Reassessment of cross-impact analysis

Five problems in cross-impact methodology are discussed, solutions are proposed, and a simple illustrative case is provided. The problems are lack of commutativity, probabilistic imbalances, double counting of impacts, linearity, and two-dimensionality.     

A Markovian cross-impact model

The author describes a cross-impact method which is based on Markov-chain theory. This allows scenarios to be derived which explicitly include the time dimension. An example of the use of the method is included which deals with the installation of nuclear reactors for energy production.     

A note on cross-impact analysis: a calculus for sequence-dependent events

As the example presented demonatrate, the analysis of cross-impactss amount a group of related events is somewhat more complex than perhaps heretofore imagined. Impacts of effects between such events very often carely depend on their temporal sequence. The event outcome space is accordingly multiplied considerably over the case where such dependency is ignored. The result is that high premium is placed on methods that seek to reduce analytical complexity without distorting essential event intrications.     

Scenario generation: limitations and developments in cross-impact analysis

Informed subjective judgement has a place in decision making, and cross-impact analysis may be useful in providing this information. The authors focus on the art of scenario generation, reviewing a number of existing procedures and noting their limitations. The information needs of decision makers are discussed and three alternative approaches are outlined and compared in terms of their relative efficiency.     

Problems in futures research: Delphi and causal cross-impact analysis

The explosive numerical growth of the futures-research community during the last 15 years has been astonishing. Its intellectual growth cannot be said to have advanced at an equal pace. This article looks at the inherent constraints that make reliance on expert judgement an essential part of futures research. Two methods are discussed: Delphi and cross-impact analysis. The latter part of the article examines a new causal cross-impact model that avoids the criticisms that have been levelled at correlational cross-impact analysis.     

Taxation of foreign multinationals: A sequential bargaining approach to tax holidays

Tax holidays for foreign multinational firms are tax concessions or straight subsidies granted for a limited period after entry. Such phenomena occur widely. We apply a sequential bargaining framework to a problem of ex post bilateral monopoly characterized by the presence of a host-country government and a foreign multinational firm. We show formally how a tax holiday can arise due to irreversible outlays and outside options.     

Stock authorizations: A sequential equilibrium analysis

We develop a sequential equilibrium model of the common stock authorization process. We provide conditions under which actions that increase the number of slack shares, such as stock authorizations, generate negative announcement effects.     

The evaluation of generic cross-impact models

The first cross-impact models were developed ten years ago. Since then, many versions of this technique have been developed. This article proposes criteria for evaluating generic cross-impact models and demonstrates the use of these criteria. It distinguishes carefully between generic cross-impact models and application models. Generic models consist of mathematical definitions and computational procedures; application models consist of a generic model plus appropriately expressed data relevant to the topic being analysed. Four criteria are proposed for evaluating generic cross-impact models: internal consistency, robustness, generality, and clarity.     

Balancing laws for R-space in cross-impact models

This note is concerned with mathematical procedures for adjusting expert estimates of probabilities of events occurring, so that they fit classical probability axioms. Issue is taken with some points raised by Alter in a previous Futures.     

Two complementary cross-impact models: MIP1 and MIP2

The postulates on which probabilistic cross-impact models are built have progressed considerably and still vary today from author to author. The author reviews the characteristics of these models and the objectives that their users can hope to achieve. Two new models, MIP₁ and MIP₂, designed to minimise the disadvantages of existing models, are proposed.     

A factor analysis approach to measuring European loan and bond market integration

By using an existing and a new convergence measure, this paper assesses whether bank loan and bond interest rates are converging for the non-financial corporate sector across the euro area. Whilst we find evidence for complete bond market integration, the market for bank loans remains segmented, albeit to various degrees depending on the type and size of the loan. Factor analysis reveals that rates on large loans and small loans with long rate fixation periods have weakly converged in the sense that, up to a fixed effect, their evolution is driven by common factors only. In contrast, the price evolution of small loans with short rate fixation periods is still affected by country-specific dynamic factors. There are few signs that bank loan rates are becoming more uniform with time.     

A novel two-stage approach for cryptocurrency analysis

Modelling and quantifying the underlying characteristics of the cryptocurrency market has drawn increasing attention since Bitcoin went online in 2009. This study proposes a two-stage decomposition and composition method (2SDC) that begins with a Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for better interpreting cryptocurrency formations. This study involves daily closing price data from six cryptocurrencies (i.e., Bitcoin, Ethereum, Bitcoin Cash, Litecoin, Monero and Dash) from July 23rd, 2017 to July 23rd, 2019. In the first stage, six time series are jointly decomposed into 10 independent intrinsic mode functions (IMF) from high to low frequency plus one residual. In the second stage, the IMFs for each cryptocurrency are composed into three components based on Wilcoxon signed-rank test, including high and low frequency components and a long-term trend. These three multi-scale components can be interpreted as short-term fluctuations caused by investor sentiment and micro-structure, the effect of significant events and fundamental values. Furthermore, we demonstrated that the low and high frequency compositions are determining factors of cryptocurrency prices, which supports for the existing evidence (e.g. Bouoiyour, Selmi, Tiwari, & Olayeni, 2016; Ji, Bouri, Lau, & Roubaud, 2019).     

A PDE approach to jump-diffusions

In this paper, we show that the calibration to an implied volatility surface and the pricing of contingent claims can be as simple in a jump-diffusion framework as in a diffusion framework. Indeed, after defining the jump densities as those of diffusions sampled at independent and exponentially distributed random times, we show that the forward and backward Kolmogorov equations can be transformed into partial differential equations. This enables us to (i) derive Dupire-like equations [Risk Mag., 1994, 7(1), 18–20] for coefficients characterizing these jump-diffusions; (ii) describe sufficient conditions for the processes they induce to be calibrated martingales; and (iii) price path-independent claims using backward partial differential equations. This paper also contains an example of calibration to the S&P 500 market.     

An integrated approach to supply chain risk analysis

Despite the increasing attention that supply chain risk management is receiving by both researchers and practitioners, companies still lack a risk culture. Moreover, risk management approaches are either too general or require pieces of information not regularly recorded by organisations. This work develops a risk identification and analysis methodology that integrates widely adopted supply chain and risk management tools. In particular, process analysis is performed by means of the standard framework provided by the Supply Chain Operations Reference Model, the risk identification and analysis tasks are accomplished by applying the Risk Breakdown Structure and the Risk Breakdown Matrix, and the effects of risk occurrence on activities are assessed by indicators that are already measured by companies in order to monitor their performances. In such a way, the framework contributes to increase companies’ awareness and communication about risk, which are essential components of the management of modern supply chains. A base case has been developed by applying the proposed approach to a hypothetical manufacturing supply chain. An in-depth validation will be carried out to improve the methodology and further demonstrate its benefits and limitations. Future research will extend the framework to include the understanding of the multiple effects of risky events on different processes.