Multiple Assets Research Articles

The sum of two independent polynomially-modified hyperbolic secant random variables with application in computational finance

In this paper, by reshaping the hyperbolic secant distribution using Hermite polynomial, we devise a polynomially-modified hyperbolic secant distribution which is more flexible than secant distribution to capture the skewness, heavy-tailedness and kurtosis of data. As a portfolio possibly consists of multiple assets, the distribution of the sum of independent polynomially-modified hyperbolic secant random variables is derived. In exceptional cases, we evaluate risk measures such as value at risk and expected shortfall (ES) for the sum of two independent polynomially-modified hyperbolic secant random variables. Finally, using real datasets from four international computers stocks, such as Adobe Systems, Microsoft, Nvidia and Symantec Corporations, the effectiveness of the proposed model is shown by the goodness of Gram–Charlier-like expansion of hyperbolic secant law, for performance of value at risk and ES estimation, both in and out of the sample period.

Market strategies for large-scale energy storage: Vertical integration versus stand-alone player

New projects using existing storage technologies such as Pumped Hydro Storage (PHS) face uncertainty due to the lack of clear business models. Market regimes have generally tended to embed storage and generators within the central management of operators with multiple assets such as the French EdF (Electricité de France). By means of back-casting, this study depicts the role of storage in the power market, within the vertical integration in the governance portfolio of EdF. A dynamic algorithm simulates hourly operation under two different storage strategies, daily and weekly, and the results are compared with actual patterns over the period 2015–2019. This reveals missing money for a stand-alone player due to price arbitrage caused by low spread, and missing market opportunities. This suggests that the economics of storage are not driven by spot prices alone, but by other services, such as the energy block provision in support of nuclear power. Differential calculus is used to estimate the value of flows integrating the duration of storage and its seasonality. These findings further support the French regulator to install new PHS despite the lack of profitability, by means of capacity-energy hybrid contracts for new competitors in place of the existing government-industry structure.

Monetary policy surprises and exchange rate behavior

Central banks unexpectedly tightening policy rates often observe the exchange value of their currency depreciate, rather than appreciate as predicted by standard models. We document this for Fed and ECB policy days using event studies and ask whether an information effect, where the public attributes the policy surprise to an unobserved state of the economy, may explain the abnormality. It turns out that many informational assumptions make a standard two-country New Keynesian model match this behavior. To identify the particular mechanism, we condition on multiple asset prices in the event study and model implications for these. We find that there is heterogeneity in this dimension in the event study and no model with a single regime can match the evidence. Further, even after conditioning on possible information effects driving longer term interest rates, there appear to be other drivers of exchange rates. Our results show that existing models have a long way to go in reconciling event study analysis with model-based mechanisms of asset pricing.

A new option for education during surgical procedures and related clinical anatomy in a virtual reality workspace.

The COVID-19 pandemic and mandatory social distancing has brought challenges to anatomy educators who generally need in-person classes. The purpose of this study is to share the experience of a distant online lecture on a surgical procedure and related anatomy in a three-dimensional (3D) virtual reality (VR) workspace and to compare it with reported teaching methods, that is, an in-person class and a Zoom online class. The lecture was delivered by three authors of this article in a VR workspace that enables people to meet through VR. The lectures were about combinations of dental surgical procedures and related clinical anatomy. Physically, the attendees could have been located anywhere in the world, so lecturers joined from the United States and the attendees were all from Japan. VR environment and its flexibility enabled attendees to join the lecture actively, helping them to gain understanding of the surgical procedure and anatomy more efficiently. The use of VR technology with a live communication tool demonstrated in this study has several advantages over previous education methods, although there are still technical issues or disadvantages that need to be addressed. Development of the technology and app/software is required so that more data can be processed at higher speed. Use of VR technology with a live communication tool could be an alternative teaching method. Its overall advantages are a closer look at the slides/monitor and concurrent observation of the multiple assets in various directions by multiple attendees. These advantages cannot be achieved by any other teaching method without VR assets with the workspace provided by Spatial. Even during the mandatory social distancing due to the COVID-19 pandemic, this could enable us to foster 3D understanding of surgery and related anatomy. Further study is now needed to evaluate the effectiveness of this newly proposed teaching method by comparing it with traditional in-person and online classes with a live communication tool.

Autonomous recommender system for reconnaissance tasks using a swarm of UAVs and asynchronous shepherding

Several artificial intelligence-based systems have been proposed to help human operators in safety-critical domains such as air-traffic control systems, health and farming. In scenarios where multiple autonomous systems are utilised, the cognitive capacity of humans hits its limits quickly, and sometimes the success of these systems is obstructed. Designing systems to sustain human performance over longer periods through balancing the load between human operators and autonomous systems can reduce errors and improve overall performance. Future human-autonomy systems need to be designed in a way that makes them scalable in terms of capabilities and safety, yet flexible enough to adapt to the cognitive workload of human operators. In this paper, we propose an adaptive human swarm interaction system for maintaining human operator performance in supervisory-control aerial-surveillance tasks. A bio-inspired guidance and control shepherding agent is proposed to support the human operator to guide multiple assets simultaneously using a single point of control. The proposed system employs an adjustable autonomous recommender system to provide the human operator with timely recommendations in real-time sittings. The proposed system is designed in a flexible way that allows it to adapt its rate of providing these recommendations in response to the needs of human operators. Simulation-based experiments are conducted to validate and evaluate the proposed recommender system. Experimental results demonstrate the effectiveness and efficiency of the proposed recommender system in minimising control errors while maintaining low computational costs that allow it to be used in a real-time applications.

Macroprudential Policy and Asset Liquidity

This paper develops a dynamic model to study optimal liquidity regulations for multiple assets that differ in liquidity. I show that optimal macroprudential policies are affected by asset liquidity and the multi-asset structure. Lower asset liquidity amplifies declines in asset prices and tightens the collateral constraint during financial crises, raising macroprudential taxes on debt. With multiple assets, the marginal benefit of investing in one asset is affected by cross-price elasticities of all assets, depending on trading positions and the collateral constraint's tightness. The optimal policy in the multi-asset model can differ from those in standard one-asset models in size and sign. Quantitatively, optimal macroprudential policies favor liquid assets and reduce borrowing. The policy reduces the probability of financial crises from 8% to zero and increases welfare by 1.2%. Finally, I analyze and quantify the current Basel III reform, which increases agents' liquid holdings and decreases the probability of crises. However, the policy reduces welfare, as agents overborrow and overinvest in liquid assets.

Markowitz Portfolio Selection for Multivariate Affine and Quadratic Volterra Models

This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and nonsemimartingale market framework with unbounded random coefficients, the optimal portfolio strategy is expressed by means of a Riccati backward stochastic differential equation (BSDE). In the case of affine Volterra models, we derive explicit solutions to this BSDE in terms of multidimensional Riccati--Volterra equations. This framework includes multivariate rough Heston models and extends the results of Han and Wong [Appl. Math. Optim. (2020)]. In the quadratic case, we obtain new analytic formulae for the the Riccati BSDE and we establish their link with infinite-dimensional Riccati equations. This covers rough Stein--Stein and Wishart type covariance models. Numerical results on a two-dimensional rough Stein--Stein model illustrate the impact of rough volatilities and stochastic correlations on the optimal Markowitz strategy. In particular for positively correlated assets, we find that the optimal strategy in our model is a “buy rough sell smooth” one.

Information Asymmetry, Non-Learnable Payoff and the Cost of Capital

This paper examines the roles of information differences among informed investors (information asymmetry) in the determination of a firm's cost of capital and price informativeness when the risky asset payoff contains a non-learnable component. The existence of non-learnable payoff disproportionately decreases the information advantage of more informed traders, relative to less informed traders, leading asset prices to be affected by both the aggregate information precision and information asymmetry among investors. As a result, a higher level of information asymmetry decreases price informativeness and increases cost of capital. In an economy with multiple assets, information asymmetry from the market factor affects the costs of capital of all risky assets, but information asymmetry from the firm specific factor only affects the cost of capital of that particular firm.

Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the S&P500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes.

Robust Pricing and Hedging of Options on Multiple Assets and Its Numerics

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 9 September 2019Accepted: 06 October 2020Published online: 18 February 2021Keywordsrobust pricing and hedging, optimal transport, martingale optimal transport, robust copula, multi-marginal transport, numerical methods, linear programming, machine learning, deep neural networksAMS Subject Headings60G42, 49M25, 49M29, 90C08Publication DataISSN (online): 1945-497XPublisher: Society for Industrial and Applied MathematicsCODEN: sjfmbj

Market Experiments with Multiple Assets: A Survey

Market Experiments with Multiple Assets: A Survey

Thinking Beyond the Hiring and Firing of Asset Managers: a New Framework Truly Aligning Asset Owners with Asset Managers

Research has shown that the way asset owners deal with their asset managers often leads to adverse outcomes for themselves and their asset managers. This paper introduces an innovative framework which plays a key role in achieving a stronger alignment between an asset owner and their asset manager. The framework has been designed to offer a strong theoretical foundation to determine the asset owner’s optimal set of investment strategies across multiple asset classes. Based on this framework, the paper offers an asset owner a coherent set of alignment strategies which contribute to a better alignment with the asset owner’s investment objectives.

Solid-state NMR approaches to investigate large enzymes in complex with substrates and inhibitors.

Enzyme catalysis is omnipresent in the cell. The mechanisms by which highly evolved protein folds enable rapid and specific chemical transformation of substrates belong to the marvels of structural biology. Targeting of enzymes with inhibitors has immediate application in drug discovery, from chemotherapeutics over antibiotics to antivirals. NMR spectroscopy combines multiple assets for the investigation of enzyme function. The non-invasive technique can probe enzyme structure and dynamics and map interactions with substrates, cofactors and inhibitors at the atomic level. With experiments performed at close to native conditions, catalytic transformations can be monitored in real time, giving access to kinetic parameters. The power of NMR in the solid state, in contrast with solution, lies in the absence of fundamental size limitations, which is crucial for enzymes that are either membrane-embedded or assemble into large soluble complexes exceeding hundreds of kilodaltons in molecular weight. Here we review recent progress in solid-state NMR methodology, which has taken big leaps in the past years due to steady improvements in hardware design, notably magic angle spinning, and connect it to parallel biochemical advances that enable isotope labelling of increasingly complex enzymes. We first discuss general concepts and requirements of the method and then highlight the state-of-the-art in sample preparation, structure determination, dynamics and interaction studies. We focus on examples where solid-state NMR has been instrumental in elucidating enzyme mechanism, alone or in integrative studies.

Multi-asset generalized variance swaps in Barndorff-Nielsen and Shephard model

This paper proposes swaps on two important new measures of generalized variance, namely, the maximum eigenvalue and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for Barndorff-Nielsen and Shephard model used in financial markets. We consider multiple assets in the portfolio for theoretical purpose and demonstrate our approach with numerical examples taking three stocks in the portfolio. The results obtained in this paper have important implications for the commodity sector where such swaps would be useful for hedging risk.

3D GEOMETRIC EXTRACTION USING SEGMENTATION FOR ASSET MANAGEMENT

Abstract. In recent years, there has been an increase in development of urbanization in the world. Nowadays, all communities in the world are concerned about current technological developments especially in terms of development and management that can facilitate their daily life. In urbanization, smart city is one of modernization changes that improves the infrastructure management, convenience and efficient for the life of citizens. Moreover, 3D asset management is one of the approach of smart city development. Asset management using the 3D concept has been witnessing a welcoming approach due to its high efficiency in organising multiple assets. 3D geometric extraction offers a perfect aid in recording information of an asset such as buildings. The model is derived from the reality techniques where the exterior surfaces of an object are captured in high resolution through the means of special equipment such as airborne imagery. From here, point clouds are generated where the sets of points based on the external surfaces of an object are present. Pre-processing of point clouds should be done in order to perform the 3D modelling. In dealing with point clouds, segmentations are used to investigate the structure of the object with information regarding to different level of sections. The boon behind this segmentation process is to identify different features that is available for the object. In this research, the aim is to analyse the different methodology and algorithm available to segment the point cloud data. Comparison between the results will be made to identify the advantages and disadvantages of the results for the use of asset management.

A Note on the Asymptotic Normality Theory of the Least Squares Estimates in Multivariate HAR-RV Models

In this work, multivariate heterogeneous autoregressive-realized volatility (HAR-RV) models are discussed with their least squares estimations. We consider multivariate HAR models of order p with q multiple assets to explore the relationships between two or more assets’ volatility. The strictly stationary solution of the HAR(p,q) model is investigated as well as the asymptotic normality theories of the least squares estimates are established in the cases of i.i.d. and correlated errors. In addition, an exponentially weighted multivariate HAR model with a common decay rate on the coefficients is discussed together with the common rate estimation. A Monte Carlo simulation is conducted to validate the estimations: sample mean and standard error of the estimates as well as empirical coverage and average length of confidence intervals are calculated. Lastly, real data of volatility of Gold spot price and S&P index are applied to the model and it is shown that the bivariate HAR model fitted by selected optimal lags and estimated coefficients is well matched with the volatility of the financial data.

Good-Deal Bounds for Option Prices under Value-at-Risk and Expected Shortfall Constraints

In this paper, we deal with the pricing of European options in an incomplete market. We use the common risk measures Value-at-Risk and Expected Shortfall to define good-deals on a financial market with log-normally distributed rate of returns. We show that the pricing bounds obtained from the Value-at-Risk admit a non-smooth behavior under parameter changes. Additionally, we find situations in which the seller’s bound for a call option is smaller than the buyer’s bound. We identify the missing convexity of the Value-at-Risk as main reason for this behavior. Due to the strong connection between good-deal bounds and the theory of risk measures, we further obtain new insights in the finiteness and the continuity of risk measures based on multiple eligible assets in our setting.

Place marketing, evolutionary theories and the management of multifaceted destinations: Cartagena, Colombia, as a study case

Tourist destinations generally offer a variety of attractions that local authorities or destination management organizations (DMOs) may incorporate into their promotional activities. This diversity of available assets has different stakeholder groups having to decide what the destination actually stands for. The present study connects existing frameworks about evolutionary dynamics and place marketing and applies them to multifaceted destinations to provide fresh insights into how these can be managed. It extends the discussion by taking the city of Cartagena de Indias, Colombia, as an in-depth case study to illustrate how multiple assets as well as multiple place identities can impact the management of a given destination. The main data source consisted of semi-structured interviews which were used to explore the (typologies of) attractions that best represent the destination and how these should be promoted according to the local institutional stakeholders. The results show that being a multifaceted destination poses significant challenges to the definition of tourism marketing strategies, even if most stakeholders agree that a shift from the traditional, beach-related elements towards more (intangible) cultural resources is necessary. The paper concludes by drawing some theoretical implications as well as practical recommendations for multiple asset destinations.

An Ontology to Describe Small Molecule Pharmaceutical Product Development and Methodology for Optimal Activity Scheduling

This contribution is the first example of a small molecule pharmaceutical product development ontology. This ontology allows the portfolio-wide visualization of the small molecule pharmaceutical development business as a network of decisions and activities. This ontology was built not only to digitalize, rapidly access, and gain insights from prior decision-making but also to deliver input data for dynamic resource allocation via optimal scheduling of research activities subject to resource, cost, and time constraints. This ontology can be understood as a generic wiring diagram for small molecule product development which outlines the predecessors and successors of activities and decisions, along with needed or allowed overlap between activities. Each activity has multiple modes with direct dollars, resource consumption, and time taken to complete each mode. This generic wiring diagram was instanced for 17 portfolio assets by creating 32 specific scenarios. These scenarios were stored in an ontology as instances and served as foundational digitalized data for a host of future applications. These specific scenarios across the multiple assets served as input to the optimal resource allocation model. In summary, this work describing pharmaceutical Chemistry Manufacturing and Controls (CMC) development activities and decisions is a foundational transformative project that enables full digitization of pharmaceutical CMC data, not only for optimal scheduling but also to generate heuristics on selection of research activities given development risks, a topic which has received virtually no mention in literature.

Analytics of machine replacement decisions: economic life vs real options

PurposeDespite the existence of multiple asset replacement theories, the economic life replacement method remains a major practical technique for making rational machine replacement decisions. The purpose of this paper is to bridge this method with comprehensive data analytic tools and make it applicable it to modern business reality with abundant data on operating and replacement costs.Design/methodology/approachThis study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.FindingsConstructed stochastic algorithms extend the deterministic economic life method and are compared to the contemporary theory of stochastic asset replacement based on real options and dynamic programming. It is proven that both techniques deliver similar results when the cost volatility is small. A major theoretic finding is that the cost uncertainty speeds up the replacement decision.Research limitations/implicationsThis research suggests that the proposed stochastic algorithms may become an important tool for managerial decisions about replacement of many similar machines with detailed data on operating and replacement costs.Originality/valueCompared to the real options replacement theory, major advantages of the proposed algorithms are that they work equally well for any distribution of age-dependent stochastic operating cost. The algorithms are tested on a real industrial case about replacement of medical imaging devices. Numeric simulation supports obtained analytic outcomes.