Future Portfolio Research Articles

Exploring the upside of risk in project management: A phenomenological inquiry

Risk management is used to identify, assess, and respond to uncertainties. In project management, risks can be categorized as either threats (negative events or conditions) or opportunities (positive). This exploratory qualitative study investigates on how practitioners use positive risks in project management practice and identifies areas for further research. Recommendations from this study include further exploration of: (a) opportunity management training (b) developing a catalog of opportunity examples, and (c) developing a template for the business case for opportunity management. Through risk management, the project manager can improve the likelihood of successfully meeting project objectives. Through a focus on both negative and positive risk, the project manager can offset negative results and possibly project objectives to delight the stakeholders. This research provides greater insight to improve the efficacy for current and future project, program, and portfolio managers.

Conditional Correlations and Principal Regression Analysis for Futures

We explore the effect of past market movements on the instantaneous correlations between assets within the futures market. Quantifying this effect is of interest to estimate and manage the risk associated to portfolios of futures in a non-stationary context. We apply and extend a previously reported method called the Principal Regression Analysis (PRA) to a universe of 84 futures contracts between 2009 and 2019. We show that the past up (resp. down) 10 day trends of a novel predictor -- the eigen-factor -- tend to reduce (resp. increase) instantaneous correlations. We then carry out a multifactor PRA on sectorial predictors corresponding to the four futures sectors (indexes, commodities, bonds and currencies), and show that the effect of past market movements on the future variations of the instantaneous correlations can be decomposed into two significant components. The first component is due to the market movements within the index sector, while the second component is due to the market movements within the bonds sector.

A Short Course in Futures Pricing, Portfolio Management, and Trading

A Short Course in Futures Pricing, Portfolio Management, and Trading

Strategic Planning of Information System/Infromation Technology (Case Study: Gresik Regency Government Environment)

Using IS (Information System)/IT (Information technology) has become the organization's need to carry out its operational activities and services. The government has also used IS/IT in carrying out its activities and services considering the benefits that are get, efficient, effective, and transparency to support good and clean governance. Gresik Regency does not yet have a master plan for implementing IS/IT so that what it needs cannot be managed. Gresik Regency needs a planned information technology-based system that can produce correct and prompt information. purpose of this study was to identify data and information needs in policy formulation and to develop an information system strategic plan in Gresik Regency. Basic concept used in compiling this research is the method proposed by John Ward and Joe Peppard. stages carried out start from analyzing the internal and external business environment of the organization, analyzing internal and external organization IS/IT, followed by identifying strategies and designing future portfolios. Method of analysis carried out uses several methods, among others, SWOT, Critical Success Factors, Value Chain, PEST, and Mc Farlan Strategic Grid. The results of this study is a blueprint of strategic planning that refers to the organization's business plan based on data obtained through the analytical methods used. It will integrate the information produced for administrative and public service needs. organization's strategic objectives in the form of superior prime service will be achieved by implementing an optimal improvement of data processing functions with the support of Information Technology. preparation of IS / IT strategic planning according to the resulting portfolio is expecting to be using in future strategic planning so it can give priority to the activities needed to get optimal results and based on its contribution to the objectives to be achieving

Speculative pressure

AbstractThe paper investigates the information content of speculative pressure across futures classes. Long‐short portfolios of futures contracts sorted by speculative pressure capture a significant premium in commodity, currency, and equity markets but not in fixed income markets. Exposure to commodity, currency, and equity index futures’ speculative pressure is priced in the broad cross‐section after controlling for momentum, carry, global liquidity, and volatility risks. The findings are confirmed by robustness tests using alternative speculative pressure signals, portfolio construction techniques, and subperiods interalia. We argue that there is an efficient hedgers‐speculators risk transfer in commodity, currency, and equity index futures markets.

Time-Series Momentum: A Monte Carlo Approach

In this article the authors develop a Monte Carlo backtesting procedure for risk premium strategies and employ it to study time-series momentum (TSM). Relying on time-series models, empirical residual distributions, and copulas, the authors address two key drawbacks of conventional backtesting procedures. They create 10,000 paths of different TSM strategies based on the S&P 500 and a cross-asset-class futures portfolio. The simulations reveal a probability distribution that shows that strategies that outperform buy-and-hold in sample using historical backtests may (1) exhibit tail risks and (2) underperform or outperform when out of sample. The results are robust to using different time-series models, time periods, asset classes, and risk measures. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • Historical backtests suffer from the problem that they contain few tail events which may be an important driver for the performance of risk premium strategies. • We develop a Monte-Carlo procedure that uses a combination of copulas, time series models and empirical residual distributions to overcome this problem. • Applied to time-series momentum, we find that this strategy may (1) exhibit tail risks (2) underperform or outperform in the long-run.

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions.

We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk. Three FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model. In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results. The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings.

Semi-continuous anaerobic digestion of the marine micro-algal species I. galbana and D. salina grown under low and high sulphate conditions

Anaerobic digestion of marine micro-algae is a necessary step for their incorporation into the future portfolio of biofuels. Digestion of marine feedstocks can pose operational issues associated with competition and toxicity to the microbial consortium. This research examined the marine species Isochrysis galbana and Dunaliella salina continuously cultivated in a tubular photobioreactor using a low sulphate medium; D. salina was also cultivated with a high sulphate medium (4.7 g SO4 L−1). Harvested micro-algal biomass was used as feedstock in semi-continuous digestion with a salt-adapted inoculum. Stable operation was achieved with reasonable specific methane production (SMP) despite a short (15-day) retention time. SMP for I. galbana and D. salina was 0.244 and 0.233 L CH4 g−1 volatile solids (VS), with VS destruction 32% and 48% respectively. SMP ranged from 62 to 94% of the biochemical methane potential, but was only 32–49% of theoretical methane yields, indicating pre-treatments may be beneficial. Changing from low to high sulphate D. salina reduced the SMP to 0.193 L CH4 g−1 VS with a rise in H2S production. Under semi-continuous digestion, evidence for sulphide precipitation and oxidation was observed, which were not seen in batch analyses. This highlights the importance of conducting continuous rather than batch studies, to avoid overlooking these effects.

A stochastic control approach to managed futures portfolios

We study a stochastic control approach to managed futures portfolios. Building on the (Schwartz, 1997) stochastic convenience yield model for commodity prices, we formulate a utility maximization problem for dynamically trading a single-maturity futures or multiple futures contracts over a finite horizon. By analyzing the associated Hamilton–Jacobi–Bellman (HJB) equation, we solve the investor’s utility maximization problem explicitly and derive the optimal dynamic trading strategies in closed form. We provide numerical examples and illustrate the optimal trading strategies using WTI crude oil futures data.

A Multi Strategy Approach to Trading Foreign Exchange Futures

In this article we present a systematic multi-strategy approach to trading foreign exchange futures for a managed futures portfolio. Our central finding is that there is more alpha to be derived from combining different indicators compared to hand engineering each indicator. We show that combining technical indicators like momentum and mean reversion with fx carry indicators leads to significant improvement over individual indicators. Through an end to end systematic portfolio construction methodology, including indicator construction, normalization and combination we are able to improve the Sharpe Ratio of the resulting portfolio over the best performing single indicator by 60% when evaluated in an unbiased walk forward backtest. Code available in the Appendix

Tracking VIX with VIX Futures: Portfolio Construction and Performance

We study a series of static and dynamic portfolios of VIX futures and their effectiveness to track the VIX index. We derive each portfolio using optimization methods, and evaluate its tracking performance from both empirical and theoretical perspectives. Among our results, we show that static portfolios of different VIX futures fail to track VIX closely. VIX futures simply do not react quickly enough to movements in the spot VIX. In a discrete-time model, we design and implement a dynamic trading strategy that adjusts daily to optimally track VIX. The model is calibrated to historical data and a simulation study is performed to understand the properties exhibited by the strategy. In addition, comparing to the volatility ETN, VXX, we find that our dynamic strategy has a superior tracking performance.

Multivariate Continuous-Time Modeling of Wind Indexes and Hedging of Wind Risk

With the introduction of the exchange-traded German wind power futures, opportunities for German wind power producers to hedge their volumetric risk are present. We propose two continuous-time multivariate models for the wind power utilization at different wind sites, and discuss the properties and estimation procedures for the models. Employing the models to wind index data for wind sites in Germany and the underlying wind index of exchange-traded wind power futures contracts, the estimation results of both models suggest that they capture key statistical features of the data. We argue how these models can be used to find optimal hedging strategies using exchange-traded wind power futures for the owner of a portfolio of so-called tailor-made wind power futures. Both in-sample and out-of-sample hedging scenarios are considered, and, in both cases, significant variance reductions are achieved. Additionally, the risk premium of the German wind power futures is analysed, leading to an indication of the risk premium of tailor-made wind power futures.

Portfolio Stress Testing Applied to Commodity Futures

In this article, we construct a portfolio of commodity futures which mimics the Dow Jones Commodity Index and perform an extensive stress testing exercise with a focus on hybrid scenarios. The increased volume of investments in commodities as financial instruments over the last decades underline the importance of a more thorough framework for stress testing of related portfolios. Our study is the first to show comparatively the marginal impact of the model choice for portfolio components versus the marginal role of tail dependence on the portfolio profit and loss in stress testing exercises. We model the distribution of returns of portfolio components with an asymmetric AR-GARCH model combined with Extreme Value Theory for extreme tails, and employ multivariate copula functions to model the time-varying joint dependence structure. Our study reveals that indeed, for a realistic stress test, a special attention should be given to the tail risk in individual commodity returns as well as to tail correlations. We also draw conclusions about parameter risk persistent in stress testing exercises. Finally yet importantly, in line with Basel IIIb, the study highlights the importance of using forward-looking hybrid and hypothetical scenarios over historical scenarios.

Optimal Dynamic Futures Portfolio in a Regime-Switching Market Framework

We study the problem of dynamically trading futures in a regime-switching market. Modeling the underlying asset price as a Markov-modulated diffusion process, we present a utility maximization approach to determine the optimal futures trading strategy. This leads to the analysis of the associated system of Hamilton-Jacobi-Bellman (HJB) equations, which are reduced to a system of linear ODEs. We apply our stochastic framework to two models, namely, the Regime-Switching Geometric Brownian Motion (RS-GBM) model and Regime-Switching Exponential Ornstein-Uhlenbeck (RS-XOU) model. Numerical examples are provided to illustrate the investor's optimal futures positions and portfolio value across market regimes.

Time-Series Momentum: A Monte-Carlo Approach

This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time-Series Momentum (TSM). Relying on time-series models, empirical residual distributions and copulas we overcome two key drawbacks of conventional backtesting procedures. We create 10,000 paths of different TSM strategies based on the S&P 500 and a cross-asset class futures portfolio. The simulations reveal a probability distribution which shows that strategies that outperform Buy-and-Hold in-sample using historical backtests may out-of-sample i) exhibit sizeable tail risks ii) underperform or outperform. Our results are robust to using different time-series models, time periods, asset classes, and risk measures.

Embracing systematic futures thinking at the intersection of Strategic Planning, Foresight and Design

In this conceptual paper, we review the literature spanning the Strategic Planning, Foresight and Design disciplines with the emphasis placed on how innovation stakeholders may engage with the future in order to explore the challenges to decision-making they highlight. From this review, and a series of facilitators identified by the authors in previous design and futures thinking field research, critical perspectives are presented that illustrate how systematic futures thinking can inform decision-makers concerning the innovation challenges and opportunities emerging over medium and longer-term (5-15 years) time horizons of social and technology environments. Combining Foresight and Design in an approach we call “Foresight by Design”, as part of Strategic Planning processes, can help the emergence of new and more creative possibilities, foster the inclusion and alignment of diverse stakeholders, and provide for ongoing learning through prototyping and experimentation by using design tools and approaches to achieve deeper insight and alignment around current reality, to facilitate a more productive conversation across difference, to aid in specifying a portfolio of desirable futures, and to engage ecosystem partners in active experimentation that generates new knowledge and learning.

Embracing systematic futures thinking at the intersection of Strategic Planning, Foresight and Design

In this conceptual paper, we review the literature spanning the Strategic Planning, Foresight and Design disciplines with the emphasis placed on how innovation stakeholders may engage with the future in order to explore the challenges to decision-making they highlight. From this review, and a series of facilitators identified by the authors in previous design and futures thinking field research, critical perspectives are presented that illustrate how systematic futures thinking can inform decision-makers concerning the innovation challenges and opportunities emerging over medium and longer-term (5-15 years) time horizons of social and technology environments. Combining Foresight and Design in an approach we call “Foresight by Design”, as part of Strategic Planning processes, can help the emergence of new and more creative possibilities, foster the inclusion and alignment of diverse stakeholders, and provide for ongoing learning through prototyping and experimentation by using design tools and approaches to achieve deeper insight and alignment around current reality, to facilitate a more productive conversation across difference, to aid in specifying a portfolio of desirable futures, and to engage ecosystem partners in active experimentation that generates new knowledge and learning.

A multi-factor HJM and PCA approach to risk management of VIX futures

PurposePrevious studies have shown the VIX futures tend to roll-down the term structure and converge towards the spot as they grow closer to maturity. The purpose of this paper is to propose an approach to improve the volatility index fear factor-level (VIX-level) prediction.Design/methodology/approachFirst, the authors use a forward-looking technique, the Heath–Jarrow–Morton (HJM) no-arbitrage framework to capture the convergence of the futures contract towards the spot. Second, the authors use principal component analysis (PCA) to reduce dimensionality and save substantial computational time. Third, the authors validate the model with selected VIX futures maturities and test on value-at-risk (VAR) computations.FindingsThe authors show that the use of multiple factors has a significant impact on the simulated VIX futures distribution, as well as the computations of their VAR (gain in accuracy and computing time). This impact becomes much more compelling when analysing a portfolio of VIX futures of multiple maturities.Research limitations/implicationsThe authors’ approach assumes the variance to be stationary and ignores the volatility smile. Nevertheless, they offer suggestions for future research.Practical implicationsThe VIX-level prediction (the fear factor) is of paramount importance for market makers and participants, as there is no way to replicate the underlying asset of VIX futures. The authors propose a procedure that provides efficiency to both pricing and risk management.Originality/valueThis paper is the first to apply a forward-looking method by way of a HJM framework combined with PCA to VIX-level prediction in a portfolio context.

Hedging of crop harvest with derivatives on temperature

This article studies hedging strategies of crop harvest incomes with futures and options on indexes of cumulated average temperatures (CAT). To account for the time and space dependence, temperatures and crop yields are modeled by three dimensions Gaussian fields. In this framework, we study the features and dynamics of CAT futures and CAT basket options. Next, we find the portfolio of CAT futures minimizing the variance of incomes from crop in different regions. We compare this hedging strategy to the portfolio maximizing the expected exponential utility of incomes. Furthermore, we assess the impact of CAT basket options on the variance of crop incomes. We conclude this work by a realistic case study in which the harvest of green maize in two Belgian regions is hedged against adverse deviations of temperatures with CAT futures or options.

Abstract A21: Using spatial analysis to identify the impact of medically underserved areas within and beyond the catchment area of a Comprehensive Cancer Center

Abstract Objective: To develop a spatial analysis-driven enhanced description of the patients treated at the Alvin J. Siteman Cancer Center (SCC) at Washington University School of Medicine, to better address cancer disparities impacting our community by tailoring the resources offered at SCC to the needs of our patients. Background: The race and ethnicity breakdown of the self-defined catchment area (CA) at NCI-designated Comprehensive Cancer Centers serves as a benchmark for the demographic proportion seeking treatment at the institution as well as clinical trial (CT) enrollment. To meet the goals set by this benchmark, institutions must have a thorough understanding of the population treated at their institution. Using Geographic Information Systems (GIS) to explore patient data provides additional context beyond the traditionally reported race and ethnic descriptors. Further, incorporating Medically Underserved Areas (MUAs), as identified by the Health Resources and Services Administration (HRSA), and rural status into the GIS is an important and comprehensive method to provide a richer understanding of the patient population we serve. This understanding can be used to tailor resources offered by the center, establish a more appropriate CT portfolio, and may provide insight into disparate trends not readily apparent. Methods: The database of patients (n=8,691) was obtained from the SCC cancer registry and includes those seen at SCC for the first time in 2015 who met the NCI's Data Table Three reporting criteria. Spatial data on MUAs were downloaded from HRSA website for use in the GIS at SCC. Rural status was defined using Census-designated, ZIP code-level rural-urban commuting area (RUCA) codes, where rural >= 7. The patient addresses were geocoded and spatial analyses performed using ArcGIS Desktop. Results: Of the patients in the database, 84.8% were geocoded to address-level specificity. Of those, 67% live in catchment area, and 25.9% live in an MUA. Exploring the percent of total patients by race and MUA, 4.1% are African American patients living in an MUA and 21.4% are white patients living in an MUA. A significantly higher proportion of patients who are African American live in an MUA (31.3%), compared to white patients who live in an MUA (25.2%, p<0.001). 12.7% of SCC patients live in a rural zip code; of these, 48.5% live in an MUA. Conclusions: As health disparities for rural patients continue to be revealed, including and beyond proximity to adequate health care, we have greater responsibility to understand this aspect of our patient population and surrounding communities, to minimize the impact of these disparities. White patients living in an MUA have not previously been represented in Comprehensive Cancer Center data reporting as having health disparities. This spatial analysis approach acknowledges this disparity and ensures that health disparities of this group will be represented in future policy priorities and CT portfolio decisions. Additionally, these results highlight the burden of intersectionality of race and geographic disparities on health, particularly in our African American patients. We continue to refine GIS approaches to expand on these results. Future applications of spatial analysis to understand patient populations and health disparities include: inform policy decisions, outreach, and clinical trial portfolio, explore known cancer disparities as it relates to MUAs (such as prostate cancer in African American men, by MUA status), review staging at diagnosis by MUA status, and possibly include in the electronic health record for use by clinical staff. Understanding the MUA and rural status of Comprehensive Cancer Center patients provides a richer description that will benefit both the patient and the community as a whole, and subsequently the potential impact on reducing cancer disparities. Citation Format: Christine M. Marx, Jessica L. Thein, Graham A. Colditz. Using spatial analysis to identify the impact of medically underserved areas within and beyond the catchment area of a Comprehensive Cancer Center [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr A21.