Forward-looking Measure Research Articles

Dynamic linkages among bitcoin, equity, gold and oil: An implied volatility perspective

Bitcoin Implied Volatility, derived from the recently launched options on Bitcoin, provides a new forward-looking measure of uncertainty in financial markets. This paper explores its long-run and short-run relationships with the implied volatilities of equity, gold, and oil. The NARDL model reveals a long-run relationship among Bitcoin volatility and others. Kyrtsou-Labys Nonlinear Causality tests established pair-wise causality between the implied volatilities under study. Our findings highlight the integration of Bitcoin markets with global financial markets, indicating the coming of age of the Bitcoin market.

Compound extreme inundation risk of coastal wetlands caused by climate change and anthropogenic activities in the Yellow River Delta, China

The coastal wetlands of the Yellow River Delta (YRD) in China are crucial for their valuable resources, environmental significance, and economic contributions. However, these wetlands are also vulnerable to the dual threats of climate change and human disturbances. Despite substantial attention to the historical shifts in YRD's coastal wetlands, uncertainties remain regarding their future trajectory in the face of compound risks from climate change and anthropogenic activities. Based on a range of remote sensing data sources, this study undertakes a comprehensive investigation into the evolution of YRD's coastal wetlands between 2000 and 2020. Subsequently, the potential fate of coastal wetlands is thoroughly analyzed through the Land Use/Cover Change (LUCC) simulation using System Dynamic-Future Land Use Simulation (SD-FLUS) model and the extreme water levels projection integrated future sea-level rise, storm surge, and astronomical high tide in 2030, 2050, and 2100 under scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results revealed that YRD's coastal wetlands underwent a marked reduction, shrinking by 1688.72 km2 from 2000 to 2020. This decline was mostly attributed to the substantial expansion in the areas of artificial wetlands (increasing by 823.78 km2), construction land (increasing by 767.71 km2), and shallow water (increasing by 274.58 km2). Looking ahead to 2030–2100, the fate of coastal wetlands appears to diverge based on different scenarios. Under the SSP1-2.6 scenario, the area of coastal wetland is projected to experience considerable growth. In contrast, the SSP5-8.5 scenario anticipates a notable decrease in coastal wetlands. Relative to the inundated area suffered from the current extreme water levels, the study projects a decrease of 6.8%–10.6% in submerged coastal wetlands by 2030 and 9.4%–18.2% by 2050 across all scenarios. In 2100, these percentages are projected to decrease by 0.4 % (SSP2-4.5) and 27.1% (SSP5-8.5), but increase by 35.7% (SSP1-2.6). Results suggest that coastal wetlands in the YRD will face a serious compound risk from climate change and intensified human activities in the future, with climate change being the dominant factor. More efficient and forward-looking measures must be implemented to prioritize the conservation and management of coastal wetland ecosystems to address the challenges, especially those posed by climate change.

Analyst coverage and the probability of stock price crash and jump

PurposeThe aim of this paper is to document the effect of analyst coverage on the ex ante probability of stock price crash and the ex ante probability stock price jump.Design/methodology/approachThis paper uses the data of non-financial firms from France to test the arguments presented in this paper during the period between 1997 and 2019. The paper also uses flexible quadrants copulas to compute the ex ante probabilities of crashes and jumps.FindingsThe results show that the extent of analyst coverage is positively associated with the ex ante probability of crash and negatively associated with the ex ante probability of jump. The results remain qualitatively the same after several sensitivity checks. The results also show that the relationship between the extent of analyst coverage and the probability of cash and the probability of jump holds when ex post probability of stock price crash and stock price jump is used.Originality/valueUnlike most of the earlier papers on this topic, this paper uses the ex ante probability of crash and jump. This proxy is better suited than the ones used in the prior literature because it is a forward-looking measure.

A monthly leading indicator of Swiss GDP growth based on Okun’s law

We propose a unique method of nowcasting and forecasting GDP growth based on a forward-looking measure of unemployment (FLUR) and Okun’s law that offers a number of advantages over current leading indicators of the Swiss business cycle. The following investigation, covering the period from 1991/1 to 2021/4, demonstrates that our approach outperforms an AR(1) model of GDP growth equally well as the popular Business Cycle Index of the Swiss National Bank and the KOF Barometer with respect to year-to-year growth, but less so in regard to quarter-to-quarter changes. Our findings suggest that our approach offers a reliable and useful indicator to policymakers seeking easily compiled information on the current and future course of the Swiss economy at monthly time intervals.

Oil Price Uncertainty and M&A Activity

This study examines the impact of oil price uncertainty on mergers and acquisition (M&A) activity in the oil and gas sector. Analyzing this industry enables us to construct a natural forward-looking measure of oil price uncertainty, namely the implied crude oil volatility. Using a sample of U.S. firms in the oil and gas sector from 1994–2018 containing 4,323 announced transactions, we document that oil price uncertainty is negatively related to future M&A activity. Uncertainty is mainly a driver of horizontal and vertical M&A activity, where upstream firms are more affected by this uncertainty than downstream firms. Our results lend support to a real options explanation of investment under uncertainty where firms choose to defer investments as a response to increased uncertainty.

Scenario analysis and climate change: a literature review via text analytics

PurposeClimate change has a direct impact on companies. Therefore, the scenario analysis is used to provide companies and stakeholders in this specific sector with forward-looking measures and narratives of the world's future state. This work aims to provide an independent, wide and rigorous literature review on the topics of scenario analysis and climate change, analyzing a large set of referred papers included in economic journals on the Web of Science Clarivate Analytics data source. This review, by means of a mixed approach, can help address new policy strategies and business models.Design/methodology/approachThe work employs 416 abstracts and relative titles in the field of economics, employing data mining for qualitative variables and performing descriptive statistics and lexicometric measures, similarity analysis and clustering with Reinert's hierarchical method in order to extract knowledge. Furthermore, qualitative content analysis allows for the return of a comprehensive and complete universe of meaning, as well as the analysis of co-occurences.FindingsContent analysis reveals three main classification clusters and four unknown patterns: model area, risks, emissions and energy and carbon pricing, indicating research directions and limitations through an overview with an extensive reference bibliography. In the research, the prevalent use of quantitative instruments and their limitations emerge, while qualitative instruments are residual for climate change assessment; they also highlight the centrality of transition risk over adaptation measures and the combination of different types of instruments with reference to carbon pricing.Originality/valueScenario analysis is a relatively new topic in economics and finance research, and it is under-investigated by the academy. The analysis combines quantitative and qualitative research using text analytics.

Quantifying Vulnerability to Poverty in the Drought-prone Lowlands of Ethiopia

AbstractA forward-looking measure of ‘vulnerability to poverty’ is estimated and a concerted effort is made to understand the sources of vulnerability in the drought-prone lowlands of Ethiopia. Using the Household Consumption Expenditure Survey and the Welfare Monitoring Survey of 2015–16, which include additional zones in the Afar and Somali regions increasing the representativeness of the survey in pastoral areas, the analysis reveals that vulnerability is remarkably higher in the drought-prone lowlands than in the other ecological zones, even though differences in poverty rates are modest. The analysis also reveals important distinctions in the sources of vulnerability. In the drought-prone lowlands, (i) the prevalence of both poverty-induced and risk-induced vulnerability is the highest among all the ecological zones and (ii) the importance of vulnerability due to aggregate shocks, such as droughts, relative to vulnerability due to idiosyncratic shocks is higher than in the other ecological zones. These findings attest to the unique nature of the drought-prone lowlands in comparison to the other agroecological zones of Ethiopia and the need for adaptive social protection programmes targeting not only the chronically poor but also the vulnerable.

Anticipating jumps: Decomposition of straddle price

We develop a novel method to decompose a straddle into two assets: a volatility risk asset and a jump risk asset. Using the price ratio of the jump risk asset to the straddle, we create a forward-looking measure (S-jump) that captures the stock price jump risk anticipated by the option market. We show that S-jump substantially increases before earnings announcements and strongly predicts the size and the probability of earnings-induced stock price jumps. We also find that S-jump amplifies the earnings response coefficient. Our jump risk asset captures the run-up and run-down return patterns observed for straddles around earnings announcements.

DETERMINANTS OF SOVEREIGN CREDIT RATINGS: THE CASE OF INDONESIA AND PEER GROUP COUNTRIES

This paper aims to identify the predominant determinants of sovereign credit ratings in Indonesia and its peer countries from 2004 to 2019 by considering GDP per capita, official reserve, government debt, Current Account Balance, Credit Default Swap (CDS), and Sovereign Credit Rating used by the three major rating agencies. CDS is considered a newly proposed variable with predicting capacity as a measure of default. Sovereign credit ratings, which are forward-looking measures of the probability of default provided by rating agencies, are being criticized for their predictive capacity with the incorporation of historical data and information in their assessment. Therefore, this study also explores the possible effect of a newly proposed variable with predicting capacity, the CDS, as a pure measure of default. The correlations among the variables were analyzed with Panel Vector Autoregressive (PVAR) as all variables were treated as endogenous and interdependent. The empirical results demonstrate that among the variables tested, GDP Per Capita, Government Debt, and CDS, as a newly proposed variable, have statistically stronger relationships with the sovereign credit rating. The authority that manages the engagement with rating agencies is expected to develop a more focused strategy when taking this aspect into account. Keywords: Financial Market, Rating Agencies, Risk, Panel VAR, Sovereign Rating DOI： https://doi.org/10.35741/issn.0258-2724.58.1.78

Asymmetric Network Connectedness of Fears

Abstract This paper introduces forward-looking measures of the network connectedness of fears in the financial system arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put traded option prices of the main U.S. banks contains valuable information for predicting macroeconomic conditions and economic uncertainty, and it can serve as a tool for forward-looking systemic risk monitoring.

Price Uncertainty and Debt Covenants: Evidence from U.S. Oil Producers

Abstract We use an exogenous, forward-looking measure of price uncertainty to examine the effect of uncertainty on the use of covenants in private and public debt contracts. The effect of uncertainty differs significantly in loans versus bonds, suggesting that private and public lenders face different incentives when contracting under high uncertainty. In loans, uncertainty increases the use of performance-based covenants. In contrast, covenant usage decreases in bonds, but firms with low agency conflicts mainly drive this effect. However, bondholders require higher spreads to offset the lack of protection with fewer covenants. Hedging does not affect the covenant-uncertainty relationship for debt contracts. (JEL: G21, G23, G32, D82, D81)

Africa’s biofuel energy and emissions prospect: Forward-looking into 2030

Renewable and clean energies foster carbon abatement. This makes the prospects of emission mitigation through Biofuels vital in Africa’s energy transitioning agenda. As such, the role of Biofuel energies in abating Africa’s emissions is worthy of investigation. This study goes a step further, from existing studies, to analyse the carbon abatement prospects of biofuel in Africa’s energy transitioning journey. To this end, the top five leading African economies are sampled. These economies account for over 77% of Africa’s carbon emissions and over 60% of Africa’s output. Firstly, the carbon emission levels of these top 5 economies, from 1990 to 2019, are decomposed using the Index Decomposition Analysis (IDA). Then, based on these decompositions, the carbon mitigation intensities are derived and analyzed due to Biofuels consumption. Finally, three different scenarios are designed as a forward-looking measure toward reducing carbon emissions in Africa up to 2030. The findings support that Biofuel energy consumption substantially increases carbon abatement levels and intensities in Africa. As a policy implication from these findings; the cultivation, processing, and production of biofuels and their sources should be encouraged in all African economies.

Nonlinear dependence and spillovers between cryptocurrency and global/regional equity markets

In this paper, we investigate the nonlinear dependence dynamics among eight cryptocurrencies (Monero, Bitcoin, Dash, Litecoin, Stellar, XRP, Ethereum, and Nem) by applying time-varying copulas. We also examine the upside and downside spillovers between cryptocurrencies and equity markets by a conditional Value-at-Risk (CoVaR) approach. We show that the dynamics of dependence of the portfolio of cryptocurrencies reveal both symmetric and asymmetric features, with the symmetric dynamics being more predominant. NEM and Ethereum have the largest downside and upside CoVaR spillovers on the world equity index, respectively. The largest downside CoVaR spillovers from the world equity index are to NEM followed by Stellar, and the largest upside spillovers are to Ethereum followed by NEM. Stellar and Bitcoin exhibit the largest downside and upside CoVaR spillovers on the Americas equity index. The largest downside CoVaR spillovers from the Americas equity index are to Stellar and NEM, and those on the upside are to Ethereum and NEM. In addition, we find that most cryptocurrencies exhibit safe haven or hedge properties more often than rare metals and diamonds for daily equity indices. Finally, we conduct an out-of-sample analysis of optimal-weighting portfolio strategies based on C-vine copulas using cryptocurrencies and equity indices that entails forward-looking measures of risk that are economically significant, which outperform benchmark strategies.

Commodity return predictability: Evidence from implied variance, skewness, and their risk premia☆☆

This paper investigates the role of realized and implied moments and their risk premia (variance and skewness) for commodities’ future returns. We estimate these moments from high frequency and commodity futures option data that results in forward-looking measures. Risk premia are computed as the difference between implied and realized moments. We highlight, from a cross-sectional and time-series perspective, the strong positive relationship between commodity returns and implied skewness. Moreover, we emphasize the high performance of skewness risk premium. Additionally, we show that portfolios built by sorting skewness risk premium and implied skewness exhibit the best risk-return tradeoff. Most of our results are robust to other factors such as the momentum and roll yield.

The Market Measure of Carbon Risk and its Impact on the Minimum Variance Portfolio

Like environment, social, and governance investing, climate change is an important concern for asset managers and owners and a new challenge for portfolio construction. Until now, investors have mainly measured carbon risk using fundamental approaches, such as with carbon intensity metrics. Nevertheless, it has not been proven that asset prices are directly affected by these fundamental-based measures. In this article, the authors focus on another approach, which consists of measuring the sensitivity of stock prices with respect to a carbon risk factor. In the authors’ opinion, carbon betas are market-based measures that are complementary to carbon intensities or fundamental-based measures when managing investment portfolios; carbon betas may be viewed as an extension or forward-looking measure of the current carbon footprint. In particular, they show how this new metric can be used to build minimum variance strategies and how it affects portfolio construction. <b>TOPICS:</b>ESG investing, portfolio construction, tail risks, fundamental equity analysis <b>Key Findings</b> ▪ Measuring carbon risk is different if we consider a fundamental-based approach by using carbon intensity metrics or a market-based approach by using carbon betas. ▪ Managing relative carbon risk implies overweighting of green firms, whereas managing absolute carbon risk implies having zero exposure to the carbon risk factor. The first approach is an active management bet, whereas the second is an immunization investment strategy. ▪ Both specific and systematic carbon risks are important when building a minimum variance portfolio and justify combining fundamental and market approaches to carbon risk.

Financial Restatements in a Time of Turbulence, Leverage, and Rational Expectations

Using agency theory and transaction cost economics as a conceptual foundation, this study uses backward- and forward-looking measures, sales instability and market leverage, to examine likelihood of financial restatement. We test hypotheses using a sample of 43,876 firm-year observations from 2005-2018, involving 5,397 firms and 3,991 restatements. As hypothesized, higher levels of sales turbulence as well as higher levels of market leverage increase likelihood of restatement. Although firms evidencing sales increases over prior years have lower likelihood of restatement, examination of interaction reveals this effect is reduced when such increases occur in the context of increased sales turbulence or increased levels of leverage.

Организация дистанционного обучения студентов неязыковых направлений в условиях пандемии

The purpose of the study is to analyse features of organising distance learning for non-linguistic students in the context of the pandemic. The article examines advantages and disadvantages of distance learning in higher education institutions, presents results of a survey that has revealed students’ opinions on various aspects of online education during the ongoing pandemic. Scientific novelty of the study lies in the fact that the authors’ assessment of students’ distance learning organisation in the context of the pandemic reveals the necessary forward-looking measures for online education organisation in higher education institutions. As a result, the study has clarified the notion “distance learning” in connection with digital transformation of the higher education system, identified positive and negative aspects in distance learning organisation in higher education institutions during the pandemic, identified the measures necessary for effective organisation of distance learning.

Rational repricing of risk during COVID‐19: Evidence from Indian single stock options market

Could the COVID‐19 related market crash and subsequent rebound be explained as a rational response to evolving conditions? Our results using multiple forward‐looking measures of uncertainty implied from stock option prices suggest so. First, we find a gradual build‐up of volatility during the month preceding the spike at the start of the pandemic. Second, while tail risk declined after government interventions, the level of uncertainty remained elevated for stocks across industries. Third, the dynamics of decline in tail risk in stocks was industry‐dependent, suggesting that the market performed a fine‐grained analysis of each stock's uncertainty through the pandemic.

Predicting corporate policies using downside risk: A machine learning approach

This paper develops a text-based downside risk measure using corporate annual reports and assesses its ability to forecast future corporate policies. The forward-looking measure dynamically captures adverse firm conditions evolving from economic fundamentals. When the measure is below its sample average, leverage, investment, R&D, employment, and dividends consistently fall. When the measure rises, firms increase cash holdings. The proposed measure also delivers robust and persistent forecasts based on in-sample and out-of-sample LASSO regressions. • Develop a text-based downside risk measure using corporate annual reports. • Forward-looking measure captures adverse firm conditions evolving from economic fundamentals. • Measure significantly predicts leverage, investment, R&D, employment, dividends and cash holdings. • Stronger predictive power than that of stock return volatility and earnings volatility. • Robust and persistent predictability based on in-sample and out-of-sample LASSO regressions.

The impact of COVID-19 on tail risk: Evidence from Nifty index options

We investigate the impact of COVID-19 using multiple forward-looking measures of uncertainty in Indian stock markets using liquid Nifty index options. The WHO declaration of COVID-19 as a pandemic coincides with a sharp rise in all measures of uncertainty considered, including option-implied volatility smiles, risk-neutral density, skewness, and kurtosis. We find that while subsequent government-imposed lockdowns and monetary easing induced a near-normalization of skewness and kurtosis, the volatility level remained elevated, demonstrating the importance of higher moments in capturing uncertainty during a pandemic. Structural breaks identified using the Bai–Perron methodology closely track the dates of significant announcements or interventions.