Search Results (93)

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving significant levels of explained variance. The results obtained show that enhancing a reduced-form model by integrating it with neural networks is possible and practical for multivariate forecasting of future default frequencies. In our analysis, we utilize the recently proposed RecessionRisk⁺, a reduced-form latent-factor model developed for default and recession risk management applications as an improvement of the well-known CreditRisk⁺ model. The model has been empirically verified to exhibit some predictive power concerning future default rates. However, the theoretical framework underlying the model does not provide the elements necessary to define a proper estimator for forecasting the target default rates, leaving space for the application of a neural network framework to retrieve the latent information useful for default rate forecasting purposes. Among the neural network models tested in combination with RecessionRisk⁺, the best results are obtained with shallow LSTM networks. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets. Full article

Institutional investors increasingly value alternative assets in strategic asset allocation, with private debt emerging as a key asset class. However, its shortage of market history has hindered the development of standardized proxy benchmarks. For that, many institutional investors still do not recognize or manage private debt as a distinct asset class. Thus, this study aims to develop an optimized benchmark that reflects the unique characteristics of private debt, thereby contributing to establishing private debt as an independent investment asset class for strategic asset allocation among institutional investors. This study seeks to address this gap by constructing a proxy benchmark for the Preqin private debt index, which, despite its comprehensive market coverage, has a three-month reporting delay. This study employs quarterly performance data for private debt indices, spanning 31 December 2006 to 31 March 2023, and is sourced from Bloomberg and the index providers’ websites. Using regression analyses with timely asset-based indexes, the research develops a multivariate model that integrates multiple indexes, demonstrating superior tracking performance compared to existing methods. The findings provide a practical framework for improving the recognition, management, and allocation of private debt in institutional portfolios, addressing the need for reliable and timely performance metrics in this growing asset class. Full article

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability. Full article

Background/Objectives: Evidence suggests that food insecurity (FI) is a risk factor for eating disorder (ED) symptoms, especially binge eating (BE), yet research focusing on the psychosocial effects among midlife/older women is lacking. Midlife/older women living with FI experience intersectional disadvantage, thus highlighting the need for an independent investigation of the cultural and contextual factors of this population. The current study examined the difference in psychological health and quality of life (QOL) among women living with BE and FI (BE + FI) versus FI without BE. Method: Female clients of a food bank, aged 50+ (N = 295; M age = 62.1 years, SD = 8.2) living with FI completed measures of BE and psychosocial comorbidities. The measures were provided in English and Spanish. Results: A multivariate analysis of covariance compared women living with BE and FI (BE + FI) versus FI without BE on outcomes related to mental health and wellbeing. Covarying for age, FI severity, and ethnicity, the results indicated that women living with BE + FI reported worsened anxiety, depression, ED-related psychosocial impairment, internalized weight stigma, and QOL versus women living with FI without BE (all ps < 0.001). Effect sizes ranged from small to medium to large. Conclusions: Midlife/older women living with BE + FI report poorer psychological health and QOL than those living with FI without BE, demonstrating a critical need for mental healthcare in this population. Innovative solutions—and likely a portfolio of interventional approaches with various entry points and delivery modalities—are warranted, if we are to make significant strides in addressing ED symptoms in this population. Full article

This study investigates the integration of the Indonesian stock market with eight major trading partner countries, namely, China, Japan, the United States, Malaysia, India, Singapore, the Philippines, and South Korea. The analysis of the stock-market integration investigation includes the following two main things: short-term and long-term dynamic relationships within the Vector Autoregressive (VAR) model framework based on the unit root test, multivariate Johansen cointegration, and paired Granger causality test. The VAR model was analyzed using weekly closing index data of the Indonesian stock exchange and eight major trading partners from January 2013 to June 2024. The results of the study show that the integration of the Indonesian stock market with those of its main trading partners in the long term is relatively low. This finding implies that investors from the eight major trading partner countries can diversify their portfolios in international investments via the Indonesian stock market and vice versa. In the short term, these results prove that Indonesia’s stock markets and those of its major trading partners are integrated, excluding China. The Chinese stock market has become segmented and more attractive for Indonesian investors who want to benefit from diversification and vice versa. Furthermore, the Indonesian stock market has two-way causal relationships with the US, Japanese, Indian, and Singaporean stock markets. In addition, the Indonesian stock market has unidirectional reciprocal-lagged relationships with Malaysia and the Philippines. An essential contribution of this study is helping policymakers and, especially, international investors understand the dynamic relationships of the Indonesian stock market with its major trading partners. Furthermore, this study contributes to the development of empirical literature on the comovement of the Indonesian stock market and those of its major trading partners, as well as the stock markets of developing and developed countries. Full article

We analyze the predictive effect of monthly global, regional, and country-level financial uncertainties on daily gold market volatility using univariate and multivariate GARCH-MIDAS models, with the latter characterized by variable selection. Based on data over the period of July 1992 to May 2020, we highlight the role of the global financial uncertainty factor in accurately forecasting gold price volatility relative to the benchmark GARCH-MIDAS-realized volatility model, with a dominant role of European financial uncertainties, and 36 out of the 42 regional financial market uncertainties. The forecasting performance of the global financial uncertainty factor is as good as an index of global economic conditions, with results based on a combination of these two models depicting evidence of complementary information. Moreover, the GARCH-MIDAS model with global financial uncertainty cannot be outperformed by the multivariate version of the GARCH-MIDAS framework, estimated using the adaptive LASSO, involving the top five developed and developing countries each, chosen based on their ability to explain the movements of overall global financial uncertainty. Our results imply that as financial uncertainties can improve the accuracy of the forecasts of gold returns volatility, it would help investors to design optimal portfolios to counteract financial risks. Also, as gold returns volatility reflects financial uncertainty, accurate forecasts of it would provide information about the future path of economic activity, and assist policy authorities in preventing possible economic slowdowns. Full article

The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets. Full article

We establish lower norm bounds for multivariate functions within weighted Lebesgue spaces, characterised by a summation of functions whose components solve a system of nonlinear integral equations. This problem originates in portfolio selection theory, where these equations allow one to identify mean-variance optimal portfolios, composed of standard European options on several underlying assets. We elaborate on the Smirnov property—an integrability condition for the weights that guarantees the uniqueness of solutions to the system. Sufficient conditions on weights to satisfy this property are provided, and counterexamples are constructed, where either the Smirnov property does not hold or the uniqueness of solutions fails. Full article

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty. Full article

This research work extends the least squares criterion. The regression models which have been treated so far in the literature do not study multilinear relationships between variables. Such relationships are of a nonlinear nature. They take place whenever two or more than two univariate variables are the components of a multiple variable of order 2 or an order greater than 2. A multiple variable of order 2 is not a bivariate variable, and a multiple variable of an order greater than 2 is not a multivariate variable. A multiple variable allows for the construction of a tensor. The $\alpha$-norm of this tensor gives rise to an aggregate measure of a multilinear nature. In particular, given a multiple variable of order 2, four regression lines can be estimated in the same subset of a two-dimensional linear space over $\mathbb{R}$. How these four regression lines give rise to an aggregate measure of a multilinear nature is shown by this paper. In this research work, such a measure is an estimate concerning the expected return on a portfolio of financial assets. The metric notion of $\alpha$-product is used to summarize the sampling units which are observed. Full article

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises. Full article