Chair: Johannes Gärttner, FZI, Germany
Extended Warranties with Residual Values
Guillermo Gallego (email@example.com), Ruxian Wang (firstname.lastname@example.org), Ming Hu (email@example.com), Julie Ward (firstname.lastname@example.org), Jose Luis Beltran (email@example.com)
Traditional extended warranties for IT products do not differentiate customers according to their risk attitudes, usage rates or operating environment. These warranties are priced to cover the costs of risk-averse and high-usage customers who are more risk-averse and tend to experience more failures. This makes traditional warranties economically unattractive to less risk-averse or low-usage customers. To address this issue, residual value warranties have been introduced in industry practice. These warranties refund a part of the upfront price to customers who have zero or few claims according to a pre-determined refund schedule. However, a warranty with residual values may induce strategic claim behavior as customers may prefer to repair small failures out of pocket rather than giving up potential refunds. Taking into account this strategic claim behavior as well as the risk attitude, we introduce, design and price residual value warranties. As customers may be heterogeneous in risk attitude, failure rate or repair cost, their willingness-to-pay and support cost may be different due to different failure realizations and different strategic claim behaviors even though they face the same residual value warranty with the uniform price and refunds. As a result, a residual value warranty enables the provider to price discriminate based on risk attitudes, usage rates or operating conditions without the need to monitor individual customers' magnitude of risk aversion or usage. We characterize customers' optimal claim strategy as well as the structure of the net value and support cost under the constant absolute risk aversion model. Surprisingly, the total support cost to the provider including repair cost and refund is lower for higher risk-averse customers under the residual value warranties, while the support cost is increasing in the risk attitude for the traditional warranties. Moreover, we identify conditions under which residual value warranties are strictly more profitable than traditional warranties.
Keywords: Risk aversion, strategic Behavior, refund warranty
Evaluating Price Risk Mitigation Strategies for an Oil and Gas Company
Antonio Quintino (firstname.lastname@example.org), João Carlos Lourenço (email@example.com), Maria Catalão-Lopes (firstname.lastname@example.org)
Financial hedging strategies are one of the preferred practices to protect oil and gas companies from prices’ volatility. The common approach consists in each business unit (e.g. crude exploration, natural gas, and refining) protecting itself against its own price risks. According to the “theory of syndicates” risk aggregation, and assuming that the risk tolerance assessment process is applied to every business unit, it is not clear whether separate hedging portfolio selections achieve a better risk protection than selection at company level. In this paper Copula-GARCH models are used to capture prices’ correlation and volatility, while business unit earnings are generated through Monte Carlo simulation. Optimal hedging portfolios are achieved with stochastic optimization over utility functions. We confront the business units’ portfolios through coherent risk measures against a portfolio for the whole company, which reveals to be the best option.
Keywords: Hedging, risk tolerance, portfolio optimization
Load Shifting, Interrupting or Both? Customer Portfolio Composition in Demand Side Management
Johannes Gärttner (email@example.com), Christoph Flath (firstname.lastname@example.org), Christof Weinhardt (email@example.com)
The share of renewable power sources in the electricity generation mix has seen enormous growth in recent years. Generation from fluctuating renewable energy sources (Wind, Solar) has to be considered stochastic and not (fully) controllable. To align demand with volatile supply, balancing and storage capacities have to be increased. To avoid high costs of storage investments, we suggest exploiting demand side flexibility instead. This can be operationalized through scheduling of electrical loads. Prior research typically assumes that both, the set of customers as well as the flexibility endowments of the scheduling problem are exogenously given. However, the quality of the scheduling result highly depends on the composition of the customer portfolio. Therefore, it should be designed in an optimal fashion. This includes two decisions: which customers should be part of the portfolio and how much flexibility each customer should offer. Thus, future energy retailers face a complicated decision-making problem. We present a portfolio design optimization model which includes both, selecting customers to be part of the portfolio and scheduling their flexibility. Furthermore, we present exemplary results from an scenario based on empirical load and generation data.
Keywords: Portfolio Optimization, Demand Side Management, Load Flexibility