Can the “VUCA Meter” Augment the Traditional Project Risk Identification Process? A Case Study

In this rapidly changing and fast-growing world, sustainability is an important paradigm. However, the constantly growing level of uncertainty leads to increased strain in decision making. This results in a growing need for a more effective and extensive approach for identifying project risk in particular events that are not easily detected but can have a severe impact, sometimes referred to as Black Swans or “fat tail” events. The VUCA meter is a normative approach to identify project risk by assessing in a structured way events that may be volatile, uncertain, complex, and ambiguous and might contribute to the project risk. In this study, the VUCA meter is benchmarked against a traditional risk identification process as recommended by PMI®. Firstly, two workshops, each referring to the respective risk identification method, were conducted. Secondly, a Delphi survey was run to investigate if the VUCA meter would capture Black Swan risk events that are bypassed by the traditional risk identification approach. The results clearly indicate that the VUCA meter can be developed to be a significant addition to the conventional risk identification process for large projects that are at an early stage. The VUCA meter facilitates a discussion that gets people to think beyond the traditional framework for identifying project risk factors. As a consequence, “fat tail” events, that are not apprehended with the conventional technique, are captured by the VUCA meter.

Risk and Ambiguity in Turbulent Times

This paper focuses on the 2008–2020 period during which two major crises, affecting the economy and the financial markets, occurred. Between 2008 and 2020, there were less extreme tail events, including the lingering Eurozone and Greece crises. In particular, after extremely high stock market volatility and volatility of volatility (VoV) during 2008, the long-run average volatility declined to about 20% and the VoV to around 100%. This paper analyzes this period through the lens of risk and ambiguity (uncertainty). It aims to address the question: what are the financial markets that trade risk — the volatility derivatives markets — telling us? To this end, this paper uses several measures of uncertainty. It reviews the history of volatility and uncertainty measures and discusses their informativeness. It then discusses the information derived from volatility derivatives.

Macroeconomic and Financial Risks: A Tale of Mean and Volatility

We study the joint conditional distribution of GDP growth and corporate credit spreads using a stochastic volatility VAR. Our estimates display significant cyclical co-movement in uncertainty (the volatility implied by the conditional distributions), and risk (the probability of tail events) between the two variables. We also find that the interaction between two shocks--a main business cycle shock as in Angeletos et al. (2020) and a main financial shock--is crucial to account for the variation in uncertainty and risk, especially around crises. Our results highlight the importance of using multivariate nonlinear models to understand the determinants of uncertainty and risk.

Sharing Asymmetric Tail Risk: Smoothing, Asset Prices and Terms of Trade

Crises and tail events have asymmetric effects across borders, raising the value of arrangements improving insurance of macroeconomic risk. Using a two-country DSGE model, we provide an analytical and quantitative analysis of the channels through which countries gain from sharing (tail) risk. Riskier countries gain in smoother consumption but lose in relative wealth and average consumption. Safer countries benefit from higher wealth and better average terms of trade. Calibrated using the empirical distribution of moments of GDP-growth across countries, the model suggests non-negligible quantitative effects. We offer an algorithm for the correct solution of the equilibrium using DSGE models under complete markets, at higher order of approximation.

Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance

Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the main problems is to decide if there is any type of dependence between the components of the vector and, if so, what type of dependence structure should be used for accurate modelling. We study a class of heavy-tailed multivariate random vectors under a non-parametric shape constraint on the tail decay rate. This class contains, for instance, elliptical distributions whose tail is in the intermediate heavy-tailed regime, which includes Weibull and lognormal type tails. The study derives asymptotic approximations for tail events of random walks. Consequently, a full large deviations principle is obtained under, essentially, minimal assumptions. As an application, an optimisation method for a large class of Quota Share (QS) risk sharing schemes used in insurance and finance is obtained.

INDIAN RETAIL INVESTORS AND INITIAL PUBLIC OFFERS: PRE AND POST COVID ANALYSIS

Going public is one of the most popular forms of raising funds for expansion and growth of business. Since the liberalisation of economy in 1991, more than 1500 companies have listed themselves on the exchange. And the Indian stock markets keep expanding with increasing number of public offers in both mainstream and SME category. This paper compares the IPOs of Indian markets in broadly two phases-pre covid and post covid. A sample of 242 listings across eleven years from 2010-2020 are considered for the study. A comparison based on details of listing, listing gains etc reveal a more active retail investor segment. Overoptimism and urge to synthesise short term gains contribute to such gains. This is further backed by analysis of search results in the Indian region using google trends. Tail events such as covid-19 alter the way Indian investors behave and invest in IPOs and make their investments more on basis of speculative measures such as grey market premium, than actual fundamentals of the issue under consideration.

Tail Risk Hedging: Contrasting Put and Trend Strategies

We summarize key research findings on risk-mitigating strategies and offer an overview of the strengths and weaknesses of regular index put buying (“Put”) and multi-asset trend following (“Trend”) as tail hedges. The two biggest questions we address are: (1) What is the long-term average return or cost, and (2) How reliable and efficient is the hedge in equity market tail events? We present empirical answers and discuss the economic rationale for each question. The common view that Put costs more but is a more effective tail hedge contains a kernel of truth but does not capture the full story. We will give a more nuanced picture, including practicality for investors, but in the end show the cost advantage favors Trend over Put.

Tail Risk and Extreme Events: Connections between Oil and Clean Energy

Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an asymmetry effect between the response to good versus bad days. The results show how the two markets influence each other more negatively, i.e., extreme negative events significantly impact the other market. Furthermore, we document how the impact of the shock transmitted by oil prices to clean-energy stocks is less than the amount of shock transmitted oppositely. These findings have important implications for investor and renewable energy policies.