Portfolio Risk Research Articles

Firm commonality, bank connectedness and portfolio riskiness

We propose a new connectedness measure that addresses heterogeneous multiple borrowing loan structure among banks. Using confidential data from over 44 million commercial loans from Turkish banks during 2007–2016, we construct a bank network that emerges from the banks lending to common firms. While the related literature mostly focuses on systemic risk, our framework allows us to empirically study banks’ loan portfolio riskiness. First, we document that banks’ portfolio riskiness decreases with connectedness, even when controlling for the impacts of loan size and multiple borrowing. Second, we find that the probability of loan default is higher for highly connected banks compared to weakly connected ones. These findings suggest that highly centralized banks seem to manage their overall portfolio risk better.

Optimal Delegation Contract with Portfolio Risk

Conventional linear benchmarked contracts tend to cause excessive pegging to the benchmark and thus price distortion of stocks in the benchmark. This paper studies the optimal delegation contract when there is principal-agent friction. Specifically, it explores the impacts of incorporating the risk of invested portfolio in the contract on optimal strategies of the principal and the agent as well as on equilibrium asset prices. When agency friction is severe, the optimal contract provides rewards for portfolio risk to improve risk sharing and grants compensation for index return to propel the agent to deviate from pegging to index. In equilibrium, the principal conducts index investment while the agent invests only in individual risky assets, and price distortion caused by agency friction is mitigated.

Volatility-managed portfolios in the Chinese equity market

This paper examines the effectiveness of the volatility-timing strategy in the Chinese equity market. We find that the volatility-managed portfolio (VMP) consistently outperforms its original counterpart. This outperformance is primarily driven by stocks with high arbitrage risks, and is further enhanced when considering the price-limit rule in China. The conditional systematic risks of volatility-managed portfolios are significantly lower during market downturns, serving as a hedge against high volatility. Additionally, the multi-factor portfolio constructed from the individual volatility-managed factors outperforms other multi-factor portfolios, especially during periods of heightened investor sentiment or diminished macroeconomic confidence.

Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques

The financial industry faces an increasingly complex landscape for portfolio management, where data-driven insights are crucial for optimizing asset allocation and managing risk. This paper explores the integration of predictive analytics and scalable data modeling techniques in enhancing financial portfolio management. By leveraging machine learning algorithms, big data architectures, and real-time data processing, predictive analytics can forecast asset trends, detect market anomalies, and assess portfolio risk with high precision. We evaluate several predictive models, including time-series forecasting, neural networks, and ensemble methods, for their efficacy in financial prediction. The study also discusses the role of scalable data modeling frameworks, such as Apache Spark and cloud-based data lakes, in handling vast volumes of unstructured data across different markets and asset classes. Findings indicate that predictive analytics, when paired with robust data models, can deliver real-time, actionable insights, enhancing decision-making for fund managers and institutional investors. Furthermore, this paper highlights best practices for implementing scalable models in financial institutions, addressing challenges like data latency, model interpretability, and system scalability. By adopting these advanced analytics frameworks, portfolio managers can achieve improved risk-adjusted returns, better asset diversification, and enhanced adaptability to market volatility.

Stock Portfolio Analysis with Machine Learning Algorithmic Approach for Smart Investment Decisions

This study investigates the application of machine learning algorithms in stock portfolio analysis within the Indonesia Stock Exchange (IDX) and their impact on investment decision-making. By engaging 500 respondents from diverse market segments, including retail investors, institutional investors, and stock traders, the research provides a comprehensive overview of adopting and utilising machine learning technologies in the Indonesian stock market. The findings reveal that over 80% of respondents have integrated machine learning algorithms into their investment strategies. The algorithms are applied in various capacities: 45% of respondents use them for portfolio risk analysis, 30% for stock price prediction, and 25% for identifying new investment opportunities. Preferences for specific algorithms vary, with regression, Support Vector Machines (SVM), and Random Forest emerging as the most used tools. The integration of machine learning was strongly associated with improved investment decisions, as more than 60% of respondents reported enhanced portfolio performance and greater accuracy in their decision-making. These results highlight the transformative potential of machine learning algorithms in enabling more innovative and more adaptive investment strategies.

The Relationship Between ESG Scores and Value-at-Risk: A Vine Copula–GARCH Based Approach

Recently, the introduction of Environmental, Social, and Governance (ESG) scores has become crucial for investment decisions and in minimizing portfolio risk. This study aims to understand the relationship between ESG scores and Value-at-Risk (VaR), computed by using a Vine copula–GARCH based approach, chosen for its reliability in detecting interdependencies among multiple stocks. In fact, one of the main challenges in estimating VaR for a stock portfolio is capturing the dependence structure among a large number of assets. The dataset consists of 16 companies listed on the FTSE100 index. The corresponding ESG scores were collected over a comprehensive period of five years, from 2018 to 2022, covering both normal and stressed market conditions. Additionally, a focused analysis was conducted for the period from 2020 to 2022 to isolate the specific effects of the COVID crisis. The results indicate that an increase in assets with the highest ESG scores reduces potential losses in the portfolio. This finding underscores the importance of integrating high-level ESG scores into portfolios to mitigate market risk. Additionally, during periods characterized by stressed market conditions, the impact of ESG scores on VaR is even more pronounced, demonstrating that sustainable assets are more resilient in times of crisis.

Investing in relative market positions in interconnected financial markets: A strategy for international portfolio diversification

International investors face challenges in properly diversifying their portfolios risk due to the increasingly interconnected nature of financial markets. Using the Tail-Event Driven Network (TENET) model, this study proposes two new indices, relative dominance and relative importance, to measure the relative market position of a stock market in the global stock market network. This study further proposes a dominance-importance-oriented (DIO) strategy to diversify portfolio risk in interconnected stock markets using data from the stock markets of 35 economies. The empirical findings show that stock markets in developed countries generally have higher relative dominance and lower relative importance, whereas those in developing and emerging countries have the opposite pattern. Moreover, financial liberalization is an important determinant of stock markets' relative positions in the global market network, although its impact varies with the economic development of their domicile countries. Furthermore, the DIO strategy offers a feasible and effective method for international portfolio diversification by jointly considering relative dominance and importance, verifying that portfolio diversification in terms of relative market position is beneficial.

The GARCH-EVT-Copula Approach to Investigating Dependence and Quantifying Risk in a Portfolio of Bitcoin and the South African Rand

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 α=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.

An index-based multi-hazard risk assessment method for prioritisation of existing bridge portfolios

In recent years, several catastrophic collapses of existing bridges have highlighted the need for rapid risk analysis methods aimed at supporting infrastructure managers in the prioritisation of detailed assessments and, if any, risk mitigation actions. A large percentage of existing road bridges were built between the 1960s and 1980s, having thus already reached or even exceeded their design lifetime. Several studies have also shown that bridges often collapse due to either natural or human-related events, such as floods, collisions or overloading that, in addition to earthquakes, should be duly considered in risk assessment. This calls for multi-hazard approaches that provide an integrated perspective of the risk of bridge portfolios, to identify critical structures to support decision-makers. This study proposes a multi-hazard risk-based prioritisation methodology for application to a large number of bridges under limited level of knowledge. Specifically, the risk level is quantified through indices, accounting for uncertainties, that are used for comparative purposes among bridges. The methodology is applied to a highway bridge portfolio located in northern Italy, producing a risk-based ranking that is critically discussed. Analysis results are then compared with the outcome of the current Italian guidelines for safety assessment and maintenance of existing bridges.

The Impact of ESG Ratings on Investment Decisions in the Energy Sector During Uncertain Times

With increasing global awareness of sustainability, the financial sector must inevitably adapt. Our research investigates the role of Environmental, Social, and Governance (ESG) ratings in investment decision-making, particularly in the energy sector, by examining their impact on portfolio performance. The study aims to assess the impact of ESG integration on portfolio risk and returns. Using data from the SP500 index, econometric models and Monte Carlo simulations are employed to evaluate how ESG considerations influence portfolio optimization and risk management. The study reveals that integrating ESG criteria into investment strategies, especially during market volatility, may not always directly affect returns, they provide significant benefits in terms of risk mitigation and ethical investment alignment.

Interconnected Stock Markets: Analysing Cointegration and Correlation among Global Stock Indices

This study examines the long-term and short-term relationships among five major global stock indices—NSE NIFTY (India), S&P 500 (USA), FTSE 100 (UK), Hang Seng (Hong Kong), and Nikkei 225 (Japan)—over the period from 2008 to 2023. Using advanced econometric techniques such as Johansen cointegration tests, Engle-Granger residual cointegration test and the Vector Error Correction Model (VECM), the research explores the degree of interconnectedness and cointegration among these indices. The results indicate strong positive correlations between NSE NIFTY, S&P 500, and Nikkei 225, suggesting a growing synchronization of these markets due to global economic integration. Johansen's test reveals the presence of multiple long-term equilibrium relationships, affirming that despite short-term deviations, these markets tend to move together in response to global macroeconomic factors. The analysis further highlights that the S&P 500 and FTSE 100 indices exhibit significant error correction mechanisms, underscoring their leadership role in global market dynamics. In contrast, NSE NIFTY shows a weaker adjustment toward long-term equilibrium, indicating its reactive nature to changes in other global indices, particularly the S&P 500. The study finds that while Hang Seng and Nikkei 225 are connected globally, their short-term linkages appear weaker, likely influenced by regional factors. Overall, the results emphasize the critical influence of the U.S. and Japanese markets on NSE NIFTY, with both long-term cointegration and short-term market dynamics shaping the Indian stock market’s movements. This research offers key insights for investors seeking to understand global stock market interdependencies and manage portfolio risk.

ON THE SOLUTION UNIQUENESS IN PORTFOLIO OPTIMIZATION AND RISK ANALYSIS

In this paper, we consider the issue of solution uniqueness of the mean-deviation portfolio optimization problem and its inverse for asset returns distributed over a finite number of scenarios. Due to the asymmetry of returns, the risk is assessed by a general deviation measure introduced by Rockafellar et al. [(2006b) Optimality conditions in portfolio analysis with general deviation measures, Mathematical Programming 108, 515–540] instead of the standard deviation as in the classical Markowitz optimization problem. We demonstrate that, in general, one cannot expect the uniqueness of Pareto-optimal profit sharing in cooperative investment and the uniqueness of solutions in the mean-deviation Black–Litterman asset allocation model. For a large class of deviation measures, we provide a resolution of the above nonuniqueness issues based on the principle of law-invariance. We provide several examples illustrating the nonuniqueness and the law-invariant solution.

Diversification of Stationary Data for Optimizing Risk of Portfolio

Diversification is a process of distributing capital that minimizes the exposure of each individual asset. The purpose of diversification is to minimize risk. This paper examines how these two strategies (stationary and non-stationary) can minimize portfolio risk. To achieve this, data on palm oil and copper from 2010 to 2016 are explored, and two methods are used: correlation and mean-variance. Diversification has different degrees, and correlation is used to check the degree of diversification of the two types of data, while the mean-variance method (MV) is used to estimate the risk of the two datasets after the correlation. The analyses in this paper show that the correlation of stationary data leads to moderately weak diversification, while non-stationary data results in very weak diversification. In estimating the risk of the two datasets, stationary data diversifies 93% of the risk, while non-stationary data diversifies 6%. This shows that stationary data minimizes portfolio risk more effectively than non-stationary data.

SOCIAL MEDIA SENTIMENT AND STOCK MARKET VOLATILITY: EVIDENCE FROM THE US HI-TECH COMPANIES

Purpose: This study aims to investigate the effects of social media sentiment, measured by Twitter-based uncertainty index, on the stock market volatility of the US technology companies. Design/methodology/approach: Methodologically, we employ the quantile regression model. Our findings reveal that the volatility of Amazon, Apple, Google and IBM stocks are sensitive to the variations in twitter-based economic and market uncertainties (i.e., TEU and TMU indexes). Findings: We observe significant influences at both lower and upper quantiles. Thus, for both high and low volatility regimes, the information on twitter-based uncertainty indexes can be used to predict the market volatility of these leading hi-tech companies. Moreover, TEU and TMU indexes exert positive effects on the stock price implied volatility implying that the variance of these technology firms experiences an upward trend as the social media uncertainty rises. Originality/value: While numerous studies have focused on the influence of social media (e.g., Facebook, twitter etc.) on investment strategies, the impact of twitter sentiments on the risk linked to hi-tech firms remains understudied. Hence, investors participating in the technology sectors could use our findings for managing portfolio risk.

Dynamic Spillovers from US (Un)Conventional Monetary Policy to African Equity Markets: A Time-Varying Parameter Frequency Connectedness and Wavelet Coherence Analysis

Since the implementation of unconventional monetary policies (UMPs) by the US in response to the global financial crisis (GFC) and the COVID-19 pandemic, there have been increasing concerns that these forward guidance and quantitative easing programmes have had spillover effects on global equity markets. We specifically question whether the implementation of these UMPs have had spillovers to African equities, which have been previously speculated to be decoupled from global markets and shocks. Time-varying-parameter (TVP) frequency connectedness and wavelet coherency methods were used to examine the dynamic time-frequency spillovers between daily time series of the US shadow short rate and African equities returns/volatility between 1 January 2007 and 31 March 2023. On one hand, the TVP frequency connectedness analysis reveals robust long-run spillovers from US monetary policy to African equity markets during specific periods: 2009, 2013, 2020, and 2021. These coincide with instances when the Federal Reserve announced their transition from conventional to unconventional monetary practices and vice versa. On the other hand, the wavelet analysis provides insights into the ‘sign’ of the spillovers, indicating mixed phase dynamics during UMPs responding to the GFC. In contrast, anti-phase or negative co-movements characterize UMPs implemented during the COVID-19 pandemic, implying that these policies increased both returns and volatilities to African equities. Altogether, we conclude that US UMP has increasing deteriorated market efficiency and amplified portfolio risk in African equities whilst during ‘normalization’ periods US monetary policy has little transmission effect.

Spillover and dependence between Chinese carbon and new energy stock markets: A cross-quantile perspective

The interplay between China's carbon emissions trading market and the new energy stock market has attracted increasing scholarly attention, given their alignment with green and sustainable development goals. This study utilizes the Cross-Quantilogram methodology to assess predictability and spillover dynamics in the Chinese carbon markets and the new energy stock markets across various quantiles. The analysis yields some significant findings: (1) Despite the small magnitude, bidirectional spillover effects are discernible between China's carbon market and the Chinese new energy market, with heterogeneous correlation among the three pilot carbon markets. (2) The cross-quantile correlation among markets exhibits varying dynamics with increasing lag orders. (3) During market downturns, the new energy market poses short-term abnormal extreme risks as a spillover source for the Guangdong carbon market, while exhibiting a stronger spillover effect on the Hubei carbon market during longer lag periods. (4) Certain sectors, such as the wind power, nuclear power, and photovoltaic sectors, stand out with a more pronounced cross-quantile in relation to the carbon market. (5) Both risk events and policy changes play a significant role in shaping market interactions. (6) Including Guangdong and Hubei carbon asset in a portfolio can effectively reduce the volatility risk of new energy stock portfolios. A valuable comprehension of the intricate interdependencies between China's carbon market and new energy assets is provided by this research, benefiting policymakers and investors in the energy and environmental sectors.

Tackling nonlinear price impact with linear strategies

AbstractEmpirical studies in various contexts find that the price impact of large trades approximately follows a power law with exponent between 0.4 and 0.7. Yet, tractable formulas for the portfolios that trade off predictive trading signals, risk, and trading costs in an optimal manner are only available for quadratic costs corresponding to linear price impact. In this paper, we show that the resulting linear strategies allow to achieve virtually optimal performance also for realistic nonlinear price impact, if the “effective” quadratic cost parameter is chosen appropriately. To wit, for a wide range of risk levels, this leads to performance losses below 2% compared to a numerical algorithm proposed by Kolm and Ritter, run at very high accuracy. The effective quadratic cost depends on the portfolio risk and concavity of the impact function, but can be computed without any sophisticated numerics by simply maximizing an explicit scalar function.

Volatility spillovers and time-varying correlations across stock and commodity markets: An assessment during the epidemic crisis era

The financial and economic stability of the business world is greatly damaged by a number of crises over the years. These crises also change and affect stock markets around the globe. This study examines the nonlinear behavior of well-known stock markets in the US, Europe, Canada, and Asia (S&P 500, Nikkei 225, CAC40, DAX30, Bombay Stock Index, Hang Seng Index, and Canadian Stock Index). We also investigate the commodity indexes (Gold, Bitcoin, Brent Crude Oil, and West Texas Crude Oil). The transmission of returns and the volatility propagation pattern across stock-commodity markets during the COVID-19 era is analyzed using the VAR DCC MEGARCH model. The results demonstrate that correlations intensify and become more complicated during the COVID-19 period, especially between financial market indices and oil commodity indices. However, the volatility spillover statistics during the pandemic period show a definite rise in the uncertainty of financial market indices due to index instability. The analysis also shows that financial markets experience a substantial leverage effect during the pandemic crisis. According to the research, Bitcoin is crucial for reducing portfolio risk during the health crisis, and gold proves to be the best option for investors to diversify their financial risk.

Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation

Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.

Distortion Risk Measures of Increasing Rearrangement

Increasing rearrangement is a rewarding instrument in financial risk management. In practice, risks must be managed from different perspectives. A common example is the portfolio risk, which often can be seen from at least two perspectives: market value and book value. Different perspectives with different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as an increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce increasing rearrangement for application in financial risk management and to apply increasing rearrangement to the class of distortion risk measures. We derive formulae to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely the value at risk, expected shortfall, range value at risk, conditional value at risk, and Wang’s risk measure. Finally, we present the connection of increasing rearrangement with inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as an illustrative example of the method.