Search Results (15)

This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be $\alpha =1.007$, implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolio’s diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure. Full article

Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution. The newly proposed model is of generality, simplicity, robustness, and financial interpretability. Most importantly, one can detect tail risk signals by calibrating the proposed model. The model includes the seminal Black–Scholes (B−S) formula as a limit case and can perfectly “replicate” the option prices from Merton’s jump-diffusion model. Based on the proposed pricing model, three tail risk warning measures are introduced for tail risk signals detection: the EGB2 implied tail index, the EGB2 implied Value-at-Risk (EGB2-VaR), and the EGB2 implied risk-neutral density (EGB2 R.N.D.). Empirical results show that the new pricing model can yield higher pricing accuracy than existing models in normal and crisis periods, and three model-based tail risk measures can perfectly detect tail risk signals before financial crises. Full article

This paper examines the effects of the Standard and Poor’s 500 (SP500) stock index crash during the global financial crisis and the COVID-19 pandemic periods on the South African top sector indices (basic materials, consumer goods, consumer services, financials, healthcare, industrials, technology, and telecommunication). The results of a copula-based BEKK-GARCH approach technique demonstrate the existence of price and volatility spillover during times of stock crashes. We discover that during a stock crisis, strong shocks and higher volatility spillover effects from the United States (U.S.) SP500 index to the top sector indices of the South African Johannesburg Stock Exchange (JSE) markets are more significant. However, there is no integrated economy, as the results did not show any spillover effects from South Africa to U.S. markets. Furthermore, the Gumbel copulas have higher dependence parameters, implying that extreme co-movements occur in the upper tails, suggesting the possibility of a large transmission of shocks from the SP500 to the eight top sector indices of the JSE and showing an asymmetric dependence between these markets. This result is important for investors willing to invest in the South African sector of equity markets to develop hedging strategies to prevent risk spillover from developed markets. Full article

In this paper, the generalised extreme value distribution (GEVD) model is employed to estimate financial risk in the form of return levels and the value at risk (VaR) for the two exchange rates, BitCoin/US dollar (BTC/USD) and the South African rand/US dollar (ZAR/USD). The Basel Committee on Banking Supervision (BCBS) responsible for developing supervisory guidelines for banks and financial trading desks recommended that VaR be computed and reported. The maximum likelihood estimation (MLE) method is used to estimate the parameters of the GEVD. The estimated risk values are used to compare the riskiness of the two exchange rates and help both traders and investors to define their position in forex trading. This is to helping understanding the risk they are taking when they convert their savings/investments to BitCoin instead of the South African currency, the rand. The high extreme value index associated with the BTC/USD compared to the ZAR/USD implies that BitCoin is riskier than the rand. The BTC/USD has higher values of expected extreme/tail losses of 13.44%, 18.02%, and 23.41% at short (6 months), medium (12 months), and long (24 months) terms, compared to the ZAR/USD expected extreme/tail losses of 2.40%, 2.84%, and 3.28%, respectively. The computed VaR estimates for losses of USD 0.17, USD 0.22, and USD 0.38 per dollar invested in BTC/USD at 90%, 95%, and 99%, compared to ZAR/USD’s USD 0.03, USD 0.03, and USD 0.04 at the respective confidence levels, confirm the high risk associated with BitCoin. The conclusion drawn from this study is that BTC/USD is riskier than ZAR/USD, despite the rand being a developing country’s currency, hence perceived as being risky. The perception is that the rand is riskier than BitCoin and perceptions do influence exchange rates. Kupiec’s backtest results confirmed the model’s adequacy. These findings are helpful to investors, traders, and risk managers when deciding on trading positions for the two currencies. Full article

Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important; however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS-LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high-frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers. Full article

Tail risk is an important financial issue today, but directly hedging tail risks with an ad hoc option is still an unresolved problem since it is not easy to specify a suitable and asymmetric pricing kernel. By defining two ad hoc underlying “assets”, this paper designs two novel tail risk options (TROs) for hedging and evaluating short-term tail risks. Under the Fréchet distribution assumption for maximum losses, the closed-form TRO pricing formulas are obtained. Simulation examples demonstrate the accuracy of the pricing formulas. Furthermore, they show that, no matter whether at scale level (symmetric “normal” risk, with greater volatility) or shape level (asymmetric tail risk, with a smaller value in tail index), the greater the risk, the more expensive the TRO calls, and the cheaper the TRO puts. Using calibration, one can obtain the TRO-implied volatility and the TRO-implied tail index. The former is analogous to the Black-Scholes implied volatility, which can measure the overall symmetric market volatility. The latter measures the asymmetry in underlying losses, mirrors market sentiment, and provides financial crisis warnings. Regarding the newly proposed TRO and its implied tail index, economic implications can be offered to investors, portfolio managers, and policy-makers. Full article

Over the course of history, the development of human societies implied the exploitation of mineral resources which generated huge amounts of mining wastes leading to substantial environmental contamination by various metal(loid)s. This is especially the case of coal mine tailings which, subjected to weathering reactions, produce acid mine drainage (AMD), a recurring ecological issue related to current and past mining activities. In this study, we aimed to determine the origin, the fate and the ecotoxicity of metal(loid)s leached from a historical coal tailing heap to the Beuveroux river (Franche-Comté, France) using a combination of mineralogical, chemical and biological approaches. In the constitutive materials of the tailings, we identified galena, tetrahedrite and bournonite as metal-rich minerals and their weathering has led to massive contamination of the water and suspended particles of the river bordering the heap. The ecotoxicity of the AMD has been assessed using Chironomus riparius larvae encaged in the field during a one-month biomonitoring campaign. The larvae showed lethal and sub-lethal (growth and emergence inhibition and delay) impairments at the AMD tributary and near downstream stations. Metal bioaccumulation and subcellular fractionation in the larvae tissues revealed a strong bioavailability of, notably, As, Pb and Tl explaining the observed biological responses. Thus, more than 70 years after the end of mining operations, the coal tailings remain a chronic source of contamination and environmental risks in AMD effluent receiving waters. Full article

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible. Full article

This paper explores whether the high or low ESG rating of a company is related to the level of its implied tail risk, measured on the basis of derivative data by implied skewness and implied kurtosis. Previous research suggests that the ESG rating of a company is indeed connected to some financial risk; however, often, only volatility is used as a risk measure. We examined the relation between ESG ratings and implied volatility, and explore the relation between ESG ratings and financial risk in more depth by looking into higher implied moments accessing financial tail risk. First, we found that higher ESG rated companies have a lower implied volatility connected with them, and exhibit more negative implied skewness and higher implied kurtosis. In other words, we observed a higher negative tail risk for higher ESG rated companies. However, on a midsized company data set, we found that higher ESG rated companies both have lower implied volatility, and exhibit less negative implied skewness and lower implied kurtosis. Hence, negative tail risk is typically lower for high ESG rated companies. Our study further investigated similar effects on individual environmental (E), social (S) and governance (G) scores of the involved companies. Second, we examined whether such a kind of trend exists for different sectors. Our results indicate that the influence of ESG ratings on implied volatility exhibits a similar trend, except for the industrial, information services, and real estate sectors, while for the materials, healthcare, and communication services sectors, the influence of ESG ratings on implied skewness and implied kurtosis is less pronounced. Moreover, the results show that the ESG ratings are correlated with implied moments for companies in consumer discretionary sectors. Full article

The present study aims at modelling market risk for four commodities, namely West Texas Intermediate (WTI) crude oil, natural gas, gold and corn for the period 2007–2017. To this purpose, we use Extreme Value Theory (EVT) together with a set of Conditional Auto-Regressive Logit (CARL) models to predict risk measures for the futures return series of the considered commodities. In particular, the Peaks-Over-Threshold (POT) method has been combined with the Indicator and Absolute Value CARL models in order to predict the probability of tail events and the Value-at-Risk and the Expected Shortfall risk measures for the selected commodities. Backtesting procedures indicate that generally CARL models augmented with specific implied volatility outperform the benchmark model and thus they represent a valuable tool to anticipate and manage risks in the markets. Full article

These days we are witnessing a deep change in the characteristics of the type of energy that our economies are supplied with. A clear trend is that sustainable and green energies are decisively replacing traditional fossil fuel-based sources of energy. For various reasons, this fundamental change implies an increasing risk in investments on portfolios heavily based on traditional energy industries. What is less known, is that these industries have returns that show a very low correlation with sustainable fossil fuel-free stock portfolios making them an appealing tool for portfolio managers to design properly diversified investments. In this study we examine this and related phenomena proposing statistical methods to implement the expected shortfall (ES), the challenging risk measure recently adopted by the financial regulator. We obtain evidence that a newly proposed backtesting procedure for the ES based on multinomial tests is an adequate and simple method to validate these risk measures when applied to a highly volatile stock index. Backtesting results of the ES show that flexible heavy-tailed distribution α–stable performs well for modelling the loss distribution. These results are even improved when the variances of fossil fuel price returns are included as external regressors in the GARCH model variance equation. In this case, the ES computed from the four considered loss distributions perform properly. Full article

The study evaluates the health risk caused by heavy metals to the inhabitants of a gold mining area. In this study, 56 soil samples from five mine tailings and 17 from two mine villages were collected and analyzed for Asernic (As), Lead (Pb), Mercury (Hg), Cadmium (Cd), Chromium (Cr), Cobalt (Co), Nickel (Ni), Copper (Cu) and Zinc (Zn) using ICP-MS. Measured concentrations of these heavy metals were then used to calculate the health risk for adults and children. Their concentrations were such that Cr > Ni > As > Zn > Cu > Co > Pb > Hg > Cd, with As, Cr and Ni higher than permissible levels. For the adult population, the Hazard Index value for all pathways was found to be 2.13, making non-carcinogenic effects significant to the adult population. For children, the Hazard Index value was 43.80, a value >>1, which poses serious non-carcinogenic effect to children living in the gold mining area. The carcinogenic risk was found to be 1.7 × 10⁻⁴ implying that 1 person in every 5882 adults may be affected. In addition, for children, in every 2725 individuals, 1 child may be affected (3.67 × 10⁻⁴). These carcinogenic risk values were both higher than acceptable values. Full article

The assessment of collective risk for flood risk management requires a better understanding of the space-time characteristics of flood magnitude and occurrence. In particular, classic formulation of collective risk implies hypotheses concerning the independence of intensity and number of events over fixed time windows that are unlikely to be tenable in real-world hydroclimatic processes exhibiting persistence. In this study, we investigate the links between the serial correlation properties of 473 daily stream flow time series across the major river basins in Europe, and the characteristics of over-threshold events which are used as proxies for the estimation of collective risk. The aim is to understand if some key features of the daily stream flow data can be used to infer properties of extreme events making a more efficient and effective use of the available data. Using benchmark theoretical processes such as Hurst-Kolmogorov (HK), generalized HK (gHK), autoregressive fractionally integrated moving average (ARFIMA) models, and Fourier surrogate data preserving second order linear moments, our findings confirm and expand some results previously reported in the literature, namely: (1) the interplay between short range dependence (SRD) and long range dependence (LRD) can explain the majority of the serial dependence structure of deseasonalized data, but losing information on nonlinear dynamics; (2) the standardized return intervals between over-threshold values exhibit a sub-exponential Weibull-like distribution, implying a higher frequency of return intervals longer than expected under independence, and expected return intervals depending on the previous return intervals; this results in a tendency to observe short (long) inter-arrival times after short (long) inter-arrival times; (3) as the average intensity and the number of events over one-year time windows are not independent, years with larger events are also the more active in terms of number of events; and (4) persistence influences the distribution of the collective risk producing a spike of probability at zero, which describes the probability of years with no events, and a heavier upper tail, suggesting a probability of more extreme annual losses higher than expected under independence. These results provide new insights into the clustering of stream flow extremes, paving the way for more reliable simulation procedures of flood event sets to be used in flood risk management strategies. Full article

This paper draws on network theory to investigate European banks’ sovereign debt exposures. Banks’ holdings of sovereign debt build a network of financial linkages with European countries that exhibits a long-tail distribution of node degrees. A highly connected network core of 15 banks is identified. These banks accounted for the majority of sovereign debt investments between December 2010 and December 2013 but exhibited only average and sometimes even below average capitalizations. Consequently, they constituted a potential source and transmission channel of systemic risk, especially due to their proneness to portfolio contagion. In a complementary regression analysis, the effect of counterparty risk on Credit Default Swap (CDS) spreads of 15 EU sovereigns is investigated. Among the banks exposed to the debt of a particular issuer, the biggest institutions in terms of their own asset sizes are identified and some of their balance sheet characteristics included into the regression. The analysis finds that the banks’ implied volatilities had a significant and increasing effect on CDS spreads during the recent crisis years, providing evidence of the presence of counterparty risk and its effect on EU sovereign debt pricing. Furthermore, the role of the domestic financial sectors is assessed and found to have affected the CDS spreads. Full article

This paper investigates the dynamic evolution of tail risk interdependence among U.S. banks, financial services and insurance sectors. Life and non-life insurers have been considered separately to account for their different characteristics. The tail risk interdependence measurement framework relies on the multivariate Student-t Markov switching (MS) model and the multiple-conditional value-at-risk (CoVaR) (conditional expected shortfall (CoES)) risk measures introduced in Bernardi et al. (2013), accounting for both the stylized facts of financial data and the contemporaneous multiple joint distress events. The Shapley value methodology is then applied to compose the puzzle of individual risk attributions, providing a synthetic measure of tail interdependence. Our empirical investigation finds that banks appear to contribute more to the tail risk evolution of all of the remaining sectors, followed by the financial services and the insurance sectors, showing that the insurance sector significantly contributes as well to the overall risk. We also find that the role of each sector in contributing to other sectors’ distress evolves over time according to the current predominant financial condition, implying different interdependence strength. Full article