for the energy management of smart buildings with HVAC systems

The urgent need for low-carbon societies calls for efficient energy management of smart buildings. This paper proposes a two-stage conditional value-at-risk (CVaR) model to determine the optimal day-ahead dispatch of smart buildings with heating, ventilation, and air-conditioning (HVAC) systems. A detailed physical model of HVAC systems with realistic working conditions is established. In the two-stage model, the first stage dispatch is to minimize the electricity consumption one day in advance and the second stage is to reduce power exchange fluctuations with external grids in the real-time dispatch. Besides, uncertainties in both photovoltaic power output and outdoor temperature are accommodated by the CVaR approach. Finally, the numerical results of an actual smart building validate the effectiveness and economy of the proposed approach.

Discovering Systemic Risks of China's Listed Banks by CoVaR Approach in the Digital Economy Era

The world has entered the digital economy era. As a developing country, China's banking industry plays an important role in the financial industry, and its size ranks first in the world. Therefore, it is of great significance to study the systemic risks of China's banks in the digital economy era. We first compare the traditional indicator approach and the market-based approach theoretically, and Conditional Value at Risk (CoVaR) model, a market-based approach, is considered to be an efficient way to discover systemic risk in different perspectives. Based on static and dynamic models, we evaluate the contributions of sixteen China's listed banks to the systemic risk. Furthermore, we model bank exposures, extend the models by considering extreme circumstance, and incorporate the effects of Fintech and non-bank financial institutions. The results show the levels of systemic risks and the corresponding systemic importance rankings vary in different time periods. We find that the contributions of some small banks to systemic risk are even higher than some big banks during the sample period. Moreover, the big banks face less risks than most of the small banks when the banking system is in distress. We make suggestions for improving financial supervision and maintaining financial stability.

Bank systemic risk and CEO overconfidence

This study examines the relationship between CEO overconfidence and banking systemic risk. We employ the CoVaR (Conditional Value-at-Risk) approach to measure a bank's contribution to systemic risk and compute its MES (Marginal Expected Shortfall) and SRISK (Systemic Risk index) to measure the exposure to banking systemic risk. We use a stock options based measure for CEO overconfidence and explore how managerial overconfidence could be associated with banking systemic risk. Using data for U.S. banks from 1995–2014, we find evidence that banks with overconfident CEOs have a higher contribution and exposure to systemic risk than banks with non-overconfident CEOs. We also show that the impact of CEO overconfidence contributed significantly more to systemic risk during the financial crisis of 2008–2009.

Resilient Scheduling Portfolio of Residential Devices and Plug-In Electric Vehicle by Minimizing Conditional Value at Risk

Home energy management systems (HEMSs) encourage participation of residential consumers into the demand response programs. This paper proposes a robust Conditional Value at Risk (CVaR) optimization approach for day ahead HEMS to reduce the effect of risk of real-time exposure to energy price and solar power generation uncertainties. Initially, the CVaR method is integrated with the two-point Estimation (2PE) analysis to approximate the solar power, modeled as Beta probability distribution function, in low computation effort compared to conventional Monte Carlo simulation (MCS) based CVaR approach. Then the optimization constraints are revised to their robust counterparts by accounting a certain amount of uncertainty in the energy prices from their nominal values. Unlike the previous literatures, the optimization problem is developed to minimize the risk value of the energy cost. Again to maximize the life of the plug-in electric vehicle (PEV) a pseudo cost function for the PEV battery degradation is proposed. The entire optimization portfolio is developed as a mixed integer linear programming for its easy execution. Simulation is demonstrated on a smart home, designed as an ac-dc microgrid, having practical appliance data sets, to prove the efficacy of the proposed method.

Risk Transmission between Chinese and U.S. Agricultural Commodity Futures Markets—A CoVaR Approach

Commodity futures markets play an important role, through risk management and price discovery, in helping firms make sustainable production and marketing decisions. An important related issue is how pricing signals between futures exchanges impact traders’ risk. We address this issue by shedding light on risk transmission between the most mature (U.S.) and the fastest growing (Chinese) commodity futures markets. Gaining greater insight of risk transmission between these key markets is vitally important to firms engaged in the efficient and sustainable trade of commodities needed to feed the world. We examine the risk transmission between Chinese and U.S. agricultural futures markets for soybean, corn, and sugar with a Copula based conditional value at risk (CoVaR) approach. We find significant upside, and to a lesser extent downside risk transmission, between Chinese and U.S. markets. We confirm the dominant pricing role of U.S. agricultural futures markets while acknowledging the increasing price discovery role performed by Chinese markets. Our results highlight that soybean markets exhibit greater risk transmission than sugar and corn markets. We argue that our findings may be explained by Chinese government policy intervention, and by the large role played by U.S. firms in the underlying cash commodity markets–both in terms of production and trade.

Inventory optimization of airport perishable emergency supplies with replacement strategy facing stochastic occurrence time by CVaR approach

Due to difficulties in accurately predicting not only the timing and magnitude of civil aviation disasters but also dealing with the serious expiration problems that exist in perishable emergency materials, this study combines the development of risk-averse stochastic models with a material replacement strategy to design and develop a perishable emergency supplies inventory system for airports. In this regard, a lifetime-based replacement strategy and a quantity-based replacement strategy are derived from the uncertainties on occurrence time and demand quantity. As part of our model development, we determine the conditional value at risk (CVaR) to be our primary means of evaluating risk, while the issues of emergency demand and aviation accident occurrence time form the two stochastic parameters used in the study. The effective boundary conditions of both replacement mechanisms and the CVaR method is demonstrated through a comparison of risk-neutral and non-replacement models. From our analysis, we find that airports are able to derive significant benefits by utilizing our proposed replacement strategies, with the risk-averse models providing a relatively efficient means from which more robust solutions may be obtained. Moreover, we find that the effects of uncertainty in occurrence time is also not linear for CVaR models.

A CVaR Scenario-Based Framework for Minimizing Downside Risk in Multi-Asset Class Portfolios

Multi-asset class (MAC) portfolios can be composed of investments in equities, fixed income, commodities, foreign exchange, credit, derivatives, and alternatives such as real estate and private equity. The return for such nonlinear portfolios is asymmetric with significant tail risk. The traditional Markowitz mean–variance optimization (MVO) framework, which linearizes all the assets in the portfolio and uses the standard deviation of return as a measure of risk, does not always accurately measure the risk for such portfolios. The authors consider a scenario-based conditional value at risk (CVaR) approach for minimizing the downside risk of an existing portfolio with MAC overlays. The approach consists of two phases: Phase 1 uses Monte-Carlo simulations to generate the asset return scenarios, and Phase 2 incorporates the return scenarios in a scenario-based convex optimization model to generate the overlay holdings. The authors illustrate the methodology in two examples involving the hedging of an equity portfolio with index puts and the hedging of a callable bond portfolio with interest rate caps. They compare the CVaR approach with parametric MVO approaches that linearize all the instruments in the MAC portfolio and show that the CVaR approach generates portfolios with better downside risk statistics; and further, it selects hedges that produce more attractive risk decompositions and stress test numbers—tools commonly used by risk managers to evaluate the quality of hedges.

A risk-based multi-objective model for optimal placement of sensors in water distribution system

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation–optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value of losses in WDS.

Optimal ordering policies in a multi-sourcing supply chain with supply and demand disruptions-a CVaR approach

In this study, we propose a conditional value at risk (CVaR) model for supply chain disruptions planning of a multi-agent, multi-product supply chain subject to supply and demand disruptions. Our focus is on building and comparing ordering policies under CVaR and expected cost criteria. The proposed formulation is illustrated through some numerical instances. The results present that the CVaR model shows a considerable difference in response policies compared to the expected cost model. Ordering quantities in response to supply and demand disruption are lower in the CVaR model than the expected cost model. In many instances, it is also seen that ordering quantities in response to disruptions tend to become lower when a decision maker becomes more risk-averse. It is expected that the proposed CVaR model would outperform to optimise the supply chain of an organisation, in particular, for the purpose of reducing the risk of high cost.

Systemic risk of Spanish listed banks: a vine copula CoVaR approach

ABSTACTWe measured the systemic impact of financial distress in a Spanish listed bank on other listed banks and on the European financial system, using conditional value at risk (CoVaR) as a systemic risk measure. We modelled multivariate dependence between listed banks using a hierarchical tree structure given by a vine copula model and, using bivariate copulas, dependence between listed banks and the European financial system. For the period January 2003 to March 2015, systemic risk dramatically increased around the time of the recent global financial crisis and, to a lesser extent, around the time of the European debt crisis. BBVA played a predominant role as it both transmitted and received systemic risk to and from the remaining listed banks. Santander played a minor role and the smallest banks, Sabadell and Bankinter, did not play any pivotal role, not even between themselves. Finally, the main systemic impact of the Spanish banks on the European financial systems originated in BBVA, Popular and Santander, with the other listed banks playing a minor role in risk transmission. These results have implications for the regulation of capital in financial institutions and for investor risk management decisions.

Systemic Risk and Government Intervention During Financial Crisis: A CoVaR Approach

This paper examines the effects of a regulator's crisis management on systemic risk as measured by the delta conditional value-at-risk (CoVaR) during the financial crisis in Japan. We evaluate the various management measures primarily in terms of their liquidity provision/capital infusion effects as well as adverse contagion effects. The findings of the study generally support evidence for the liquidity provision/capital infusion effect, but favor the adverse contagion effect for public fund injection programs with multiple recipients. In addition, the management restrictions accompanying injection programs or the moral hazard with failure resolution might have aggravated the systemic risk. Furthermore, although we confirm that the average systemic risk of the largest banks is higher than that of other banks, we do not necessarily observe the amplified effects of the former on the latter. Lastly, crisis management does not effectively work for the systemic tail risk.

Robust Optimization of Order Execution

Order execution for algorithmic trading has been studied in the literature to determine the optimal strategy by minimizing a trade-off between expected execution cost and risk. Usually, the variance of the execution cost is taken as a proxy of risk due to mathematical tractability. However, the variance has been recognized not to be practical since it is a symmetric measure of risk and, hence, penalizes the low-cost events. In this paper, we propose the use of the conditional value-at-risk (CVaR) of the execution cost as risk measure, which allows to take into consideration only the unfavorable part of the return distribution, or, equivalently, unwanted high cost. In addition, due to the parameter estimation errors in the price model, the naive strategies given by the nominal problem may perform badly in the real market, and hence it is extremely important to take such parameters estimation errors into consideration. To deal with this, we extend both the traditional mean-variance approach and our proposed CVaR approach to their robust design counterparts.

Optimizing Critical Spare Parts and Location Based on the Conditional Value-At-Risk Criterion

This article considers the problem of managing the risks associated with random equipment failures by optimizing decisions regarding the quantity and placement of critical spares over a network of related industrial sites. To develop the model and provide a practical example, we focus on the allocation of electrical transformer spares for a large-scale industrial producer, such as a mining company or chemical manufacturer, operating several different sites across a geographic region. In particular, we consider the risk of financial loss due to interrupted business and lost production following an unexpected transformer failure. A two-stage stochastic integer programming model with a conditional value-at-risk (CVaR) criterion to incorporate risk aversion is developed. Computational results are presented to illustrate the advantages of the CVaR approach compared to a corresponding expected cost minimization approach. The CVaR model results in policies that have lower loss than the corresponding risk neutral model since, at sufficiently high risk aversion levels, the CVaR model introduces the acquisition of more spares as a hedge against catastrophic scenarios.

Newsvendor problem with random shortage cost under a risk criterion

We study profit maximization vs risk approaches for the standard newsvendor problem with uncertainty in demand as well as a generalized version with uncertainty in the shortage cost (as often applies in practice). We consider two well-known risk approaches: Value-at-Risk (VaR) included as a constraint and Conditional Value-at-Risk (CVaR). We first derive the explicit expressions of the optimal solution with uncertainty of shortage cost under different risk measures and then perform a numerical analysis to quantify the effect of uncertainty in shortage cost and risk measures. For the standard newsvendor problem, we find that the optimal quantity under CVaR is always lower than that under the VaR constraint, which in turn is lower than the order quantity that maximizes the expected profit. Insightful explanations for this result are that: (a) a higher degree of risk aversion drives the newsvendor to order fewer products, increasing the likelihood that all will be sold; (b) this effect is stronger for the CVaR approach as this does not consider the expected profit at all. Another interesting and counter-intuitive observation for the CVaR approach is that a higher retail price may lead to a smaller order quantity, as fewer items need to be sold in order to attain a sufficient profit. These results show that one should be careful in employing the CVaR risk measure for newsvendor type problems. The results remain valid if the shortage cost becomes uncertain. However, increased uncertainty of this type does improve the relative profitability under the CVaR approach by increasing the order quantity under that criterion whilst there is no effect under the other criteria.

Financial and operational decisions in the electricity sector: Contract portfolio optimization with the conditional value-at-risk criterion

The restructuring of electricity markets around the world have caused increased volatility and uncertainty of the price power. As a result, providers of power now face increased uncertainty and risk in the operational and financial decisions related to procurement. Providers must seek optimal ways to deliver the required volume of power to retailers and end users while managing risk. We consider a mixed-integer programming model for a power providing agent that jointly considers the problem of selecting custom electricity contracts and finding the optimal procurement strategy of meeting contract obligations under spot price uncertainty. A two-stage stochastic integer programming (SIP) model with a conditional value-at-risk (CVaR) constraint to incorporate risk aversion is developed. Computational results are presented that demonstrates the CVaR approach and the results are compared with a corresponding expected cost minimization approach. The SIP model with CVaR will allow acceptance of contracts at lower prices compared to an approach based on a corresponding risk-neutral model as a hedge against uncertainty and mis-specified arbitrage.

Risk Management in the Oil Supply Chain: A CVaR Approach

This study analyzes the strategic planning of an oil supply chain. To optimize this chain, a two-stage stochastic model with fixed recourse and incorporation of risk management was developed. The model took a scenario-based approach and addressed three sources of uncertainty. To deal with these uncertainties, the conditional value-at-risk (CVaR) was adopted as a risk measure, and then the model was applied to the supply chain of six oil refineries. The goal of the study was to maximize the expected net present value, E(NPV), of the supply chain under analysis. The results indicate that the optimization of the several scenarios yielded an E(NPV) variation that reached US$ 36 million. Such a significant difference demonstrates that taking uncertainties into consideration is a fundamental step in decision-making processes.

Generation self-scheduling with partial information on the probability distribution of prices

Recent research has shown that generation self-scheduling in electricity markets can be approached using conditional value-at-risk (CVaR). This study considers a worst-case CVaR methodology applicable to cases where only partial information on the underlying probability distribution of prices is given. In particular, the probability distribution is considered under box and ellipsoidal uncertainty structures. It is shown that both structures result in self-scheduling problems that can be formulated as a quadratic cone program. The cone program can be used to (i) compute the worst-case conditional robust profit with probability level β and (ii) optimise the self-schedule for a pre-specified probability β of the corresponding worst-case conditional robust profit. Simulation results are used to demonstrate the self-scheduling model based on the worst-case CVaR. The usefulness of the proposed model is established by contrasting it with the CVaR approach.