Shujian Ma, Jiarong Cai, Gang Wang, Xiangliang Ge · 6 authors
The application of blockchain has become a trend in the development of supply chain finance. Aiming to bridge the gap in the existing literature, this paper investigates a supply chain finance system based on blockchain technology which contains a manufacturer, a retailer and a financial institution and incorporates blockchain costs into the model. Firstly, this paper establishes a supply chain finance model based on blockchain technology and it presents a comparison with the process employed under the traditional model. Secondly, this paper establishes the revenue mathematical model of supply chain finance based on blockchain technology. Thirdly, the optimal decisions of each participant under centralized and decentralized decision-making are proved and obtained, respectively, and the influencing factors of the optimal decisions are analyzed. Finally, the conclusions are verified via simulations. This study finds that, when blockchain is used, the benefits of each participant in the chain are increased. In addition, centralized decision-making, which is more optimal in the traditional model, is also enhanced under blockchain. This paper demonstrates the superiority of blockchain-enabled supply chain finance in terms of model and revenue. This provides some suggestions for companies in the supply chain with regard to solving the problem of financing difficulties.
To demonstrate the flexibility and power of stochastic optimal control theory and the numerical methods therein, this thesis tackles three applied problems which have significant implications in their application fields. The thesis first discusses an optimal control problem in pandemic modeling and mitigation which incorporates novel macroeconomic elements. By modeling the reactions of individuals to current infection levels through personal protective measures which alter disease transmission, improvements in public health outcomes can be achieved with minimal macroeconomic sacrifices. This research was motivated by the 2020 pandemic and presents a tool to aid policymakers in making informed decisions when facing public health crises. Another area that has developed rapidly in recent years is the application of machine learning to problems that would be extremely difficult to solve analytically or with traditional grid-based numerical methods. This thesis extends the literature by developing an efficient and accurate machine learning algorithm to solve the high-dimensional optimal switching problem faced by a power plant operator under uncertain production costs and profits. The algorithm is able to perform accurately on high-dimensional models without suffering from extremely long run times arising from the curse of dimensionality. This could facilitate quicker power plant operator decisions when facing stochastic changes in input factors. Not only does the thesis consider cutting edge technologies for solving stochastic optimization problems, but it also seeks to investigate emerging technologies in financial markets. The thesis combines models of competitive games with empirical data to investigate competition between liquidity providers in a decentralized cryptocurrency exchange. It demonstrates that a Stackelberg game between a mean field of liquidity providers as the leader and a market manipulator as the follower is able to produce extremely accurate predictive results, indicating that the model is accurately capturing pool dynamics and has potential for use in decentralized finance.
Cryptocurrencies are said to be very risky, and so are the currencies of emerging economies, including the South African rand. The steady rise in the movement of South Africans’ investments between the rand and BitCoin warrants an investigation as to which of the two currencies is riskier. In this paper, the Generalised Pareto Distribution (GPD) model is employed to estimate the Value at Risk (VaR) and the Expected Shortfall (ES) for the two exchange rates, BitCoin/US dollar (BitCoin) and the South African rand/US dollar (ZAR/USD). The estimated risk measures are used to compare the riskiness of the two exchange rates. The Maximum Likelihood Estimation (MLE) method is used to find the optimal parameters of the GPD model. The higher extreme value index estimate associated with the BTC/USD when compared with the ZAR/USD estimate, suggests that the BTC/USD is riskier than the ZAR/USD. The computed VaR estimates for losses of $0.07, $0.09, and $0.16 per dollar invested in the BTC/USD at 90%, 95%, and 99% compared to the ZAR/USD’s $0.02, $0.02, and $0.03 at the respective levels of significance, confirm that BitCoin is riskier than the rand. The ES (average losses) of $0.11, $0.13, and $0.21 per dollar invested in the BTC/USD at 90%, 95%, and 99% compared to the ZAR/USD’s $0.02, $0.02, and $0.03 at the respective levels of significance further confirm the higher risk associated with BitCoin. Model adequacy is confirmed using the Kupiec test procedure. These findings are helpful to risk managers when making adequate risk-based capital requirements more rational between the two currencies. The argument is for more capital requirements for BitCoin than for the South African rand.
Meriem Youssef, Bouthaina Ben Naoua, Fouad Ben Abdelaziz, Messaoud Chibane
Abstract This paper analyzes the diversification benefits of adding alternative crypto‐assets in a traditional portfolio, from the perspective of an investor who seeks to achieve multiple objectives. Our analysis is based on daily and weekly return data for eight different assets including Bitcoin, Ethereum, Ripple, for the crypto‐assets and NASDAQ, S&P500, Dow‐Jones, Crude‐Oil, and Gold, for traditional assets. We use both in‐sample and out‐of‐sample estimation procedures to analyze these data sets. We apply the weighted sum of deviations goal programming method to solve a bi‐objective optimization problem where investors optimize simultaneously portfolio risk and return. For a variety of investor characteristics, ranging from risk‐seeking to risk‐averse, we show that augmenting portfolios with alternative crypto‐currencies improves portfolio performance and the efficiency frontier, compared to standard portfolios. This improvement is even more observable for risk‐seeking investors, for both in‐sample and out‐of‐sample estimation procedures.
We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.
Financial institutions own balanced portfolios with many assets and hence a small risk. But they are not able to split them into smaller parts with comparable risk for resale due to regulators' restrictions caused by a lack of auditability. Blockchain and smart contracts allow overcoming this problem via tokenizing assets into a commodity. The paper seeks to answer the question: how to assemble as many as possible standardized packages from a given portfolio. The optimal algorithms for two special cases-discrete and continuous homogeneous-are provided.
This research studies the risk decision-making (RDM) problem faced by participants in a spacecraft supply chain, considering the adoption of the blockchain technology to facilitate information sharing. We investigate a three-level spacecraft supply chain composed of a spacecraft builder, supplier, and logistics service integrator with the Stackelberg game under the scenarios of decentralized, partially centralized, and fully centralized decision-making. For each scenario, we show how the spacecraft builder’s optimal order quantity and the supplier’s application degree of the blockchain technology change with the spacecraft builder’s risk coefficient. We also compare the overall profit of the supply chain for three decision-making scenarios.
The overall performance of a portfolio is the utmost measure of success for the skills of the portfolio manager. The Sharpe ratio and the modified Sharpe ratio have been some of the most referenced standards used in finance, to evaluate the efficiency of funds and hedge fund managers, however; such an ordering should be accompanied by proper statistical inference. In this work we examine whether Bitcoin can diversify significantly a reference portfolio composed from the five best performers of the Dow Jones industrial average in 2017 that is, Apple, Boeing, Caterpillar, Visa, and Walmart. The portfolios are constructed via analytical solutions in the mean-variance framework, constrained optimisation for the cases of long-only and risk-parity portfolios, and an equal weight strategy. The statistical significance of the Sharpe and modified Sharpe ratio differences is examined via a variety of tests. The results indicate that Bitcoin can significantly improve only the Sharpe and modified Sharpe ratios of the minimum variance and risk-parity portfolios. On the efficient frontier, the tangent portfolios are dominated by the traditional stocks.
Investoren und Vermögensverwalter suchen nach Finanzinstrumenten, die die erwartete Rendite ihrer Investition bei gleichzeitiger Minimierung des potenziellen Risikos erhöhen. In der Praxis diversifizieren Vermögensverwalter ihre Vermögensallokation auf verschiedene Anlageklassen allen voran Aktien, Anleihen und Rohstoffe. In den letzten Jahren gewinnt die neue Anlageklasse der Kryptowährungen immer mehr an Einfluss - Anlagekapital. Die erste unter ihnen, Bitcoin, wurde wegen ihrer Technologie sehr berühmt: dezentrale Datenhaltung, sicheres und schnelles elektronisches Tausch- und Zahlungsmittel. Diese Arbeit konzentriert sich auf die Leistung der Core-Satellite Strategie mit diesen neuen digitalen Währungen als Satellit. Die Herausforderung besteht darin, die Kryptowährungen zu finden, die Abwärtstrends kompensieren kann und dem Anleger bessere Renditen erwirtschaftet. Die Korrelationsstruktur der Kryptowährungen muss untersucht werden, sodass gegenläufige Kryptowährungen ausgewählt werden können. Dazu verwenden wir TEDAS – Tail Event Driven Asset allocation, eine aktive Investitionsstrategie zur Auswahl der Kryptowährungen. Diese Methode untersucht die Abhängigkeit von Kryptowährungen in verschiedenen Quantilen am linken Rand der Verteilung. Die Arbeit vergleicht verschiedene auf TEDAS basierenden Investitionsstrategie.
Pedro Bonillo Bueno, Emilio Aragon Fortes, Konstantinos Vlachoski
Since its launch in 2008, Bitcoin becomes one of the most successful and fast-growing alternative currencies. As of 2017, the market capitalization is around $46 billion and arguably expected to continue growing. The Bitcoin to the US dollar exchange rate has been very volatile and fluctuating significantly. Although Bitcoin was designed as a medium of exchange, it is now more as an investment tool and thus the development of effective quantitative risk management tools becomes quite urgent for all the market participants. In this paper, we investigate empirical distribution of the Bitcoin exchange rate returns by using four types of widelyused heavy-tailed distribution and show that the Skewed t distribution has the best empirical performance. We further calculate the VaR based risk measures and found the Skewed t distribution generates the VaR values, which are closest to historical VaR values. Our results could be directly used in the industry’s stress testing practice, and help financial institutions fulfill the regulatory requirements.
The minmax regret problem for combinatorial optimization under uncertainty\ncan be viewed as a zero-sum game played between an optimizing player and an\nadversary, where the optimizing player selects a solution and the adversary\nselects costs with the intention of maximizing the regret of the player. The\nexisting minmax regret model considers only deterministic solutions/strategies,\nand minmax regret versions of most polynomial solvable problems are NP-hard. In\nthis paper, we consider a randomized model where the optimizing player selects\na probability distribution (corresponding to a mixed strategy) over solutions\nand the adversary selects costs with knowledge of the player's distribution,\nbut not its realization. We show that under this randomized model, the minmax\nregret version of any polynomial solvable combinatorial problem becomes\npolynomial solvable. This holds true for both the interval and discrete\nscenario representations of uncertainty. Using the randomized model, we show\nnew proofs of existing approximation algorithms for the deterministic model\nbased on primal-dual approaches. Finally, we prove that minmax regret problems\nare NP-hard under general convex uncertainty.\n
Bitcoin is an unregulated digital currency originally introduced in 2008 without legal tender status. Based on a decentralized peer-to-peer network to confirm transactions and generate a limited amount of new bitcoins, it functions without the backing of a central bank or any other monitoring authority. In recent years, Bitcoin has seen increasing media coverage and trading volume, as well as major capital gains and losses in a high volatility environment. Interestingly, an analysis of Bitcoin returns shows remarkably low correlations with traditional investment assets such as other currencies, stocks, bonds or commodities such as gold or oil. In this paper, we shed light on the impact an investment in Bitcoin can have on an already well-diversified investment portfolio. Due to the non-normal nature of Bitcoin returns, we do not propose the classic mean-variance approach, but adopt at Conditional Value-at-Risk framework that does not require asset returns to be normally distributed. Our results indicate that Bitcoin should be included in optimal portfolios. Even though an investment in Bitcoin increases the CVaR of a portfolio, this additional risk is overcompensated by high returns leading to better risk-return ratios.
Value at risk (VaR) is a central concept in risk management. As stressed by Artzner et al. (1999, Coherent measures of risk, Math. Finance 9(3) 203–228), VaR may not possess the subadditivity property required to be a coherent measure of risk. The key idea of this paper is that, when tail thickness is responsible for violation of subadditivity, eliciting proper conditioning information may restore VaR rationale for decentralized risk management. The argument is threefold. First, since individual traders are hired because they possess a richer information on their specific market segment than senior management, they just have to follow consistently the prudential targets set by senior management to ensure that decentralized VaR control will work in a coherent way. The intuition is that if one could build a fictitious conditioning information set merging all individual pieces of information, it would be rich enough to restore VaR subadditivity. Second, in this decentralization context, we show that if senior management has access ex post to the portfolio shares of the individual traders, it amounts to recovering some of their private information. These shares can be used to improve backtesting to check that the prudential targets have been enforced by the traders. Finally, we stress that tail thickness required to violate subadditivity, even for small probabilities, remains an extreme situation because it corresponds to such poor conditioning information that expected loss appears to be infinite. We then conclude that lack of coherence of decentralized VaR management, that is VaR nonsubadditivity at the richest level of information, should be an exception rather than a rule.