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Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and forecast accuracy. The judgment and decision-making (J/DM) literature suggests that those with more expertise will not perform better when tasks exhibit either extremely high or extremely low complexity. Expertise is expected to contribute to superior performance for tasks between these two extremes. Using archival data, this research examines the effect of analysts' expertise on forecasting performance by taking into consideration the forecasting task's complexity. Results indicate that expertise is not an explanatory factor for forecast accuracy when the forecasting task's complexity is extremely high or low. However, when task complexity falls between these two extremes, expertise is a significant explanatory variable of forecast accuracy. Both results are consistent with our expectations.

Unfortunately, the answers given are thoroughly embedded in the physics-inspired view of the financial economy as a stable and an equilibrium seeking system. In such a view, if some changes do occur in the financial markets, those changes present no discontinuities and the model has ample time to react by slowly adjusting risk forecasts as the volatility rises. As almost everybody in the world by now knows, currently accepted risk models have time and again shown their inability to deal with financial market reality. Frequent talk of ‘hundred year floods’ and ‘rise in correlations’ not only suggests frequent failures of a theory, but also the inability of the theory to learn from past mistakes by incorporating new data. The crash of 2008, completely unforeseen by all traditional risk systems, should serve as the final wake-up call to re-examine the foundations of the old paradigm and consider how sound they really are.

Purpose – The purpose of this chapter is to investigate the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements.

Methodology/Approach – We use a biorhythm approach characterized by the seasonal affective disorder (SAD) variable to study the impact of emotion on energy markets. Normal times and periods of extreme price movements are approximated by OLS and quantile estimations, respectively.

Findings – We use European energy forward prices of oil, gas, coal, and electricity. European equity future index (Dow Jones Euro Stoxx 50) and euro/dollar US exchange rate are used as control variables for economic and financial environment. Estimating OLS and quantile regressions, we find that seasonal patterns have a significant impact during extreme volatility periods only. Further investigations reveal that the SAD effect is significant during periods of price decrease, but insignificant during price increase times. The out-of-sample predictive ability properties show that our “SAD model” outperforms significantly the pure “macroeconomic” one.

Originality/Value of chapter – This topic is novel in energy finance since I use psychological background theory to understand energy price dynamics. I illustrate the relevance of our approach by comparing the out-of-sample predictive ability of our model against macroeconomic one. My results could be considered to improve energy porfolio allocation.

Changing climate dynamics have resulted in a confluence of disaster events to which Louisiana government leaders and emergency managers have never before had to respond simultaneously: a global pandemic and an “epidemic” of landfalling hurricanes during the 2020 season (eight cones over Louisiana) with challenging, unusual characteristics: (1) two hurricanes passing over the same location within 36 hours, a fujiwhara – Hurricanes Marco and Laura, (2) 150 mile-per-hour winds inadequately forecasted and of an almost unprecedented speed, (3) a difficult to forecast surge magnitude that led to incorrect immediate response, (4) delayed long-term recovery efforts from responders outside of the area because of initial reporting errors regarding surge heights and wind speed, and (5) a storm, Zeta, that passed directly over a densely populated area that would have been hard hit by rain if the storm had slowed. In addition, the number and closeness in dates of storm occurrences led to lengthy coastal high-water levels. To these co-occurring threats forecasters, state and local officials and residents responded with expertise and commitment, adhering to close collaboration, modifying evacuations and undertaking protective measures, all contributing to a low death rate from storms and a modest death rate from COVID. More just outcomes were supported by the general capacity of the responders, commitment to keep the residents informed about both risks and appropriate responses to them and the provision of special services, calculated for the new situation of the pandemic and the storm epidemic, for those without the means to respond adequately to both.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

In this work, we estimated the impact that the US VIX, economic policy and epidemic uncertainty indexes had on leisure and recreation stocks. We extended the current literature in two ways: first, we estimated the smoothed probabilities of being in ‘normal’ ( s = 1 ), ‘distress’ ( s = 2 ) and ‘crisis’ ( s = 3 ) episodes in the Refinitiv global leisure and recreation index. Then, we estimated the influence that the VIX and uncertainty indexes had on the generation of distress and crisis episodes in these stocks. By using logit regressions, we found out that only the US Economic policy uncertainty index is a detonator of distress and crisis episodes. We also found that the pandemic (COVID-19) news uncertainty has no significant and direct influence on the smoothed probabilities. Finally, and complementary to the current literature, we found that the volatility spillover effect from the S&P 500 to these stocks generates extreme volatility (crisis) episodes. Our results could be of use for practitioners and scholars and could provide a model to forecast distress and crisis episodes among leisure and recreation stocks. This model could be used for potential portfolio management or economic (tourism) policy purposes.

This chapter discusses methodological challenges that may be faced by researchers interested in financial markets in relation to the COVID-19 pandemic. In particular, we focus on the behaviour of investors and consider three aspects that affect their investment decision process, namely comovement, cross-sectional asset pricing, and out-of-sample forecasting. We argue that, in relation to the pandemic, relevant financial time series such as asset returns exhibit nonlinear dynamics, which should be suitably incorporated within appropriate methodological tools. We discuss possible existing approaches that ensure that those nonlinearities are properly accounted for. Finally, possible areas of future research are touched upon.

Without short-sales constraints, mean-variance (MV) and power-utility portfolios generated from historical data are often characterized by extreme expected returns, standard deviations, and weights. The result is usually attributed to estimation error. I argue that modeling error, that is, modeling the portfolio problem with just a budget constraint, plays a more fundamental role in determining the extreme solutions and that a more complete analysis of MV problems should include realistic constraints, estimates of the means based on predictive variables, and specific values of investors’ risk tolerances. Empirical evidence shows that investors who utilize MV analysis without imposing short-sales constraints, without employing estimates of the means based on predictive variables, and without specifying their risk tolerance miss out on remarkably remunerative investment opportunities.

Purpose – This chapter aimed to investigate hedge funds market risk. One aims to go further the traditional measures of risk that underestimates it by introducing a more appropriate method to hedge funds. One demonstrates that daily hedge fund return distributions are asymmetric and leptokurtic. Furthermore, volatility clustering phenomenon and the existence of ARCH effects demonstrate that hedge funds volatility varies through time. These features suggest the modelisation of their volatility using symmetric (GARCH) and asymmetric (EGARCH and TGARCH) models used to evaluate a 1-day-ahead value at risk (VaR).

Methodology/Approach – The conditional variances were estimated under the assumption that residuals t follow the normal and the student law. The knowledge of the conditional variance was used to forecast 1-day-ahead VaR. The estimations are compared with the Gaussian, the student and the modified VaR. To sum up, 12 VaRs are computed; those based on standard deviation and computed with normal, student and cornish fisher quantile and those based on conditional volatility models (GARCH, TGARCH and EGARCH) computed with the same quantiles.

Findings – The results demonstrate that VaR models based on normal quantile underestimate risk while those based on student and cornish fisher quantiles seem to be more relevant measurements. GARCH-type VaRs are very sensitive to changes in the return process. Back-testing results show that the choice of the model used to forecast volatility has an importance. Indeed, the VaR based on standard deviation is not relevant to measure hedge funds risks as it fails the appropriate tests. On the opposite side, GARCH-, TGARCH- and EGARCH-type VaRs are accurate as they pass most of the time successfully the back-testing tests. But, the quantile used has a more significant impact on the relevance of the VaR models considered. GARCH-type VaR computed with the student and especially cornish fisher quantiles lead to better results, which is consistent with Monteiro (2004) and Pochon and Teïletche (2006).

Originality/Value of chapter – A large set of GARCH-type models are considered to estimate hedge funds volatility leading to numerous evaluation of VaRs. These estimations are very helpful. Indeed, public savings under institutional investors management then delegate to hedge funds are concerned. Therefore, an adequate risk management is required. Another contribution of this chapter is the use of daily data to measure all hedge fund strategies risks.

This paper compares the out-of-sample forecasting performance of three long-memory volatility models (i.e., fractionally integrated (FI), break and regime switching) against three short-memory models (i.e., GARCH, GJR and volatility component). Using S&P 500 returns, we find that structural break models produced the best out-of-sample forecasts, if future volatility breaks are known. Without knowing the future breaks, GJR models produced the best short-horizon forecasts and FI models dominated for volatility forecasts of 10 days and beyond. The results suggest that S&P 500 volatility is non-stationary at least in some time periods. Controlling for extreme events (e.g., the 1987 crash) significantly improved forecasting performance.