Search Results (46)

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

This paper develops a framework for quantifying risk by integrating analytical derivations of Value at Risk (VaR) and Conditional VaR (CVaR) under the chi-squared distribution with empirical modeling via Generalized Autoregressive Conditional Heteroskedasticity (GARCH) processes. We first establish closed-form expressions for VaR and CVaR under the chi-squared distribution, leveraging properties of the inverse regularized gamma function and its connection to the quantile of the distribution. We evaluate the proposed framework across multiple time windows to assess its stability and sensitivity to market regimes. Empirical results demonstrate the chi-squared-based VaR and CVaR, when coupled with GARCH volatility forecasts, particularly during periods of heightened market volatility. Full article

The article analyzes the Granger-based causal relationship between two major crude oil markets, namely Brent and West Texas Intermediate (WTI), by using the standard vector autoregression (VAR) framework. In this regard, the effects of the COVID-19 pandemic and the Russia–Ukraine war on causality between Brent and WTI are examined. The empirical results from Granger-causality tests show (a) strong causality from Brent to WTI during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (b) no causality from WTI to Brent during the period prior to the COVID-19 pandemic and Russia–Ukraine war, (c) no causality from Brent to WTI during the COVID-19 pandemic, (d) evidence of causality from WTI to Brent during the COVID-19 pandemic, and (e) no evidence of causality from both markets during the period of Russia–Ukraine war. In addition, causality tests in quantiles support results from the linear Granger causality tests in general. However, contrary to the standard linear causality test, the quantile-in-regression causality test shows that Brent returns cause WTI returns during the pandemic period and WTI returns cause Brent returns before the pandemic. Furthermore, the results from the time-varying Granger causality tests support all conclusions from the standard linear (and static) Granger causality test, except the hypothesis that Brent causes WTI during the pandemic. Moreover, the time-varying Granger tests show evidence that causality between Brent and WTI clearly varies across the pandemic and war periods. Revealing the causalities between Brent and WTI across periods of economic and political stability, pandemic, and war would help policymakers develop appropriate energy policy and help investors determine appropriate risk management actions. Full article

This study employs Markov-Switching Regression (MS-Regression) to model four macroeconomic indicators—US GDP, CPI, interest rate, and unemployment rate—to identify economic crisis cycles, while all indicators provide some level of insight into these cycles, the unemployment rate offers the closest alignment with the actual patterns of economic cycles. Based on the regime identification derived from the unemployment rate, we delineate the time series for expansion and recession periods. Subsequently, we apply the Quantile Vector Autoregression (Quantile-VAR) model to analyze three sets of time series: the entire dataset, the expansion period, and the recession period. Our findings reveal that, under normal conditions, the US dollar exerts the greatest influence on and is most influenced by other currencies, whereas the Australian dollar has the least impact on others. In the extreme lower and upper tails, the mutual influence among the currencies of different countries intensifies, concurrently diminishing the relative influence of the US dollar. Notably, the spillover effects under extreme lower and upper tail conditions are not consistent, as the occurrence of extreme values does not coincide, suggesting an asymmetry in the spillover effects at these quantiles. Full article

Understanding the factors influencing individual tree mortality is essential for sustainable forest management, particularly for Prince Rupprech’s larch (Larix gmelinii var. Principis-rupprechtii) in North China’s natural forests. This study focused on 20 sample plots (20 × 20 m each) established in Shanxi Province, North China. This study compared three individual tree mortality models—Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Bayesian Generalized Linear Model (Bayesian GLM)—finding that both GLM and Bayesian GLM achieved approximately 0.87 validation accuracy on the test dataset. Due to its simplicity, GLM was selected as the final model. Building on the GLM model, six binning methods were applied to categorize diameter at breast height (DBH): equal frequency binning, equal width binning, cluster-based binning, quantile binning, Chi-square binning, and decision tree binning. Among these, the decision tree binning method achieved the highest performance, with an accuracy of 90.12% and an F1 score of 90.06%, indicating its effectiveness in capturing size-dependent mortality patterns. This approach provides valuable insights into factors affecting mortality and offers practical guidance for managing Larix gmelinii var. Principis-rupprechtii forests in temperate regions. Full article

In this study, we examine the critical question of whether global equity and bond assets (both green and non-green) offer effective hedging and safe haven properties against stock market risks in South Asia, with a focus on Bangladesh, India, Pakistan, and Sri Lanka. The increasing integration of global financial markets and the volatility experienced during recent economic crises raise important questions regarding the resilience of South Asian markets and the potential protective role of global assets. Drawing on methods like VaR and CVaR tail risk estimators, the DCC-GJR-GARCH time-varying connectedness approach, and cost-effectiveness tools for hedging, we analyze data spanning from 2014 to 2022 to assess these relationships comprehensively. Our findings demonstrate that stock markets in Bangladesh experience lower levels of downside risk in each quantile; however, safe haven properties from the global financial markets are effective for Bangladeshi, Indian, and Pakistani stock markets during the crisis period. Meanwhile, the Sri Lankan stock market neither receives hedging usefulness nor safe haven benefits from the same marketplaces. Additionally, global green assets, specifically green bond assets, are more reliable sources to ensure the safest investment for South Asian investors. Finally, the portfolio implications suggest that while traditional global equity assets offer ideal portfolio weights for South Asian investors, global equity and bond assets (both green and non-green) are the cheapest hedgers for equity investors, particularly in the Bangladeshi, Pakistani, and Sri Lankan stock markets. Moreover, these results hold significant implications for investors seeking to optimize portfolios and manage risk, as well as for policymakers aiming to strengthen regional market resilience. By clarifying the protective capacities of global assets, particularly green ones, our study contributes to a nuanced understanding of portfolio diversification and financial stability strategies within emerging markets in South Asia. Full article

Fluctuations in oil prices substantially impact both the real economy and international financial markets. Despite extensive studies on oil market dynamics and overnight momentum, a comprehensive understanding of the link between oil price changes and energy market momentum, as well as their broader influence on global financial markets, remains elusive. This study delves into the intricate mechanics of overnight momentum transmission within financial markets, focusing on its origin in oil price fluctuations and its overarching impact on market dynamics. Employing the quantile VAR method, we analyze daily market data from 3 January 2014 to 17 January 2024. This study emphasizes the significance of overnight momentum on the transmission of volatility, particularly in the tails of the distribution, and highlights the necessity for efficient strategies to govern financial stability. The shale oil revolution, COVID-19, the Russia–Ukraine war, and the Israel–Hamas conflict have significantly impacted the interconnectivity of financial markets on a global scale. It is crucial for policymakers to give priority to the monitoring of the energy market to reduce risks and improve the resilience of the system. Full article

Recognizing the importance of incorporating different risk measures in the portfolio management model, this paper examines the dynamic mean-risk portfolio optimization problem using both variance and value at risk (VaR) as risk measures. By employing the martingale approach and integrating the quantile optimization technique, we provide a solution framework for this problem. We demonstrate that, under a general market setting, the optimal terminal wealth may exhibit different patterns. When the market parameters are deterministic, we derive the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the benefits of our dynamic portfolio model compared to its static counterpart. Full article

This paper deals with the problem of financial risk management using a new expected shortfall regression. The latter is based on the expectile model for financial risk-threshold. Unlike the VaR model, the expectile threshold is constructed by an asymmetric least square loss function. We construct an estimator of this new model using the k-nearest neighbors (kNN) smoothing approach. The mathematical properties of the constructed estimator are stated through the establishment of the pointwise complete convergence. Additionally, we prove that the constructed estimator is uniformly consistent over the nearest neighbors (UCNN). Such asymptotic results constitute a good mathematical support of the proposed financial risk process. Thus, we examine the easy implantation of this process through an artificial and real data. Our empirical analysis confirms the superiority of the kNN-approach over the kernel method as well as the superiority of the expectile over the quantile in financial risk analysis. Full article

This paper investigates the estimation of the value at risk (VaR) across various probability levels for the log-returns of a comprehensive dataset comprising four thousand crypto-assets. Employing four recently introduced adaptive conformal inference (ACI) algorithms, we aim to provide robust uncertainty estimates crucial for effective risk management in financial markets. We contrast the performance of these ACI algorithms with that of traditional benchmark models, including GARCH models and daily range models. Despite the substantial volatility observed in the majority of crypto-assets, our findings indicate that ACI algorithms exhibit notable efficacy. In contrast, daily range models, and to a lesser extent, GARCH models, encounter challenges related to numerical convergence issues and structural breaks. Among the ACI algorithms, Fully Adaptive Conformal Inference (FACI) and Scale-Free Online Gradient Descent (SF-OGD) stand out for their ability to provide precise VaR estimates across all quantiles examined. Conversely, Aggregated Adaptive Conformal Inference (AgACI) and Strongly Adaptive Online Conformal Prediction (SAOCP) demonstrate proficiency in estimating VaR for extreme quantiles but tend to be overly conservative for higher probability levels. These conclusions withstand robustness checks encompassing the market capitalization of crypto-assets, time-series size, and different forecasting methods for asset log-returns. This study underscores the promise of ACI algorithms in enhancing risk assessment practices in the context of volatile and dynamic crypto-asset markets. Full article

The expectile-based Value at Risk (EVaR) has gained popularity as it is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). This paper applies the expectile regression approach to evaluate the EVaR of stock market indices of Australia and Japan. We use an expectile regression model that considers lagged returns and common risk factors to calculate the EVaR for each stock market and to evaluate the interdependence of downside risk between the two markets. Our findings suggest that both Australian and Japanese stock markets are affected by their past development and the international stock markets. Additionally, ASX 200 index has significant impact on Nikkei 225 in terms of downside tail risk, while the impact of Nikkei 225 on ASX is not significant. Full article

The Russia–Ukrainian war, which began in 2014 and exploded with the invasion of the Russian army on 24 February 2022, has profoundly destabilized the political, economic and financial balance of Europe and beyond. To the humanitarian emergency associated with every war has been added the deep crisis generated by the strong energy and food dependence that many European countries, and not only European, have developed over decades on Ukraine (especially for wheat) and Russia (especially for natural gas). The aim of this article is to verify the existence of a link between the performance of the Eurostoxx index and the price of wheat futures and TTF natural gas, from 25 February 2019 to 28 September 2023. Through a quantile VAR analysis, a link is sought between the Eurostoxx 50 index, and wheat and TTF gas futures prices. Furthermore, the analysis intends to understand whether the presence of such relationship only manifested itself following the war events, or whether it was already present in the market. The analysis carried out also shows that the relationship between the stock market and raw material prices was present even before the conflict. Full article

This study aims to provide important insights regarding the integrated structure of global factors and Pakistan’s leading sector-level indices by estimating the dynamic network and pairwise connectedness of the global crude oil index, MSCI index, European economic policy uncertainty index, and important sector-level indices of Pakistan based on QVAR using daily frequency over the period of 20 years from 2002 to 2022. The findings demonstrate high interconnectedness among global factors indices and Pakistan’s leading sector-level indices. The results of net directional connectivity showed that the EPEUI, WTI, and MSCI indices are the “net receivers” of volatility spillover. At the same time, the financial and energy sectors are the “net transmitter” of shocks. Connectedness is high amid financial upheavals. The research findings provide crucial insights for policymakers, businesses, portfolio managers, and investors. Full article

The banking crisis experienced at the beginning of 2023 in the aftermath of the global 2008 crisis served as a stark reminder of the importance of systemic risk once again across the world. This study examines the dynamics of systemic risk in the Turkish banking system and its impact on sustainable economic growth between the period of 2007 and 2022. Through the Component Expected Shortfall (CES) method and quantile spillover analysis, private banks, such as Garanti Bank (GARAN), Akbank (AKBNK), İş Bank (ISCTR), and Yapı ve Kredi Bank (YKBNK), are identified as major sources of systemic risk. The analysis reveals a high level of interconnectedness among the banks during market downturns, with TSKB, Vakıfbank (VAKBNK), İş Bank (ISCTR), Halk Bank (HALKB), Akbank (AKBNK), Yapı ve Kredi Bank (YKBNK), and Garanti Bank (GARAN) serving as net risk transmitters, while QNB Finansbank (QNBFB), ICBC Turkey Bank (ICBCT), Şekerbank (SKBNK), GSD Holding (GSD), and Albaraka Türk (ALBRK) act as net risk receivers. Employing the Markov switching VAR (MS-VAR) model, the study finds that increased systemic risk significantly reduces economic growth during heightened financial periods. These findings underscore the importance of monitoring systemic risks and implementing proactive measures in the banking sector. The policy implications highlight the requirement for regulators and policymakers to prioritize systemic risk management. Close monitoring helps detect weaknesses and imbalances that could put financial stability at risk. Timely implementation of policies and rules is crucial in the prevention of the accumulation of systemic risks and in dealing with the existing hazards. Such measures protect the stability of the banking sector and mitigate potential negative effects on the broader economy. Full article