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This study examines the forecasting ability of the adjusted implied volatility (AIV), which is suggested by Kang, Kim and Yoon (2009), using the horserace competition with historical volatility, model-free implied volatility, and BS implied volatility in the KOSPI 200 index options market. The adjusted implied volatility is applicable when investors are not risk averse or when underlying returns do not follow a normal distribution. This implies that AIV is consistent with the presence of risk premia for other risk such as volatility risk and jump risk. Using KOSPI 200 index options, it is shown that the AIV outperforms other volatility estimates in terms of the unbiasedness for future realized volatilities as well as the forecasting errors.

The purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.

This article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.

Findings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.

The originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

The purpose of this paper is to examine the impact of structural breaks on the conditional variance of carbon emission allowance prices.

The authors employ the symmetric GARCH model, and two asymmetric models, namely the exponential GARCH and the threshold GARCH.

The authors show that the forecast performance of GARCH models improves after accounting for potential structural changes. Importantly, we observe a significant drop in the volatility persistence of emission prices. In addition, the effects of positive and negative shocks on carbon market volatility increase when breaks are taken into account. Overall, the findings reveal that when structural breaks are ignored in the emission price risk, the volatility persistence is overestimated and the news impact is underestimated.

The authors are the first to examine how the conditional variance of carbon emission allowance prices reacts to structural breaks.

Firms can choose from an array of approaches for reducing the detrimental financial effects caused by unfavorable fluctuations in commodity prices. The purpose of this paper is to provide guidance for effectively estimating the financial effects of mitigating commodity price risk volatility (CPV) in supply chain management decisions.

This paper adopts two prominent and complementary methodologies, namely, total cost of ownership (TCO and real options valuation (ROV), to illustrate how commodity price risk mitigation strategies can be analyzed with respect to their effect on costs and performance. The paper provides insights through a case study to demonstrate the application of these methods together and establish the benefits and challenges associated with their implementation.

The paper illustrates advantages and disadvantages of TCO and ROV and how these approaches can be adopted together to contribute to effective purchasing decisions. Supply chain flexibility is a key capability but requires investments. Holistically measuring the financial effects of flexibility investments is imperative for gaining executive management support in mitigating commodity price volatility.

This study can provide supply chain professionals with useful guidance for measuring the costs and benefits related to developing strategies for mitigating commodity price volatility. TCO provides a focus on the costs associated with the commodity purchasing process, and ROV enables the aggregation of all the costs and benefits associated with the use of the strategy and synthesizes them into the net value estimate.

The paper provides a comparison of different but complementary approaches, specifically TCO and ROV, for analyzing the effectiveness of CPV risk mitigation decisions. In addition, these two methods allow supply chain professionals to evaluate and control the financial effects of CPV risk, particularly the impact of mitigation on firm’s cash flows.

As cryptocurrencies continue to gain viability as an asset class, institutional investors and publicly traded firms have started taking investment positions in digital currencies. What firms may not be considering, however, is the effect these assets may have on their risk profiles. This study aims to (1) measure the effect of cryptocurrencies on the risk and return characteristics of publicly traded companies; (2) decipher the motives behind holding cryptocurrencies as an asset class; and (3) determine whether one reason for holding is more effective than another. To conduct this research, the four largest publicly traded holders of cryptocurrency as well as four of the most prominent cryptocurrencies are explored.

The cross-sectional analysis approach has been used to analyze the daily returns, volatility, betas and Sharpe Ratios of firms during periods without cryptocurrency strategies and during periods with cryptocurrency strategies.

The impact of the cryptocurrency asset class on common stock performance and corporate disclosures are documented. The importance of risk disclosures on cryptocurrency holdings is emphasized: Firms must better inform their stakeholders through comprehensive disclosures in financial statements. Firms utilize cryptocurrencies for various reasons such as treasury management tools or as direct sources of income. Consequently, the impact on returns and risks varies substantially.

To the best of the authors’ knowledge, this is one of the first studies on cryptocurrency investments in the treasury departments of publicly traded companies. The study contributes to the literature by extracting relevant information regarding company risk reporting and cryptocurrency risk at firms. The conclusions also promote firm transparency with detailed reporting of cryptocurrency holding risks.

This study aims to investigate the influences of global uncertainty indicators volatility on the domestic socioeconomic and environmental vulnerability in a sample of 54 developing countries.

The two-step system generalized method of moments estimator is recruited to deal with autoregression and endogeneity matter in our dynamic panel data. Seven different global uncertainty indicators (US trade uncertainty; world trade uncertainty; economic policy uncertainty; world commodities and oil prices; the geopolitical risk index and the world uncertainty index) have been mobilized and compared for their empirical impact on the economic (growth and GDP), social (the misery index and income inequality) and environmental (CO₂ emissions) vulnerabilities of nations.

Our empirical estimations suggest that the socioeconomic and environmental vulnerability cannot be solved through the same pattern: all decrease of a particular aspect will necessarily have a cost and an opposite influence on at least one of the other aspects of the nations' vulnerability.

The originality of this article is to combine these three dimensions of vulnerability in the same investigation. To our knowledge, our research is one of the few providing a joint analysis of the influence of global uncertainty on the economic and socioenvironmental countries' vulnerabilities – given the fact social, economic and environmental aspects are at the heart of the UN sustainable goals, our study can be seen as an investigation of the nations' capabilities to work proactively on meaningful sustainable goals in an increasingly uncertain world.

The purpose of this paper is to examine the impact of the Goods and Service Tax (GST) implementation on Malaysian stock market index.

This study used daily closing prices of the Malaysian stock index and futures markets for the period ranging from June 2009 to November 2016. Empirical estimation is based on the generalised autoregressive conditional heteroscedasticity (1, 1) model for pre- and post-announcement of the GST.

Result shows that volatility of Malaysian stock market index increases in the post-announcement than in the pre-announcement of the GST which indicates that educative programs employed by the government before the GST announcement did not yield meaningful result. The volatility of the Malaysian stock market index is persistent during the GST announcement and highly persistent after the implementation. Noticeable increase in post-announcement is in support with the expectation of the market about GST policy in Malaysia.

The finding of this study is consistent with expectation of the market that GST policy will increase the price of the goods and services and might reduce standard of living. This is supported by a noticeable increase in the volatility of the Malaysian stock market index in the post-announcement of GST which is empirically shown during the announcement and after the implementation of GST. Although the GST announcement could be classified as a scheduled announcement, unwillingness to accept the policy prevails in the market as shown by the increase in the market volatility.

Past studies on Malaysian stock market index volatility focus on the impact of Asian and global financial crisis whereas this study examines the impact of the GST announcement and implementation on the volatility of the Malaysian stock market index.

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

This study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.

The study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan’s (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West’s model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.

The study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.

The study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

The purpose of this paper is to study the evolution of financial contagion between Eurozone banks, observing the credit default swaps (CDSs) market during the period 2009–2017.

The authors use a dynamic spatial Durbin model that enables to explore the direct and indirect effects over the short and long run and the transmission channels of the contagion.

The results show how contagion emerges through physical and financial market links between banks. This finding implies that a bank can fail because people expect other related financial institutions to fail as well (self-fulfilling crisis). The study provides statistically significant evidence of the presence of credit risk spillovers in CDS markets. The findings show that equity market dynamics of “neighbouring” banks are important factors in risk transmission.

The research provides a new contribution to the analysis of EZ banking risk contagion, studying CDS spread determinants both under a temporal and spatial dimension. Considering the cross-dependence of credit spreads, the study allowed to verify the non-linearity between the probability of default of a debtor and the observed credit spreads (credit spread puzzle). The authors provide information on the transmission mechanism of contagion and, on the effects among the largest banks. In fact, through the study of short- and long-term impacts, direct and indirect, the paper classify banks of systemic importance according to their effect on the financial system.