Portfolio Of Options Research Articles

Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector

Carbon dioxide removal technologies, such as bioenergy with carbon capture and direct air capture, are valuable for stringent climate targets. Previous work has examined implications of carbon removal, primarily bioenergy-based technologies using integrated assessment models, but not investigated the effects of a portfolio of removal options on power systems in detail. Here, we explore impacts of carbon removal technologies on electric sector investments, costs, and emissions using a detailed capacity planning and dispatch model with hourly resolution. We show that adding carbon removal to a mix of low-carbon generation technologies lowers the costs of deep decarbonization. Changes to system costs and investments from including carbon removal are larger as policy ambition increases, reducing the dependence on technologies like advanced nuclear and long-duration storage. Bioenergy with carbon capture is selected for net-zero electric sector emissions targets, but direct air capture deployment increases as biomass supply costs rise.

Option Return Predictability

Abstract We uncover new return predictability in the cross-section of delta-hedged equity options. Expected returns to writing delta-hedged calls are negatively correlated with stock price, profit margin, and firm profitability, but positively correlated with cash holding, cash flow variance, new shares issuance, total external financing, distress risk, and dispersion of analysts’ forecasts. Our option portfolio strategies have annual Sharpe ratio above two and remain profitable after transaction costs. Their profits can be explained by two option factors, while equity risk factors have no explanatory power. We find support for several economic channels at work, yet the option return predictability remains puzzling.

American Basket Option Pricing Formulas in Uncertain Environment

American basket option is a contract containing multiple underlying assets, and its payoff is correlated with average prices or weighted average prices of these assets on or before the expiration date. The type of option entitles a holder the right to trade at the strike price within a specified date, and this right can be waived. Therefore, there is a certain price to be paid for acquiring this right, which produces the problem of option pricing. A lot of literature shows that basket option price is usually cheaper than option portfolios on individual underlying assets. Based on this advantage, basket option becomes popular among investors. Consequently, this paper predominantly explores four types of American basket option pricing in uncertain financial environment. Specifically they are American arithmetic basket call option, American arithmetic basket put option, American geometric basket call option and American geometric basket put option. Assuming that these stocks prices follow corresponding uncertain differential equations, we derive corresponding option pricing formulas. Some numerical examples are taken to illustrate the feasibility of pricing formulas. Simultaneously, this paper discusses the relationship between option price and some parameters.

Practical Applications of Deep Reinforcement Learning for Option Replication and Hedging

If a computer model using machine learning and artificial intelligence like Google’s DeepMind can beat the world’s best human player of the ancient Chinese game of “Go,” can a similar approach help solve the challenge of hedging and replicating option portfolios? <b><i>Deep Reinforcement Learning for Option Replication and Hedging</i></b>, from the Fall 2020 issue of <b><i>The Journal of Financial Data Science</i></b>, takes a “deep” dive into reinforcement learning approaches for option portfolios. It explores ways of training a computer “agent” by a clever form of trial and error to learn to replicate and hedge an option portfolio directly from data. It’s a positive step forward in the use of modern mathematical approaches to portfolio hedging, and the authors are extending their research in deep reinforcement learning (DRL) to real-world problems in finance, including trading, portfolio management, and hedging. <b>TOPICS:</b>Big data/machine learning, options, risk management, simulations

Use and access in the new ecology of public messaging

AbstractThe use of social media and other communication technologies have created a new ecology of public messaging. As it is a core task of government to inform its residents about risks, public managers and emergency managers, specifically, must understand this new ecology if they are to effectively communicate with the public. A challenge of this new media environment is the differential access of members of the community to various technologies. Partial proportional odds regression (PPO) provides a strategy that is useful to separate effects of access from effects of use. This article illustrates the use of PPO regression to separate access and use effects based on a survey which followed a series of severe weather events in the spring of 2016. The survey includes an address‐based sample of residents in the state of Oklahoma to ask about the use of various communication technologies to share information about the weather system (among other subjects). We find that age and work status are related to access while income, gender, race and exposure to extreme weather are related to use of various communication media. This information provides emergency managers with a stronger foundation for developing a portfolio of information options for their communities.

Influence of the geographical environment on Islamic banking performance

The main goal of this study is to examine the influence of the geographic environment on the performance of Islamic banks present in four regions: Africa, Asia, Europe and North America. To achieve this goal, we have used daily data from 113 Islamic banks between 2010 and 2019. We applied different methodological approaches, such as principal component analysis and quantile regression with fixed effects for panel data. As a result, we found that the analysis of the main components shows that the performance of Islamic banks varies between regions. The regression of the linear panel highlights that the geographic environment positively and significantly affects the Islamic banking system, suggesting the importance of this aspect’s effects. Finally, the environmental effect seems to vary with the quantiles, bringing positive effect to the lower quantile and negative effect to the highest quantile. This quantile specification points to the environment-performance nonlinear relationship of the Islamic bank, reflecting a discipline variable in the time imposed by the Council of Shariah or Islamic Law. This discovery helps to better explain the main difference between Islamic banks in the East and those in the West, and also allows investors to adjust their portfolio options when considering the products of Islamic banks according to regional specificities.

Practical Applications of Being Right Is Not Enough: Buying Options to Bet on Higher Realized Volatility

Practical Applications In Being Right Is Not Enough: Buying Options to Bet on Higher Realized Volatility, published in the Fall 2020 issue of The Journal of Alternative Investments, Roni Israelov, of NDVR Capital Management, and Harsha Tummala, of AQR Capital Management, illustrate that it is not easy to profit from long equity option positions. They do so by creating delta-hedged option portfolios and examining the returns to the resulting long-volatility strategy. The authors find that traders must not only correctly predict that volatility will increase during their holding period, but also must successfully time their entries and exits. Profitably accomplishing these tasks is particularly difficult due to the well-documented volatility risk premium (VRP) that usually accrues to option writers. The authors analyze the following measures and their relationships: the percentage change in realized volatility of the underlying asset (equity indexes), the ex post VRP, and long option portfolio returns levels. They also examine the usefulness of using the strategy to hedge equity losses. Additionally, they perform an event study to investigate the timing effects of entering and exiting long volatility positions. TOPICS:Options, performance measurement

What Incentivizes Managers to Engage in Financial Misreporting? An Analysis of Stock Delta, Option Delta, and Vega

Previous studies have provided mixed evidence on the relation between managers’ equity incentives and financial misstatements. These studies often measure equity incentives with the sensitivity of a manager’s stock and option portfolio to changes in equity price (i.e., portfolio delta) and/or the sensitivity of her option portfolio to changes in equity risk (i.e., vega). We hypothesize and find that the mixed results are attributable to the measurement error in portfolio delta because option compensation causes a manager’s incentive structure to be convex while stock compensation makes it linear. Splitting the portfolio delta into stock delta and option delta, we provide evidence that option delta has a dominating effect over stock delta and vega in explaining the likelihood and extent of financial misstatements. We also find that the positive association between option delta and misreporting variables is mainly driven by the effect of CEO’s option delta rather than that of CFO’s option delta. Our findings contribute to the literature by revealing the differential effects that stock delta, option delta, and vega have on providing managers with incentives for financial misreporting.

Portfolio optimization under safety first expected utility with nonlinear probability distortion

In this paper, we study a portfolio selection problem under safety first expected utility model with distortion (SFEUD model) where the underlying probability scale is transformed by a nonlinear distortion function. By employing the quantile formulation and the relaxation method, we obtain the general form of optimal solution to the problem, and the necessary and sufficient condition for existing such an optimal solution is also proved. We further demonstrate an analytical solution to the optimal strategies under four different monotonic cases. What’s more, the optimal terminal wealth process is replicated into a portfolio of options and deposits under specific circumstances. In addition, we empirically verify the model implication and figure out that investors’ realization of disaster risk can explain the price change of call options to some degree.

Debate: Quantity of impatient beds and quality of child psychiatric and psychotherapeutic care provision - a German perspective.

Provision of care for children with mental health disorders or behavioural difficulties differs from country to country. Historically, Germany has the highest number of inpatient beds throughout Europe (64 inpatient beds per 100,000 young people). In addition, nearly 146 departments for child and adolescent psychiatry offer day-care beds as well as ambulatory care. A high number of resident child and adolescent psychiatrists/psychotherapists (more than 12,000 professionals) complement provision of care in the different regions of Germany. Yet, only 50% of children and adolescents with mental health problems receive the treatment they need. Barriers to care - for example lack of transportation, fear of stigmatization, long distances in rural regions or fear of long hospital inpatient stays - keep families from presenting their child/adolescent to the relevant institutions. Recently, a new treatment module: StäB, an intensive daily home treatment, delivered by a multiprofessional team, has been added to the portfolio of treatment options in Germany. This closes the gap between highly intensive inpatient care and low-frequency outpatient treatment, allowing a continuum of care in intensity and frequency within the treatment alternatives.

A data-driven deep learning approach for options market making

We develop a data-driven approach for options market making. Using stock options data from CBOE, we find that both buy and sell orders exhibit strong self-excitation but insignificant cross-excitation. We show that a Hawkes process with a time-varying baseline intensity and the power law kernel provides a good fit to the data of market order flows for stock options. To solve the optimal market making problem for a single option, we approximate the market making strategy at each decision time by a neural network and train them to optimize the expected utility of the market maker. We study feature selection for the neural networks and compare the out-of-sample performance of the optimal neural network strategy trained from data generated by the Hawkes process and the Poisson process. We find that using the more realistic Hawkes model improves the out-of-sample performance significantly. Furthermore, utilizing the Hawkes process intensity or the expected number of market order arrivals computed under the Hawkes model as an additional input feature can boost the performance. We also show how to solve the market making problem for option portfolios with Greeks and inventory constraints using neural network approximation.

Pricing the exotic: Path-dependent American options with stochastic barriers

Abstract We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate ratchet, Asian, and barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, compared to the traditional Monte Carlo simulations method, we highlight that our estimation’s prediction is more accurate and requires less than 1% of the computational time.

Revisiting the Functional Zoning Concept under Climate Change to Expand the Portfolio of Adaptation Options

Climate change is threatening our ability to manage forest ecosystems sustainably. Despite strong consensus on the need for a broad portfolio of options to face this challenge, diversified management options have yet to be widely implemented. Inspired by functional zoning, a concept aimed at optimizing biodiversity conservation and wood production in multiple-use forest landscapes, we present a portfolio of management options that intersects management objectives with forest vulnerability to better address the wide range of goals inherent to forest management under climate change. Using this approach, we illustrate how different adaptation options could be implemented when faced with impacts related to climate change and its uncertainty. These options range from establishing ecological reserves in climatic refuges, where self-organizing ecological processes can result in resilient forests, to intensive plantation silviculture that could ensure a stable wood supply in an uncertain future. While adaptation measures in forests that are less vulnerable correspond to the traditional functional zoning management objectives, forests with higher vulnerability might be candidates for transformative measures as they may be more susceptible to abrupt changes in structure and composition. To illustrate how this portfolio of management options could be applied, we present a theoretical case study for the eastern boreal forest of Canada. Even if these options are supported by solid evidence, their implementation across the landscape may present some challenges and will require good communication among stakeholders and with the public.

Equity Market Integration and Portfolio Decisions: A Study of NASDAQ USA and MSCI Emerging Markets Asia Indexes

This article studies market integration among the MSCI Emerging Markets Asia Index and the NASDAQ Composite Index for the United States of America (USA) with respect to the 2008 Global Financial Crisis. The study uses a three-level methodology based on short-term integration, long-term integration, and portfolio decisions. The correlation and Granger causality find short-term integration among the majority of the markets. According to the results of the cointegration test and value-at-risk (VAR) analysis, long-term integration exists among the markets in the post–Global Financial Crisis period. The outcome of the portfolio risk-return analysis indicates that investors have many portfolio options where investment diversification can yield better returns and lower levels of risk compared to investing only in the Indian market. Investors can make portfolio diversification decisions based on the integration among markets. Investors can generate better Sharpe ratios with diversification in both the pre-crisis and post-crisis periods. The government and policymakers can apply this information to draft decisions on monetary and economic policies. In addition, multinational companies can design their investment policies after considering integration among their markets. <b>TOPICS:</b>Mutual funds/passive investing/indexing, statistical methods, emerging markets, VAR and use of alternative risk measures of trading risk <b>Key Findings</b> ▪ The markets have become more integrated after the Global Financial Crisis. ▪ Investment in an international market is better than investing only in the home market. ▪ Investors can diversify their funds to other markets as the diversification can result in a better Sharpe ratio.

Therapist-Led, Internet-Delivered Treatment for Early Child Social Anxiety: A Waitlist-Controlled Evaluation of the iCALM Telehealth Program

Despite recent advances in the treatment of early child social anxiety, the broad accessibility of brick-and-mortar services has been limited by traditional barriers to care, and more recently by new obstacles related to efforts to slow the spread of COVID-19. The present waitlist-controlled trial examined the preliminary efficacy of a family-based behavioral parenting intervention (i.e., the iCALM Telehealth Program) that draws on Parent-Child Interaction Therapy and videoconferencing to remotely deliver clinician-led care for anxiety in early childhood. Young children (3–8 years) with a diagnosis of social anxiety disorder (N = 40; 65% from ethnic/racial minority backgrounds) were randomly assigned to iCALM or waitlist. Intent-to-treat analyses found that at post, independent evaluators classified roughly half of the iCALM-treated children, but only 6% of waitlist children, as “Responders” (Wald test = 4.51; p = .03). By Post, iCALM led to significantly greater reductions than waitlist in child anxiety symptoms, fear, discomfort, and anxiety-related social impairment, and also led to greater improvements in child soothability. By 6-month follow-up, the percentage of iCALM-treated children classified as “Responders” rose to roughly 60%. Exploratory moderation tests found iCALM was particularly effective in reducing life impairments and parental distress among families presenting with higher, relative to lower, levels of baseline parental accommodation. The present findings add to a growing body of research supporting the promise of technology-based strategies for broadening the portfolio of options for delivering clinician-led mental health services.

Informative option portfolios in filter design for option pricing models

Option pricing models are tools for pricing and hedging derivatives. Good models are complex and the econometrician faces many design decisions when bringing them to the data. I show that strategically constructed low-dimensional filter designs match and often outperform those that try to use all the available option data, in terms of state recovery, pricing, and hedging. The filters are built around option portfolios that aggregate option data, and track changes in risk-neutral variance and skewness. They also explicitly account for difficulties in the recovery of risk-neutral moments from option prices. The performance advantage is greatest in empirically relevant settings: in models with strongly skewed jump components that are not driven by Brownian volatility.

Recovering the Variance Premium

This paper generalizes Ross (2015) recovery theory to accommodate growth, including the Black-Scholes and stochastic volatility option models. The new theory recovers information about equity risk premia and variance risk premia from options prices. In the Heston (1993) stochastic volatility model, the theory predicts an exact (negative) value for the variance risk premium as a function of the equity premium. Recovery theory also predicts that the stochastic discount factor is the reciprocal return on a model-free portfolio of index options. This paper tests the theory on returns from one-month VIX and three-month VIX3M option portfolios from 2007-2018. Recovery theory links the equity pre- mium to the values of both conditional and unconditional variance premia. It also predicts how VIX3M option variance is a biased predictor of future one-month VIX variance. Empirically, recovery theory simultaneously matches the average S&P 500 equity premium, the average variance premium, and observed biases in the variance expectations hypothesis. Autocorrelation properties of VIX indices imply a 12% annual equity premium.

Incentivizing stewardship in a biodiversity hot spot: land managers in the grasslands

Federal and provincial governments of Canada recently signed onto a Pan-Canadian Approach to Transforming Species at Risk Conservation. The approach is based on collaboration among jurisdictions and stakeholders to enhance multiple species and ecosystem-based conservation in selected biodiversity hot spots. In this review paper, we focus on one of the biodiversity hot spots—the South of the Divide area in the province of Saskatchewan—to propose appropriate mechanisms to incentivize stewardship on agricultural Crown lands. Through a focused review and synthesis of empirical studies, we propose a range of policy instruments and incentives that can help deliver multi-species at risk conservation on Crown agricultural lands in Saskatchewan. We outline a range of policy instruments and incentives that are relevant to conservation on Crown agricultural lands and argue that a portfolio of options will have the greatest social acceptability. More germane is the need to foster collaboration between the government of Saskatchewan, other provincial/territorial governments, and the federal government, nongovernmental organizations, and land managers. Such collaboration is critical for enhanced decision-making and institutional change that reflects the urgent call for creating awareness of species at risk policies, building trust, and leveraging the local knowledge of land managers for conservation.

Evaluation of options portfolios for exchange rate hedges

&lt;p&gt;&lt;span&gt;In this paper evaluate six exchange rate hedging strategies with financial options from the OTC market in Colombia. Three hedging strategies for importers and three for exporters were raised. The coverage for importers was carried out with the traditional strategy of long call, bull call spread and bull put spread, the last two correspond to options portfolios. the coverage for importers was carried out with the traditional strategy of long put, bear call spread and bear put spread, the last two correspond to options portfolios. to determine the best hedging strategy, the currency price was modeled with a Wiener process and the VaR for the six covered scenarios was calculated and compared with the VaR of the uncovered scenario. The results shown by the six hedging strategies manage to mitigate the exchange risk, but the most efficient strategies are the traditional ones for both importers and exporters.&lt;/span&gt;&lt;/p&gt;

Option Return Predictability with Machine Learning and Big Data

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.