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Unpacking the role of political will in digital business ecosystem development for socioeconomic benefits
No full textAs digital technology transforms many organisational alliances, new collaborative networks such as digital business ecosystems have emerged. In digital business ecosystems, participants leverage technological innovations to develop capabilities for value co-creation. Despite the growing number of studies, there is lack of research on how political-will facilitates development of digital business ecosystems. Therefore, this study develops a framework to explicate the role of political-will in the development of digital business ecosystems to achieve socioeconomic benefits. The findings show that political-will leads to provision of resources and legislative support as well as formulation of strategic initiatives required at the birth, expansion and maturity phases of digital business ecosystems to generate socioeconomic benefits such as (1) reduced corruption, (2) improved operational processes, (3) increased government revenue, (4) reduced bureaucracy and (5) improved transparency, fairness and accountability
Risk Management for Energy Markets.
No full textABSTRACT: Questa tesi si occupa di gestione del rischio. Il tema unificante è la gestione del rischio di mercato dell'energia. I diversi capitoli trattano la gestione del rischio in modo diverso e considerano diversi mercati energetici. Nel primo capitolo viene affrontato il tema della stima del rischio applicato a due principali mercati elettrici europei: Powernext (Francia) ed EEX (Germania e Austria). Nel secondo capitolo la misurazione del rischio viene effettuata mediante l’applicazione dell’allocazione ottima di portafoglio nei mercati dell'energia elettrica utilizzando i rendimenti calcolati sui prezzi futures. Nel terzo capitolo, la teoria di allocazione ottima di portafoglio viene applicata al mercato zonale italiano (utilizzando le zone fisiche). La modellazione, la misurazione e la contabilità del rischio sono importanti dal punto di vista teorico e, in particolare, dal punto di vista pratico. In pratica, esso svolge un ruolo chiave nella strategia di allocazione del portafoglio sui mercati energetici. Nel contesto attuale gli operatori attivi sui mercati energetici devono affrontare livelli di rischio senza precedenti. L'importanza di una corretta gestione del rischio e di una corretta comprensione dei rischi è fondamentale per l’effettuazione di un buon investimento e per guidare le decisioni contrattuali. La gestione del rischio deve condurre al raggiungimento di un equilibrio mix di rischio e rendimento attraverso una particolare strategia di trading. Con “strategia di trading” si intende un insieme definito di regole da seguire per effettuare buone decisioni di negoziazione. Questa tesi si compone di tre capitoli autonomi. Nel primo capitolo, effettuiamo un'analisi econometrica del rischio nel mercato elettrico utilizzando prezzi spot. Il mercato dell'energia, ed in particolare il mercato dell'energia elettrica, sta attraversando una fase di transizione in tutto il mondo. Le fluttuazioni dei prezzi e a la loro stretta correlazione con la domanda sono una caratteristica comune a tutti i mercati elettrici liberalizzati. Il test più importante per i nuovi mercato liberalizzati è la capacità di gestire l'eccessiva volatilità connessa ad un sistema con sostanziali variazioni temporali di capacità di generazione. In questo primo lavoro abbiamo proposto di utilizzare AR-GARCH - tipo – EVT (Estreme Value Theory) con diverse distribuzioni delle innovazioni e con varianti che tengano conto della risposta asimmetrica della volatilità per la stima del Value at Risk nei mercati elettrici. Quindi, il rischio di investimento sui mercati dell'energia elettrica viene calcolato in base alla stima del VaR e del VaR condizionato mediante filtri di tipo GARCH con distribuzioni a code pesanti. L'attenzione si è fissata sia dal punto di vista dei regolatori (code superiori) e degli investitori (code inferiori). Le autorità di vigilanza e i regolatori sono infatti più preoccupati del rischio che si verifichino prezzi elevati poichè il loro obiettivo è quello di garantire l'efficienza del mercato. Il secondo capitolo, presenta l’applicazione della teoria dell’allocazione ottimale di portafoglio ai mercati energetici attraverso una versione modificata del classico approccio mean-variance suggerito originariamente da Markowitz. Il risultato principale del capitolo mostra che i portafogli con scadenze diverse potrebbero fornire agli operatori di mercato delle linee guida per una buona strategia di gestione del rischio nei mercati energetici. Le tecniche di ottimizzazione vengono utilizzate per ottenere pesi ottimali per l'allocazione degli investimenti finanziari, al fine di analizzare il rischio di investimento connesso al mercato dell'energia elettrica utilizzando prezzi futures. Si tratta di un'applicazione originale di una tecnica di ottimizzazione già nota in letteratura, ma che non è ancora stata esplorata nello studio del mercato energetici. In particolare, la tecnica di ottimizzazione basata sul VaR condizionale come misura del rischio non è ancora stata utilizzata per l'analisi del rischio insito nei mercati dell'energia elettrica. Nel terzo capitolo, effettuiamo un’analisi spaziale del rischio di investimento nei mercati zonali. Da quando il mercato elettrico italiano è stato liberalizzato, non esistono documenti a conoscenza dell’autore che abbiano considerato l'ottimizzazione portafoglio nel mercato zonale italiano. Nei mercati liberalizzati dell'energia elettrica con prezzi zonali, il mercato è suddiviso in alcune zone, a ciascuna delle quali è assegnato un prezzo di mercato al quale i partecipanti reagiscono in un qualsiasi istante temporale. Il nostro contributo consiste nell’applicazione dell’allocazione di portafoglio basata sul VaR come misura di rischio al mercato zonale italiano per prendere decisioni ponderate di investimento nelle diverse zone in cui è suddiviso il mercato. Lo scopo principale consiste nel mitigare il rischio connesso ad investimenti sul mercato. L' analisi si sposta quindi dalla prospettiva di una diversificazione temporale a quella di una diversificazione spaziale.ABSTRACT: This thesis is concerned with risk management. The unifying theme is the risk management of the energy market. The different chapters deal with risk management in a different way and consider different energy markets. The first chapter addressed the issue of risk assessment applied to two major European electricity markets: Powernext (France) and EEX (Germany and Austria). In the second chapter the measurement of risk is done through the application of the optimal portfolio in the electricity markets calculated using the returns on the futures prices. In the third chapter, the theory of optimal allocation of the portfolio is applied to the zonal Italian market (using physical zones). The modeling, measurement and accounting of risk are important from a theoretical point of view and, in particular, from the practical point of view. In practice, it plays a key role in the strategy of portfolio allocation in the energy markets. In the present context, the operators active on energy markets are facing unprecedented levels of risk. The importance of a proper risk management and a proper understanding of the risk are crucial to the making of a good investment and to guide decisions contract. Risk management must lead to the achievement of a balance mix of risk and return through a particular trading strategy. While “trading strategy" means a defined set of rules to follow to make good trading decisions. This thesis consists of three self-contained chapters. In the first chapter, we carry out an econometric analysis of the risk in the electricity market using spot prices. The energy market, and in particular the electricity market is going through a transition phase in the world. Price fluctuations and their correlation with demand are common features of all liberalized electricity markets. The most important test for the new liberalized market is the ability to manage excessive volatility connected to a system with substantial temporal variations of generation capacity. We have proposed the use of AR-GARCH-type-EVT (Extreme Value Theory) with different distributions of the innovations and variations that take into account the asymmetric response of volatility to estimate the value at risk in the electricity markets. Thus, the risk of investment in electricity markets is calculated based on the estimated VaR and conditional VaR using GARCH filters distributions with heavy tails. The focus is fixed from the point of view of the regulators (upper tails) and investors (lower tails). Supervisors and regulators are in fact more concerned with the risk of experiencing high prices because their aim is to ensure the efficiency of the market. The second chapter suggests the application of the theory of the optimal portfolio to energy markets through a modified version of the classical mean-variance approach originally suggested by Markowitz. The main result of the chapter shows that portfolios with different maturities could provide market operators with guidelines for a good strategy of risk management in energy markets. Optimization techniques are used to obtain optimal weights for the allocation of financial investments, in order to analyze the investment risk connected to the electricity market using futures prices. It is an original application of an optimization technique already known in the literature, but which has not yet been explored in the study of the energy market. In particular, the optimization technique based on conditional VaR as a risk measure has not yet been used for the analysis of the risk inherent in the electricity markets. In the third chapter; we carry out a spatial analysis of the risks of investing in local markets. Since the Italian electricity market has been liberalized, there are no documents as the author knows that they have considered the zonal portfolio optimization in the Italian market. In liberalized electricity markets with zonal prices, the market is divided into several zones, each of which is assigned a market price at which participants react at any moment in time. Our contribution consists in the application of the allocation of the portfolio based on VaR as a risk measure to the market to make informed decisions about zonal Italian investment in the different areas that comprise the market. The main purpose is to mitigate the risk associated with investments in the market. The analysis therefore moves from the perspective of a temporal diversification to that of a spatial diversification
Digital platformisation as public sector transformation strategy: a case of Ghana’s paperless port
No full textPublic sector organisations around the world are deploying digital platforms as part of their transformational strategy. However, prior research has predominantly focused on developed economies with stable institutional environments, while limited studies exist on less developed economies. Notwithstanding the digital divide, institutional voids, economic and development challenges facing less developed economies, digital platformisation as a strategy is fuelling technology leapfrogging in public sector transformation. Drawing on a case study of Ghana’s paperless port digital transformation and the technology affordance theory, we address the research question: “How can digital platformisation facilitate public sector transformation?” Based on the findings and the technology affordance theory, this study develops a transformational affordance framework (TAF) and offers propositions on how digital platforms can enable public sector transformation
The impact of COVID-19 on managing product returns in retail
No full textThe Covid-19 pandemic has affected customers' shopping and returns behaviour and significantly aggravated the problem of high product returns rates and returns fraud. Measures for public health and safety resulted in retailers modifying their returns processes. The effects of these changes on returns management are unclear; very little is known about what retailers have experienced in terms of product returns during the pandemic. This paper addresses this research gap via a series of semi-structured interviews and a consumer survey. Our findings include a list of recommendations for retailers to mitigate the effects of the pandemic on returns and related fraud
Forecasting digital asset return : an application of machine learning model
No full textIn this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable. We draw on a multivariate large data set of Bitcoin prices and its market microstructure variables and apply three machine learning models, namely double deep Q-learning, XGBoost and ARFIMA-GARCH. The findings show that the double deep Q-learning model outperforms the others in terms of returns and Sortino ratio and is capable of one-step-ahead sign forecast of the returns even on synthetic data. These critical insights in forecasting literature will support practitioners and regulators to identify an economically viable cryptocurrency forecasting return model
Testing the weak-form efficiency in African stock markets
No full text<p>Purpose – The purpose of this paper is to investigate and compare the weak-form efficiency of a set of 24 African continent-wide stock price indices and those of eight individual African national stock price indices.</p>
<p>Design/methodology/approach – Variance-ratio tests based on ranks and signs were used to examine the weak-form efficiency of the 32 stock price indices investigated.</p>
<p>Findings – On average, it was found that irrespective of the test employed, the returns of all the 24 African continent-wide stock price indices examined in the study are less non-normally distributed compared to the eight individual national stock price indices examined. The authors also report evidence of the African continent-wide stock price indices having significantly better weak-form informational efficiency than their national counterparts.</p>
<p>Practical implications – The policy implication of this evidence is that the African equity price discovery process can be significantly improved if African stock markets integrate their operations. Economically, this may contribute to improved liquidity and more efficient allocation of capital, which in turn can be expected to have a positive impact on economic growth.</p>
<p>Originality/value – The paper makes two major contributions to the extant literature. First, it offers for the first time a comparative analysis of the informational efficiencies of a sample of national stock price indices as against African continent-wide stock price indices. Second, there is no prior evidence as to whether African stock markets can improve their informational efficiencies by integrating their operations. The paper fills this gap by demonstrating that the African equity price formation process can be improved if African stock markets integrate their operations.</p>
Sustainability of product returns
No full textProduct returns are harmful to the environment due to increased transportation, waste and emissions (Frei et al., 2020). Whilst literature recognises the importance of sustainability in operations and supply chains, there is limited research on the environmental impact of returns and retailers’ views on sustainability in returns. To address these gaps, the first phase of this research consisted of a review of academic and other publications (e.g., blogs and reports by reverse logistics companies) to gain a comprehensive picture of the current product returns sustainability research and practice. The second phase was to undertake semi-structured interviews with retailers in the UK and Canada. We also participated in member sessions with the Efficient Consumer Response (ECR) Retail Loss Group to learn about the risks of implementing a sustainability strategy in returns under uncertain conditions in a pandemic world. The results from our first phase indicate that most sustainability studies to date excluded the analysis of returns (e.g., Mangiaracina et al., 2015). Although environmental assessment methods, such as Material flow analysis and Life-cycle assessment (Withanage and Habib, 2021), can assist practitioners in making more sustainable decisions in their forward supply chains, no study has proposed a systematic evaluation of the environmental impacts of reverse supply chains. Future research should assess this to help reduce the ecological damage caused by returns. The interviews conducted in the second phase revealed that retailers have only recently started paying attention to the returns’ financial impact. Many commented that they have not gained enough experience yet to manage returns sustainably. This is exacerbated by the lack of data and communications on returns’ costs and wastes ecologically. Additionally, the reverse logistics on returned products need further sustainable development. The scientific understanding of returns and their degrees of (un-)sustainability is still at an early stage. Retailers also highlighted that because of the pandemic, increased returns resulted in excessive transportation and processing (e.g., returned products needing quarantine). They are uncertain whether current customers’ returns behaviours will persist after the pandemic and are concerned about more unnecessary waste. Based on the obtained results, we produced recommendations on improving the ecological sustainability of returns. Overall, our study has made both practical and theoretical contributions in the field of sustainable returns operations and circular economy.<br/
Forecasting Digital Asset return: an Application of Machine Learning Model
No full textData Availability Statement:
Data sharing is not applicable to this article as no new data were created or analyzed in this study.In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable. We draw on a multivariate large data set of Bitcoin prices and its market microstructure variables and apply three machine learning models, namely double deep Q-learning, XGBoost and ARFIMA-GARCH. The findings show that the double deep Q-learning model outperforms the others in terms of returns and Sortino ratio and is capable of one-step-ahead sign forecast of the returns even on synthetic data. These critical insights in forecasting literature will support practitioners and regulators to identify an economically viable cryptocurrency forecasting return model
Search for , and Decays
No full textWe report a search for the decays and . We also measure other charmless decay modes with and final states. The results are obtained from a data sample containing pairs collected with the Belle detector at the KEKB asymmetric-energy collider. We set upper limits on the branching fractions for B^0\to K^{*0} \kstarbar and of and , respectively, at the 90% confidence level
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