Portfolio Of Options Research Articles

Decision Science Can Help Address the Challenges of Long‐Term Planning in the Colorado River Basin

Abstract“Deep uncertainty” is a term that describes planning contexts in which it is impossible to determine the likelihood of any given set of future conditions, there are conflicting performance objectives and priorities, and decision outcomes are unpredictable. Evidence of the relevance of this term in the Colorado River Basin can be found in the unexpected severity and length of the ongoing drought, the existence of a wide range of future streamflow and demand projections and anticipated system requirements, as well as in the diverse range of stakeholders, viewpoints, and priorities that must be considered when developing and analyzing future operating policies. Decision Making under Deep Uncertainty (DMDU) is a branch of decision science devoted to developing planning approaches that can address these challenges. Reclamation began exploring DMDU techniques in 2012 in the Colorado River Basin Water Supply and Demand Study when it used the concepts of adaptation, vulnerability, and robustness in the design and evaluation of portfolios of options and strategies to address a potential supply‐demand imbalance in coming decades. This article presents the basis for continuing to explore DMDU techniques and briefly describes how Reclamation’s recent and ongoing DMDU studies could contribute to future planning efforts in the Colorado River Basin.

Estimating risks of option books using neural-SDE market models

In this paper, we examine the capacity of an arbitrage-free neural-SDE market model to produce realistic scenarios for the joint dynamics of multiple European options on a single underlying. We subsequently demonstrate its use as a risk simulation engine for option portfolios. Through backtesting analysis, we show that our models are more computationally efficient and accurate for evaluating the Value-at-Risk (VaR) of option portfolios, with better coverage performance and less procyclicality than standard filtered historical simulation approaches.

Hedging Option Books Using Neural-SDE Market Models

We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options. In particular, we derive sensitivity-based and minimum-variance-based hedging strategies using these models and examine their performance when applied to various option portfolios using real-world data. Through backtesting analysis over typical and stressed market periods, we show that neural-SDE market models achieve lower hedging errors than Black--Scholes delta and delta-vega hedging consistently over time, and are less sensitive to the tenor choice of hedging instruments. In addition, hedging using market models leads to similar performance to hedging using Heston models, while the former tends to be more robust during stressed market periods.

Option pricing under a discrete-time Markov switching stochastic volatility with co-jump model

<p style='text-indent:20px;'>We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can capture asset price features such as leptokurtosis, skewness, volatility clustering, and varying mean-reversion speed of volatility. For pricing European options, we develop a computationally efficient method for obtaining the probability distribution of average integrated variance (AIV), which is key to option pricing under stochastic-volatility-type models. Building upon the efficiency of the European option pricing approach, we are able to price an American-style option, by converting its pricing into the pricing of a portfolio of European options. Our work also provides constructive guidance for analyzing derivatives based on variance, e.g., the variance swap. Numerical results indicate our methods can be implemented very efficiently and accurately.</p>

Options Portfolio Selection with Position Limits

Options Portfolio Selection with Position Limits

Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model

This study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called “Quantile based neural network and model integration combination algorithm.” The algorithm effectively lowers the volatility prediction error (measured by root of mean square error, shorted for RMSE: 0.319724) and beat the Wavenet (RMSE: 0.44) which is the benchmark and surpasses integrated model (RMSE: 0.348346) in test set. In terms of option investment strategy, this paper constructs a CSI 300 index option portfolio which hedges the underlying asset price risk and exposes the volatility risk. Then propose the “Option strategy of volatility prediction with dynamic thresholds.” With the new algorithm above, the strategy improves the return‐risk ratio in test set (measured by Sharpe ratio: 1.99–2.07).

Sitting on the fence - Untangling the role of uncertainty in entrepreneurship and paid employment for hybrid entry

Prior research agrees that uncertainty in entrepreneurship shapes individuals' decision between hybrid and full-time entry, but largely neglects the role of uncertainty in paid employment. By theorizing that hybrid entrepreneurship is a portfolio of real options in entrepreneurship and paid employment, we argue that both uncertainty in entrepreneurship and paid employment as well as their interplay determine individuals' decisions regarding their mode of entry into entrepreneurship. We validate our theory using data from the Current Population Survey and the Study of Income and Program Participation and contribute to the hybrid entrepreneurship literature and research on uncertainty in entrepreneurship.

Performance measurement for option portfolios in a stochastic volatility framework

Measuring the performance of stock portfolios that include options is challenging due to options' nonlinearity in the underlying, their exposure to volatility risk, and their time decay. Our contribution to the literature is twofold: First, we provide a theoretically rigorous derivation of the time-variable factor loadings in a two-factor model under stochastic volatility according to [Heston, S.L., A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financ. Stud., 1993, 6, 327–343.]. Within this setting, the portfolio returns are explained by the market and an additional option factor, i.e. a portfolio of standard options exposed to volatility risk. We show that (i) any option factor is suitable to perfectly explain the portfolio behavior if simple returns are considered in instantaneous time and that (ii) the option factor's loading equals the fraction of the volatility elasticities of the portfolio and of the option factor while the option factor's underlying elasticity enters the factor loading of the underlying. Second, we analyze the behavior of option factors in practical applications, where time is discrete and factor loadings are estimated in a single regression over a certain time horizon. We show how the bias of (Jensen) alpha and its sign depend on the skewness of market returns. For several option factors from the literature, we conduct a simulation study to analyze their suitability to reduce this bias. As the results are disappointing, we propose a two-step procedure for choosing an adequate factor.

Software for Electronic Portfolio of Students of the Russian Higher Education

The purpose of the study. For Russian universities, the problem of choosing the form of the electronic portfolio and its software is relevant; ensuring the formation of the electronic portfolio of the student is fixed in the requirements for the electronic information and educational environment of the educational institution by the current federal state standards for higher education in Russia. The purpose of the study is to develop recommendations on the choice of software based on the generalization of the practice of using the author’s capabilities of foreign and domestic software to organize work on the management of the electronic portfolio by students. The study was made as another step in the author’s development of the conceptual apparatus of the electronic portfolio subject field.Materials and methods. The author of the study has been teaching and organizing the educational work of students with an electronic portfolio since 2007: from 2007 to 2019 at the Southern Institute of Management (first as the Head of the Department of Applied Informatics, then as Rector’s Assistant for informatization and electronic training), from 2019 at the North Caucasus branch of the Russian State University of Justice and Kuban State University as a lecturer of Informatics, Psychology and Pedagogy. Only in the 2020-2021 academic years, students of these two universities during the study of the author’s educational disciplines with cloud tools Google and P7 Office conducted about 400 electronic portfolios. The criteria for comparing the software of the electronic portfolio from different developers were elaborated on the basis of the author’s vision of the functionality of the electronic portfolio in Russian universities as a virtual office of students. To determine the forms of the electronic portfolio used by Russian higher education institutions and the software used by them, the author analyzed the documentation on the organization of the institution’s electronic environment posted on the official websites of universities in the country.Results. During the study of components of the structural model of the investigated problem, the author classified and considered portfolio software options and revealed that the most functional form of the electronic portfolio is an electronic portfolio in the form of an Internet site, designed and supported according to the “software as a service” model. The following criteria were added to the list of traditional criteria for comparing electronic portfolio software from different developers by the author: the availability (number) of training tools for performing training tasks with ecosystem tools, into which the portfolio is integrated, and the presence of registration in the Unified Register of Russian programs for electronic computers and databases. The author supplemented the recommendations on the choice of software for the portfolio of universities with the wishes of its developers and customers.Conclusion. A number of Russian universities continue their own development of electronic portfolio systems “from scratch” without taking into account the existing developments in culture. This circumstance, as well as the fact of the dependence of most universities on foreign technology platforms that ensure the maintenance of an electronic portfolio, prompted the author to show the possibility of creating an electronic portfolio system by modifying the P7 Office ecosystem of office applications, in accordance with the existing example of using Google’s online ecosystem tools for electronic portfolio management.

Harmonized comparison of virgin steel production using biomass with carbon capture and storage for negative emissions

A harmonized method was developed to assess CO2 mitigation performance and avoidance costs for different steel production routes, including blast furnace with blast oxygen furnace (BOF), direct reduced iron (DRI) production with electric arc furnace (EAF), and Hisarna with BOF. Mass and energy balances were used to evaluate each route's cradle-to-gate CO2eq emissions. Results indicate that using either CO2 capture and storage (CCS) or biomass can reduce, but not eliminate, CO2eq emissions in the iron and steel sector. However, the combination of CCS and biomass (BECCS) can result in CO2-neutral or even CO2-negative steelmaking. The results show that implementation of BECCS is possible at an avoidance cost < 100 €/t CO2eq. BECCS combined with carbon neutral electricity has a CO2 mitigation potential of 107–139% with CO2 avoidance costs of 59–157 €/t CO2eq. Reducing biomass upstream emissions could further improve these results. The Biomass-DRI-EAF-CCS route has the highest CO2 mitigation potential (146%), while the Biomass-Hisarna-BOF-CCS route has the lowest CO2 avoidance cost (54 €/t CO2eq). This study indicates that the developed harmonized methodology can also be applied in other industrial sectors to screen different portfolios of mitigation options.

Explainable neural network for pricing and universal static hedging of contingent claims

We present here a regress-later based Monte Carlo approach that uses neural networks for pricing multi-asset discretely-monitored contingent claims. The choice of specific architecture of the neural networks used in the proposed algorithm provides for interpretability of the model, a feature that is often desirable in the financial context. Specifically, the interpretation leads us to demonstrate that any discretely monitored contingent claim —possibly high-dimensional and path-dependent— under Markovian and no-arbitrage assumptions, can be semi-statically hedged using a portfolio of short maturity options. We also show, for Bermudan style derivatives, how the method can be used to obtain an upper and lower bound to the true price, where the lower bound is obtained by following a sub-optimal policy, while the upper bound is found by exploiting the dual formulation. Unlike other duality based upper bounds where one typically has to resort to nested simulation for constructing super-martingales, the martingales in the current approach come at no extra cost, without the need for any sub-simulations. We demonstrate through numerical examples the simplicity and efficiency of the method for both pricing and semi-static hedging of path-dependent options.

Regionalization of a national integrated energy system model: A case study of the northern Netherlands

Integrated energy system modeling tools predominantly focus on the (inter)national or local scales. The intermediate level is important from the perspective of regional policy making, particularly for identifying the potentials and constraints of various renewable resources. Additionally, distribution variations of economic and social sectors, such as housing, agriculture, industries, and energy infrastructure, foster regional energy demand differences. We used an existing optimization-based national integrated energy system model, Options Portfolio for Emission Reduction Assessment or OPERA, for our analysis. The modeling framework was subdivided into four major blocks: the economic structure, the built environment and industries, renewable energy potentials, and energy infrastructure, including district heating. Our scenario emphasized extensive use of intermittent renewables to achieve low greenhouse gas emissions. Our multi-node, regionalized model revealed the significant impacts of spatial parameters on the outputs of different technology options. Our case study was the northern region of the Netherlands. The region generated a significant amount of hydrogen (H2) from offshore wind, i.e. 620 Peta Joule (PJ), and transmitted a substantial volume of H2 (390 PJ) to the rest of the Netherlands. Additionally, the total renewable share in the primary energy mix of almost every northern region is ∼90% or more compared to ∼70% for the rest of the Netherlands. The results confirm the added value of regionalized modeling from the perspective of regional policy making as opposed to relying solely on national energy system models. Furthermore, we suggest that the regionalization of national models is an appropriate method to analyze regional energy systems.

Just and fair household energy transition in rural Latin American households: are we moving forward?

In this paper we conduct a scoping review of current household energy use patterns and trends in rural Latin America (LA), with the objective of identifying strategies that help promote just and fair transitions in the region. We reviewed a total of 143 publications covering 13 countries within the period from 1996 to 2021. The review shows: (a) fuelwood (FW) continues to be a very important, resilient—and in many countries—the dominant cooking fuel for rural LA households, both exclusively and increasingly stacked (combined) with liquefied petroleum gas (LPG); (b) FW is mostly used in open fires and rustic stoves, with a total toll of 59 000 premature deaths. Interventions have centered on the dissemination of improved woodburning chimney cookstoves and increasing access to LPG through top-down government programs. These programs have focused mostly on single-fuel and stove combinations, and on the number of devices installed with little or no follow-up with local users. As a result, success has been limited and open fires have not been fully displaced in most programs. We conclude that renewed efforts are needed to ensure a sustainable and just household energy transition in the LA region. These efforts should promote integrated portfolios of options including improved practices (drying wood, use of pressure cooker), and the stacking of devices (stoves, water heaters, space heating) and fuels (biomass, other). Specifically, improved chimney woodburning stoves need to be integrated with and be an important component of these programs. Programs should adopt a user-centered perspective, beginning with the understanding of users’ needs and priorities and tailoring solutions to their socio-environmental context. Innovation should be fostered through participatory methods, developing tests adapted to local circumstances and enforcing national standards. Implementation programs should focus on the adoption and sustained use of clean(er) devices and the displacement of traditional fires. Public policies should be more integrated and intersectoral seeking synergies between health, environmental, social development, and economic objectives.

Information gains from using short‐dated options for measuring and forecasting volatility

SummaryWe study the gains from using short‐dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak assumptions for the underlying asset. This volatility estimator complements existing ones constructed from high‐frequency returns. We show empirically, using the market index and Dow 30 stocks, that combining optimally return and option data can lead to nontrivial gains for volatility forecasting. These gains are due to “diversification” of the measurement error in the two volatility proxies. The information content of short‐dated options, not spanned by the current spot volatility, is of limited relevance for volatility forecasting.

Distributional Replication.

A function which transforms a continuous random variable such that it has a specified distribution is called a replicating function. We suppose that functions may be assigned a price, and study an optimization problem in which the cheapest approximation to a replicating function is sought. Under suitable regularity conditions, including a bound on the entropy of the set of candidate approximations, we show that the optimal approximation comes close to achieving distributional replication, and close to achieving the minimum cost among replicating functions. We discuss the relevance of our results to the financial literature on hedge fund replication; in this case, the optimal approximation corresponds to the cheapest portfolio of market index options which delivers the hedge fund return distribution.

Pandemic inspires pharmacogenomics telehealth pilot project.

Max Smith Before the COVID-19 pandemic, Clinical Pharmacogenomics Specialist D. Max Smith was a trusted resource for clinician colleagues at MedStar Health with questions about pharmacogenomics. Nowadays, Smith’s pharmacogenomics expertise is also available to some patients through a telehealth pilot project that’s part of MedStar Health’s growing portfolio of remote care options. “It wasn’t by design that I was going to be talking to patients directly. This service was initially designed to be a peer-to-peer consult service for providers,” Smith noted. “But . . . clinicians were reaching out and saying, ‘Hey, I need some help talking with my patients about pharmacogenomics and how this affects their care.’” MedStar Health operates 10 hospitals and more than 300 care sites in Maryland, Virginia, and Washington, DC. Smith is cochair of the organization’s Pharmacogenomics Steering Committee and Pharmacy and Therapeutics Pharmacogenomics Subcommittee. Smith completed a postgraduate year 2 residency program and postdoctoral fellowship in pharmacogenomics at the University of Florida before joining MedStar Health in 2018.

Mature offshore oil field development: Solving a real options problem using stochastic dual dynamic integer programming

Oil and gas companies are facing low output prices and are forced to focus on the development of mature fields. Relevant investment decisions for operators include lifetime-enhancing activities, such as drilling new wells or permanent shutdown. We study the problem of optimal timing of investments in mature oil and gas fields in the presence of price uncertainty, which is an example of a complex real options problem consisting of a portfolio of interdependent options. We formulate a multistage stochastic integer programming model that incorporates a detailed representation of the uncertain oil price, and demonstrate how such a complex real options problem can be efficiently solved using the Stochastic Dual Dynamic Integer Programming algorithm. The paper presents a numerical example based on realistic data and discusses our computational results. We find that only a small number of Markov states are required to represent the uncertain price process, while obtaining convergence of the lower and upper bounds of the objective function. The value of stochastic solution of 11% is considerable in this example. It is concluded that the shutdown decision tends to be postponed as a result of high decommissioning costs, high discount rates, high price uncertainty and low operational expenditures, while it generally is accelerated if the decommissioning costs increase over time.

Managing Crop Load in European Pear (Pyrus communis L.)—A Review

Reducing levels of fruit set is often desirable in many European pear (Pyrus communis L.) cultivars. With a negative linear relationship between crop load and fruit size, crop load management early in the season minimises wastage of tree carbohydrate resources and provides maximum benefits in terms of fruit size and quality. There are several tools available for managing crop load including hand thinning, chemical thinning, photosynthetic inhibition through shading or application of chemicals, mechanical thinning and pruning. While hand thinning is the most accurate method of reducing excessive crop loads, there are some major drawbacks. With awareness that the early thinning offered by chemical thinning provides distinct advantages with regard to fruit size and other quality parameters, chemical thinning is gaining increasing acceptance in pear production. Some chemicals are used worldwide for thinning, but there are differences between countries and growing regions on recommended application timing and concentrations. The risks involved in chemical thinning can be mitigated by use of a structured approach, using a sequential spray program with both bloom and post-bloom thinners. Knowledge of conditions that impact the carbon balance of the tree and the ability to make use of carbon-deficit conditions are likely to improve the predictability of chemical thinning. Mechanical thinning has potential as a thinning tool, with advantages over chemical thinning in that it is environmentally friendly, can be used in organic production and is not weather dependent. Although artificial bud extinction has not been trialled on pears to date, it has been shown to be economically viable in apple. As it is a precision crop load management method that minimises tree resource wastage, it should be given serious consideration. As growers require large annual yields of high-quality fruit, the aim of this review was to examine current and potential crop load management methods for European pear cultivars and provide a portfolio of available options that can be integrated into a systematic approach for managing crop load.

Carbon dioxide removal technologies are not born equal

Technologies for carbon dioxide removal (CDR) from the atmosphere have been recognized as an important part of limiting warming to well below 2 °C called for in the Paris Agreement. However, many scenarios so far rely on bioenergy in combination with carbon capture and storage as the only CDR technology. Various other options have been proposed, but have scarcely been taken up in an integrated assessment of mitigation pathways. In this study we analyze a comprehensive portfolio of CDR options in terms of their regional and temporal deployment patterns in climate change mitigation pathways and the resulting challenges. We show that any CDR option with sufficient potential can reduce the economic costs of achieving the 1.5 °C target substantially without increasing the temperature overshoot. CDR helps to reduce net CO2 emissions faster and achieve carbon neutrality earlier. The regional distribution of CDR deployment in cost-effective mitigation pathways depends on which options are available. If only enhanced weathering of rocks on croplands or re- and afforestation are available, Latin America and Asia cover nearly all of global CDR deployment. Besides fairness and sustainability concerns, such a regional concentration would require large international transfers and thus strong international institutions. In our study, the full portfolio scenario is the most balanced from a regional perspective. This indicates that different CDR options should be developed such that all regions can contribute according to their regional potentials.

Relative exploration orientation and real options reasoning: survey evidence from Denmark

PurposeThis paper aims to extend the understanding of how real options reasoning (ROR) is associated with downside risk and how a firm’s portfolio (explore and exploit) of investment activities affects managers’ ability to effectively apply ROR in relation to downside risk.Design/methodology/approachThe survey method is used. It is applied to a population of Danish firms, which in 2018 had more than 100 employees. The chief financial officer was the target respondent.FindingsThis study finds that a higher level of ROR is associated with lower levels of downside risk. ROR’s association with lower levels of the downside risk is also moderated by the level of relative exploration orientation in a negative direction.Originality/valueThe field of ROR research on downside risk and portfolio subadditivity has been dominated by research focused on multinationality. This paper extends extant literature on ROR by studying ROR as a multidimensional construct of firm action, which is associated with lower levels of downside risk, also when studied outside of a multinationality setting. This is the case when ROR is implemented as a complete system. This paper also applies a framework of exploitation and exploration to show that findings on subadditivity in options portfolios caused by asset correlations extend outside the scope of multinationality and into one of product/service innovation.