DeepDrive: A braking decision making approach using optimized GAN and Deep CNN for advanced driver assistance systems

Reduction of the number of traffic accidents is a vital requirement in many countries over the world. In these circumstances, the Human–Robot Interaction (HRI) mechanisms utilization is currently exposed as a possible solution to recompense human limits. It is crucial to create a braking decision-making model in order to produce the optimal decisions possible because many braking decision-making approaches are launched with minimal performance. An effective braking decision-making system, named Optimized Deep Drive decision model is developed for making braking decisions. The video frames are extracted and the segmentation process is done using a Generative Adversarial Network (GAN). GAN is trained using the newly developed optimization technique known as the Autoregressive Anti Corona Virus Optimization (ARACVO) algorithm. ARACVO is created by combining the Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) and Anti Corona Virus Optimization (ACVO) models. After retrieving the useful information for processing, the Deep Convolutional Neural Network (Deep CNN) is next used to decide whether to apply the brakes. The proposed approach improved performance by achieving maximum values of 0.911, 0.906, 0.924, and 0.933 for segmentation accuracy, accuracy, sensitivity, and specificity. © 2023 Elsevier Ltd

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensified the spillover effects across international stock markets. Our results demonstrate that DE, UK, EU, and US acted as net transmitters of dynamic connectedness;however, Japan, China, India, and Hong Kong acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

Quantile prediction for Bitcoin returns using financial assets' realized measures

This paper explores which properties of financial asset prices drive Bitcoin's return distributions, using quantile regressions with lagged realized moment measures of various financial assets. The result shows that Bitcoin's lagged realized volatility predicts its return distributions very well, revealing Bitcoin's aspect as a risk asset. Moreover, its lagged realized kurtosis plays some role in prediction in recent periods. In contrast, other financial assets' realized measures have limited predictive power, which implies the relative uniqueness of Bitcoin's price movements. Finally, out-of-sample predictions using lasso quantile regressions confirm the robust predictive power of lagged Bitcoin variables even in the Covid-19 period. © 2023 Elsevier Inc.

The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies

In recent years, the cryptocurrency market has been experiencing extreme market stress due to unexpected extreme events such as the COVID-19 pandemic, the Russia and Ukraine war, monetary policy uncertainty, and a collapse in the speculative bubble of the cryptocurrencies market. These events cause cryptocurrencies to exhibit higher market risk. As a result, a risk model can lose its accuracy according to the rapid changes in risk levels. Value-at-risk (VaR) is a widely used risk measurement tool that can be applied to various types of assets. In this study, the efficacy of three value-at-risk (VaR) models—namely, Historical Simulation VaR, Delta Normal VaR, and Monte Carlo Simulation VaR—in predicting market stress in the cryptocurrency market was examined. The sample consisted of popular cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), and Ripple (XRP). Backtesting was performed using Kupiec's POF test, Kupiec's TUFF test, Independence test, and Christoffersen's Interval Forecast test. The results indicate that the Historical Simulation VaR model was the most appropriate model for the cryptocurrency market, as it demonstrated the lowest rejections. Conversely, the Delta Normal VaR and Monte Carlo Simulation VaR models consistently overestimated risk at confidence levels of 95% and 90%, respectively. Despite these results, both models were found to exhibit comparable robustness to the Historical Simulation VaR model. © 2023 by the authors.

Investor sentiment and the Chinese new energy stock market: A risk–return perspective

The new energy industry is crucial for solving the problem of pollution, and its development requires support from the stock market. This paper proposes a Chinese investor sentiment index based on the Long Short-Term Memory (LSTM) deep learning method, and investigates the effect of investor sentiment on new energy stock returns as well as value at risks (VaR) behavior before and during COVID-19. It also compares these effects on traditional energy companies to identify differences between the new energy and traditional companies. The empirical results show that investor sentiment has significant effects on stock returns and VaR of both new and traditional energy companies but the effects are stronger in the new energy industry. The effects of investor sentiment have increased during COVID-19, and investors pay more attention on risks than returns during COVID-19. These results provide guidance for small and medium-sized investors in China to optimize their investment strategies and alleviate losses associated with extreme risks. © 2022 Elsevier Inc.

Tail risk forecasting of realized volatility CAViaR models

This research proposes a new class of RES-CAViaR (conditional autoregressive value-at-risk) models, that incorporate daily realized volatility and expected shortfall (ES) to forecast VaR and ES simultaneously. We further consider weekly and monthly realized volatilities in the proposed model to approximate a long-memory process. We employ the Bayesian adaptive Markov chain Monte Carlo approach to estimate all unknown parameters and to jointly predict daily VaR and ES over a 4-year out-of-sample period including the COVID-19 pandemic. Our results show that the realized CAViaR-type models outperform in terms of three backtests, four loss-function criteria, and ES measurement at the 1% level. © 2022 Elsevier Inc.

Heterogeneity of inbound tourism driven by exchange rate fluctuations: implications for tourism business recovery and resilience in Australia

Quantifying risk spillovers from exchange rates to inbound tourist arrivals by purpose of visit is essential for Australia to take proactive measures to achieve tourism business recovery and resilience after such critical events like the recent bushfires and the COVID-19 pandemic. Using a monthly dataset over the period January 1998–March 2020, this paper calculates the conditional value-at-risk (CoVaR) to evaluate how different types of inbound tourists to Australia respond to exchange rate fluctuations. The empirical results identify inbound tourist arrivals with the highest sensitivity to exchange rate fluctuations, confirming the role of exchange rates in determining inbound tourist arrivals by purpose of visit. Furthermore, these results shed light on provisions of tourism products, services, and infrastructural facilities to satisfy different requirements of Australia's inbound tourists by purpose of visit, aiming to promote tourism business recovery and resilience in Australia.

The impact of the COVID-19 pandemic on bank systemic risk: some cross-country evidence

Purpose: This study tends to investigate how the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected banks' contribution to systemic risk. In addition, the authors examine whether the impact of the pandemic may vary across advanced/emerging economies, and with banks with differed characteristics. Design/methodology/approach: The authors construct the bank-specific conditional value at risk (CoVaR) and marginal expected shortfall (MES) to measure their contribution to systemic risk and define the outbreak of the COVID-19 pandemic by the timing when countries report more than 100 confirmed cases. The authors use the approach of difference-in-differences to assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. This sample comprises monthly panel data of around 900 listed commercial banks in 39 advanced and emerging economies. Findings: The authors find that, firstly, the COVID-19 pandemic increased banks' contribution to systemic risk significantly around the world. Secondly, the impact of the COVID-19 virus was more pronounced in developed countries than in emerging economies. Finally, banks with a larger size and higher loan-to-deposit ratio are more greatly affected by the COVID-19 pandemic, while a higher capitalization for banks is insufficient to shelter them from the adverse impact of such pandemic. Originality/value: The authors assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. Using the conditional value at risk (marginal expected shortfall) of banks as the measure, this study's results suggest that banks' contribution to systemic risk increases by around 25% (48%) amid the COVID-19 pandemic. This study's findings may shed some light on the potential policies that financial regulators may employ to ameliorate the adverse outcomes of the ongoing pandemic. © 2022, Emerald Publishing Limited.

Determining the investors' strategy during the COVID-19 crisis based on the S&P 500 stock index1

Background: The most significant changes caused by the COVID-19 crisis were the sharp increase in working from home and the growing importance of e-commerce, which affected the development of some industries. This change also affects the investors' investment operations, which are based on analysis to ensure an unquestionable certainty of the invested financial amount and a satisfactory return. It is, therefore, interesting to analyze the possible return of the chosen investment strategy based on the optimization model of portfolio selection based on the CVaR risk measure. Purpose: The paper aims to present the possible use of the analysis of returns of effective portfolios constructed based on the optimization model of portfolio selection based on the CVaR risk measure during the crisis (COVID-19) and the pre-crisis period. Study design/methodology/approach: Paper presents the impact of the COVID-19 crisis on investor decision-making through the CVaR risk measure, which was implemented on the historical data of the components of the Standard and Poor's 500 stock index (S&P 500) in the crisis period as well as in the pre crisis period. Findings/conclusions: The presented approach based on the CVaR risk rate measure and the relevant portfolio selection model provides the investor with an effective tool for allocating funds to the financial market in particular segments in both monitored periods. Limitations/future research: Time series data are divided into two periods based on visible factors such as the number of COVID-19 cases. In future research, we aim to divide monitored periods based on unobservable factors influencing investors' decisions, such as bull or bear mood on the market.

BSE Greenex: A Pre & Post Covid-19 Empirical Study

BSE GREENEX is one of its kind indices that assesses the listed stocks on their "carbon performance"to quantify the energy efficiency of those listed stocks based on publicly available data. Past studies have analyzed the performance of listed stock of the index but not the performance of index itself. The present study analyzes the BSE GREENEX performance. The performance has also been analyzed for pre and post covid era. The result suggests that there is consistency in return over the period of time, whereas post covid performance of index is better than that of pre covid. As post covid return outperform the pre covid return, the study concludes that including sustainable finance not only attract more profit but also brings stability to the financial market and economy as well. © 2022 The Authors. Published by Elsevier B.V.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Historical VaR method and Cornish-Fisher Approximation: Efficacy Against Unlikely Risks in Financial Engineering, The case of Covid-19 in Moroccan Financial Market

- Covid-19 hit the global economy, its impact on global supply chains and financial operations was clear, and it showed the importance of managing unlikely risks. To manage the impact of such risks, analytical tools are needed. These tools can provide decision-makers with ways to confront these risks[1]. This article assesses the impact of Covid-19, a concrete example of unlikely risks: it's a sanitary risk on the Moroccan stock market. This evaluation consists first of choosing optimal investments that minimize the risk of loss for expected returns, based on the Markowitz model, which was awarded the Nobel Prize in Economics in 1990. This choice was made at the beginning of the covid-19 pandemic in Morocco. Then estimate the maximum loss for these investments, which should not exceed using the Historical VaR and Cornish-Ficher VaR calculation methods. Finally, it compared real losses with estimated losses to highlight the need to consider unlikely risks during financial engineering and risk management. The two methods: Historical VaR and Cornish-Fisher VaR, are chosen because they don't impose the normality assumption on return distributions. The Cornish-Fisher VaR approximation is generally used for crisis management. Its novelty consists of testing these methods' efficiency against unlikely risks and precisely against the sanitary risk. Existing researches suggest risking managers use Cornish Fisher VaR in time of crisis. This work demonstrates that the Cornish Fisher VaR overestimates losses and that more research is needed to estimate them better using the Extreme value theory. © 2022 Seventh Sense Research Group®

Is Tether a safe haven of safe haven amid COVID-19? An assessment against Bitcoin and oil using improved measures of risk.

Bitcoin is a new speculative investment with extremely volatile movement, thus possibly failing to act as a safe haven for crude oil when the price of this energy commodity plummeted following the global outbreak of COVID-19. Meanwhile, Tether is designed to behave similarly to the US dollar with stable fluctuation. In this study, we assessed their safe-haven properties in terms of risk reduction opportunities by proposing an improved version of Value-at-Risk (VaR) and Expected Shortfall (ES). Using vine copula-based AR-GJR-GARCH models, we demonstrated that Bitcoin exhibited inconsistent risk reduction capability for oil, particularly before COVID-19. When adding Tether into a portfolio containing oil and Bitcoin, the risk reduction was achieved for any portfolio allocation and was more pronounced amid the COVID-19 period. This suggests that Tether consistently served strong support for Bitcoin to protect oil investors against extreme risk and received a significant impact from the COVID-19 outbreak. However, the consistent safe-haven functionality of Tether was not as good as that of the US dollar in most cases, and this implied the vanishing of its stability. These results were robust when considering another asymmetric volatility model and another dependence model. Furthermore, the proposed improved VaR and ES forecasts outperformed their corresponding unimproved version in quantifying portfolio risk and therefore provided a more accurate assessment of safe-haven roles.

The distribution of COVID-19 mortality.

We estimate the distribution of COVID-19 mortality (measured as daily deaths) from the start of the pandemic until July 31st, 2022, for six European countries and the USA. We use the Pareto, the stretched exponential, the log-normal and the log-logistic distributions as well as mixtures of the log-normal and log-logistic distributions. The main results are that the Pareto does not describe well the data and that mixture distributions tend to offer a very good fit to the data. We also compute Value-at-Risk measures as well as mortality probabilities with our estimates. We also discuss the implications of our results and findings from the point of view of public health planning and modelling.

Connectedness between crude oil, coal, rare earth, new energy and technology markets: a GARCH-vine-copula-EVT analysis

In recent years, climate change has attracted great attention from governments and promoted the booming of the new energy market indirectly. However, this market will be influenced by traditional energy, rare earth and technology markets. Hence, it is necessary to incorporate these markets into an analytical framework simultaneously and analyse their relationships. Based on the GARCH-vine-copula-EVT model considering extreme risks, we investigate the connectedness between crude oil, coal, rare earth, new energy, and technology markets. The results show that the technology market is most closely associated with the new energy market;the rare earth market reacts as an intermediary market between the new energy market and fossil fuel markets. When taking the rare earth market as the conditional market, the connectedness between the new energy and the other four markets weakens and even becomes negative. Besides, we find that the COVID-19 epidemic has increased the connectedness between these target markets. Finally, the backtesting results of value at risk and expected shortfall show that the GARCH-vine-copula-EVT model considering extreme risks can depict the risk dependence structure between these target markets well. Our study has important reference significance for market participants, risk managers and investors.

Anticipating extreme losses using score-driven shape filters

We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor's 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements.

Estimating the risk of SARS-CoV-2 deaths using a Markov switching-volatility model combined with heavy-tailed distributions for South Africa.

BACKGROUND: SARS-CoV-2 (Covid-19 virus) infection exposed the unpreparedness of African countries to health-related issues, South Africa included. Africa recorded more than 211 853 deaths as a consequence of Covid-19. When rare and deadly diseases require urgent hospitalisation strikes, governments and healthcare providers are usually caught unprepared, resulting in huge loss of lives. Usually, at the beginning of such pandemics, there is no rich data for health practitioners and academics to be able to forecast the number of patients or deaths related to the pandemic. This study aims to predict the number of deaths associated with Covid-19 infection. With the availability of the number of deaths on a daily basis, the results stemming from this study are important to inform and plan health policy. METHODS: This study uses the daily number of deaths due to Covid-19 infection. Exploratory data analysis reveals that the data exhibits non-normality, three structural breaks and volatility clustering characteristics. The Markov switching (MS)-generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions is fitted to the returns of the data. Using available daily reported Covid-19-related deaths up until 26 August 2021, we report 10-day ahead forecasts of deaths. All forecasts are compared to the actual observed values in the forecasting period. RESULTS: The Anderson-Darling Goodness of fit test confirms that the fitted models are adequate for the data. The Kupiec likelihood ratio test and the root mean square error (RMSE) were used to select the robust model at different risk levels. At 95% the MS(3)-GARCH(1,1) combined with Pearson's type IV distribution (PIVD) is the best model. This indicates that the proposed best-fitting model is reasonable and can be used for predicting the daily number of deaths due to Covid-19. CONCLUSION: The MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management.

Tail risk forecasting of realized volatility CAViaR models

This research proposes a new class of RES-CAViaR (conditional autoregressive value-at-risk) models, that incorporate daily realized volatility and expected shortfall (ES) to forecast VaR and ES simultaneously. We further consider weekly and monthly realized volatilities in the proposed model to approximate a long-memory process. We employ the Bayesian adaptive Markov chain Monte Carlo approach to estimate all unknown parameters and to jointly predict daily VaR and ES over a 4-year out-of-sample period including the COVID-19 pandemic. Our results show that the realized CAViaR-type models outperform in terms of three backtests, four loss-function criteria, and ES measurement at the 1% level.

Oil tail-risk forecasts: from financial crisis to COVID-19

The coronavirus outbreak has caused unprecedented volatility in oil prices. This paper extends previous studies on oil Value-at-Risk (VaR) by providing extra insights into Expected Shortfall (ES) forecasting over the last decade, including several oil crises. We introduce a conditional volatility model combined with the Cornish-Fisher expansion for ES forecasting. In comparison to the widely used volatility models and innovation distributions, this approach is superior for predicting the ES of long positions but overestimates VaR for short positions. Overall, the volatility model addressing leverage effects with skewed t innovation produces the most accurate joint VaR and ES forecasting. Moreover, the magnitude of ES relative to VaR varies across models and time, implying that ES should be used in conjunction with VaR to inform timely risk management decisions. The results would be of interest to the regulatory authorities, energy companies, and financial institutions for oil tail-risk forecasting.

Dynamic correlations and portfolio implications across stock and commodity markets before and during the COVID-19 era: A key role of gold.

Novel Coronavirus (COVID-19) has affected stock markets around the globe, adding serious challenges to asset allocations and hedging strategies. This investigation analyses the dynamic correlations and portfolio implications among the S&P 500 index and various commodities (gold, WTI crude oil, Brent oil, beverages, and wheat) before and during the COVID-19 era. Using multivariate asymmetric GARCH models, the results show weak correlations during the standard period. However, the correlations intensify and become more complicated during the COVID-19 era, especially between gold and S&P 500. Similarly, bidirectional return and volatility spillovers across stock-commodity markets are more pronounced during the COVID-19 outbreak. Analysis involving the optimal portfolio weights and time-varying hedge ratios indicates that a $1long position in the S&P 500 can be hedged for 15 cents in crude oil during the standard period and for 33 cents in gold during the COVID-19 era. A portfolio of S&P 500 - beverages displays the highest VaR, while a portfolio of S&P 500 - gold displays the lowest VaR, especially during the COVID-19 era. This finding suggests that gold offers better portfolio diversification benefits and downside risk reductions, which are useful in determining strategies for portfolio investors during the COVID-19 outbreak.