Portfolio Risk Reduction Research Articles

CARBON RISK HEDGING: REDUCING PORTFOLIO CARBON RISK USING A BETA HEDGE RATIO

In response to the global transition to a low-carbon economy, carbon risk reduction in diversified portfolios has become imperative. We develop a new hedge approach to mitigate carbon risk, quantified as the carbon beta using a multi-factor model. By calculating a novel hedge ratio, investors are provided with an optimal allocation to a hedge asset, facilitating the effective reduction of carbon beta within their portfolios. Our findings highlight the effectiveness of this approach, which leads to carbon beta reductions without significant losses in risk-adjusted returns, implying that investors and fund managers can adopt this hedge strategy to moderate their carbon risk exposure. Compared to other carbon risk hedge methods, our approach only requires an investment in one more asset, making real-world applications relatively straightforward.

Impact of Crypto Assets as Risk Diversifiers: A VAR-based Analysis of Portfolio Risk Reduction

This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.

Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking

This study utilizes the random forest technique to investigate risk management practices and concerns in Islamic banks using survey data from 2016 to 2021. Findings reveal that larger banks provide more consistent survey responses, driven by their confidence and larger survey budgets. Moreover, a positive link is established between a country’s development, characterized by high GDPs and low inflation and interest rates, and the precision of Islamic banks’ survey responses. Analyzing risk-related concerns, the study notes a significant reduction in credit portfolio risk attributed to improved risk management practices, global economic growth, stricter regulations, and diversified asset portfolios. Concerns related to terrorism financing and cybersecurity risks have also decreased due to the better enforcement of anti-money laundering regulations and investments in cybersecurity infrastructure and education. This research enhances our understanding of risk management in Islamic banks, highlighting the impact of bank size and country development. Additionally, it emphasizes the need for ongoing analysis beyond 2021 to account for potential COVID-19 effects and evolving risk management and regulatory practices in Islamic banking.

Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for global financial crises

AbstractWe employ and examine vine copulas in modeling symmetric and asymmetric dependency structures and forecasting financial returns from 2001 to 2022, a period that includes the 2008 financial crisis, the 2011 European sovereign debt crisis, the 2020 COVID‐19 pandemic crisis, and the 2022 Russian invasion of Ukraine with the resulting energy crisis. We analyze the asset allocations performed and test different portfolio strategies, such as maximum Sharpe ratio, minimum variance, and minimum conditional value at risk. Using international financial market indices, we specify the regular, drawable, and canonical vine copulas, such as the Gaussian, Student's , Clayton, Frank, Joe, Gumbel, and mixed copulas, and analyze both in‐sample and out‐of‐sample portfolio performances. Out‐of‐sample portfolio back‐testing shows that vine copulas reduce portfolio risk better than the benchmark portfolio strategies and also better than simple multivariate copulas. Overall, we find that mixed vine copula models perform best with regard to risk reduction, both for the entire period 2001–2022 and during financial crises periods. Thus, a mixture of symmetric and asymmetric copula families works best in terms of portfolio risk reduction irrespective of the chosen optimization approach.

Quantile Dependence between Crude Oil and China’s Biofuel Feedstock Commodity Market

This paper investigates the heterogeneous dependence between global crude oil futures and China’s biofuel feedstock commodities under different market conditions. Quantile-on-quantile regression and the causality-in-quantiles test are employed to capture comprehensive and informative relationships. The empirical results are as follows: First, there is a positive relationship between the returns on China’s biofuel feedstock commodities and crude oil. The effects are heterogeneous, conditional on the market regimes, where the impacts of the bearish/bullish crude oil market on biofuel feedstock commodity returns are significant when the commodity market in China is in a bearish/bullish state. Second, crude oil returns have reliable predictive power for the returns on China’s biofuel feedstock commodities under the average market condition and move in connection with the volatility of China’s biofuel-related commodity market in normal and bullish market conditions. Third, the risk reduction effectiveness of soybean and corn is significant, while for wheat, this reduction in portfolio risk is less apparent and enhanced, and the risk reduction effectiveness increases significantly during financial and oil crises. Overall, our findings will be helpful in understanding the heterogeneous interplay between global oil and China’s biofuel-related commodities and in evaluating portfolio diversification opportunities under different market conditions.

Dynamic time-frequency connectedness between European emissions trading system and sustainability markets

This paper examines the time-frequency spillovers and connectedness network between European ETS and the sustainability markets. Empirically, we rely on the Baruník and Křehlík (2018) Diebold and Yilmaz (2012) spillover index to measure the time-varying spillovers, directional spillover, net directional spillover and connectedness network in the short-term and long-term investment horizons. Our findings suggest higher overall interconnectedness between the carbon price returns and sustainability indices, with a total spillover index of 69.75%. More specifically, the magnitude of spillovers is substantially higher in the short term than in the long terms. Moreover, European, France, and Germany sustainability indices exhibit the properties of the largest transmitter of spillover of returns towards the system whereas carbon prices are the lowest contributor to transmitting the spillovers towards the system. In the connectedness network, ETS offers a good investment opportunity with Belgium and Finland sustainability indices due to the lower level of returns connectedness. Finally, ETS provides the portfolio risk reduction in the short-term and long-term investment horizons.

The risks of trading on cryptocurrencies: A regime-switching approach based on volatility jumps and co-jumping behaviours

ABSTRACT Previous research has shown volatility jumps and co-jumping behaviours in cryptocurrency markets. Motivated by these findings, we employ the herding effect and financial contagion channel to outline a theoretical framework of volatility-state-dependent correlations in cryptocurrency markets. We show that digital currency markets are more strongly correlated when experiencing an identical volatility regime, which echoes co-jumping behaviours addressed by the literature. Moreover, the strong correlation that occurs when the paired cryptocurrencies simultaneously experience a high volatility regime results in the least effectiveness of diversification in terms of a minimum portfolio risk reduction. Last but not least, the proposed state-dependent approach in this study proves effective at the task of risk forecasting and risk reduction for cryptocurrency portfolios, beyond the bivariate GARCH-based models, which are a pure and simple time-dependent approach.

Co-Movement of Indonesian State-Owned Enterprise Stocks

According to portfolio theory, diversifying investment to several stocks with negative correlations may reduce portfolio risk. In contrast, combining stocks with similar movement (co-movement) has no impact on portfolio risk reduction. This study aims to examine state-owned enterprise stock co-movement in Indonesia using orthogonal generalized auto-regressive conditional heteroscedasticity (O-GARCH) to help investors selectively choose stocks in a portfolio to reduce portfolio risks. Saturation sampling was used since all state-owned enterprise stocks listed on the Indonesian Stock Exchange were selected as samples. Based on monthly data from January 2013 to December 2021, the O-GARCH method was able to simplify the covariance matrix of the 17 SOEs. Of 17 SOEs, 11 had co-movement, as indicated by their similar principal components, whereas the remaining 6 stocks had a different principal component. Hence, investment managers or investors should not put the eleven stocks in the same portfolio as they have similar risk factors; instead, they may combine them with the six remaining SOE stocks which have a different co-movement. In addition, when the fiscal deficit is high and unconventional monetary policy is implemented in a crisis period, the SOE stock co-movement is higher. Thus, the SOE stock co-movement also depends on government-related matters and faces slightly different risks compared to its private-sector counterparts. Hence, the regulators formulating the policy on SOE stock holdings may use the results of this study by considering the potential merging of the SOE stocks with a similar stock return co-movement by taking account the timing in relation to fiscal deficit and crisis period.

Forecasting Asset Dependencies to Reduce Portfolio Risk

Financial assets exhibit dependence structures, i.e., movements of their prices or returns show various correlations. Knowledge of assets’ price dependencies can help investors to create a diversified portfolio, aiming to reduce portfolio risk due to the high volatility of the financial market. Since asset dependency changes with time in complex patterns, asset dependency forecast is an essential problem in finance. In this paper, we organize pairwise assets dependencies in an Asset Dependency Matrix (ADM) and formulate the problem of assets dependencies forecast to predict the future ADM given a sequence of past ADMs. We propose a novel idea viewing a sequence of ADMs as a sequence of images to capture the spatial and temporal dependencies among the assets. Inspired by video prediction tasks, we develop a novel Asset Dependency Neural Network (ADNN) to tackle the ADM prediction problem. Experiments show that our proposed framework consistently outperforms baselines on both future ADM prediction and portfolio risk reduction tasks.

Mitigating financial risk of corporate power purchase agreements via portfolio optimization

In a world where corporate sustainability commitments are increasing, and governments are starting to phase out of renewable energy subsidy schemes, renewable power purchase agreements (PPAs) can act as a substitute to the governmental support. The combination of renewable energy projects placed in multiple locations (multi-location) and based on different technologies (multi-technology) has the potential to offset the volatility of single renewable energy projects and the risk associated with them. This paper presents a stochastic optimization framework to maximize the expected financial performance of multi-location and multi-technology PPAs while minimizing the financial risk associated with them, which is defined as the likelihood of having low financial performance, from the corporate off-taker perspective. This is achieved through the assessment and optimization of multi-location and multi-technology PPA portfolios. Conditional value at risk is applied to reduce the risk of low financial performance while preserving high expected financial gains. The developed framework enables the identification of the optimal trade-offs between expected financial performance and financial risk, and of the corresponding optimal configurations of PPA portfolios. The benefits of multi-technology and multi-location on portfolio risk reduction are demonstrated via the application to two PPAs portfolios, namely, one compounding projects with different technologies located in the same country, and one compounding different technologies across different countries. Results show that higher expected financial gains are obtained by investing in the single, most-convenient PPA project, whereas combining projects reduces the overall financial risk and allows to meet the off-taker energy demand profile with higher expected financial performance.

Computer-Aided Decision Making for Regional Seismic Risk Mitigation Accounting for Limited Economic Resources

Seismic risk mitigation levels for an existing building are a balance between the reduction of risk and the cost of rehabilitation. Evidently, the more that is paid the more risk is reduced; however, due to limited public budgets a practical approach is needed to manage the risk reduction program when a portfolio of buildings is concerned. Basically, decision makers face a challenge when there are a large number of vulnerable buildings and there is no plan for how to allocate the appointed budget. This study develops a technological platform that implements a decision-making procedure to establish how to optimally distribute the budget in order to achieve the maximum possible portfolio risk reduction. Decisions are made based on various presumed intervention strategies dependent on building’s level of risk. The technological platform provides an interactive, user-friendly tool, available online, that supports stakeholders and decision makers in understanding what the best economic resource allocation will be after selecting the available budget for a specific portfolio of buildings. In addition, the ease of use enables the user to analyze the extent of risk reduction achievable for different budget levels. Therefore, the web platform represents a powerful tool to accomplish two challenging tasks, namely optimal budget selection and optimal budget allocation to gain territorial seismic risk mitigation.

Risk Reduction and Efficiency Increase in Large Portfolios: Gross-Exposure Constraints and Shrinkage of the Covariance Matrix

Abstract We investigate the effects of constraining gross-exposure and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller versus larger universe of stocks, alternative portfolio formation objectives (global minimum variance versus exposure to profitable factors), and various transaction cost assumptions, we find that a judiciously chosen shrinkage method always outperforms an arbitrarily determined constraint on gross-exposure. We extend the mathematical connection between constraints on the gross-exposure and shrinkage of the covariance matrix from static to dynamic, and provide a new explanation for our finding from the perspective of degrees of freedom. In addition, both simulation and empirical analysis show that the dynamic conditional correlation-nonlinear shrinkage (DCC-NL) estimator results in risk reduction and efficiency increase in large portfolios as long as a small amount of short position is allowed, whereas imposing a constraint on gross-exposure often hurts a DCC-NL portfolio.

Factor-Based Optimization of a Fundamentally-Weighted Portfolio in the Illiquid and Undeveloped Stock Market

In this paper, the possibility of using fundamental weighting as a tool to intentionally tilt a portfolio toward specific and unobservable risk factors in the illiquid and undeveloped Croatian stock market is explored. Thus far, fundamental-weighting has been shown to be able to outperform the cap-weighted index in such environments but no attempt regarding control for implicit factor exposure of such portfolios has been reported. Therefore, in this study principal component analysis is performed to capture the underlying risk factors of the fundamentally-weighted portfolio in order to optimize the portfolio’s performance by minimizing its volatility. Previous attempts focusing purely on portfolio risk reduction by estimating minimum variance portfolios failed both from an in-sample and out-of-sample perspective. Results in this study are based on 22 in-sample and out-of-sample tests in the period from March 2009 till March 2020. On the in-sample estimation basis, the proposed approach significantly improves the portfolio’s performance and, if restrictions to weights are imposed, it can outperform the cap-weighted benchmark. However, out-of-sample testing yielded poor results both in terms of risk and return. Such results are in contrast to findings for the developed markets but corroborate the claim that a broad investment base is needed for successful risk exposure in the long run.

A novel risk management framework for natural gas markets

AbstractThis paper examines dynamic hedges in the natural gas futures markets for different horizons and explores the gains from devising risk management strategies. Despite the substantial progress made in developing hedging models, forecast combinations have not been explored. We fill this gap by proposing a framework for combining hedge‐ratio predictions. Composite hedge ratios lead to significant reduction in portfolio risk, whether spot prices are partially predictable or not. We offer insights on hedging effectiveness across seasons, backwardation‐contango conditions and the asymmetric profiles of long‐short hedgers. We conclude that forecast combinations better reconcile realized performance with the hedging process, mitigating model instability.

Risk Reduction and Tracking Error in Small Commercial Real Estate Portfolios

Because of the extreme heterogeneity and lumpiness of commercial real estate, the topic of diversification and risk management has always attracted substantial interest, and a large body of work has been written on the subject. However, most of the analysis has focused on just a few markets, most notably the United Kingdom, or has been based on limited samples. This article uses asset-level data from over 9,000 office assets in the MSCI Global Annual Property Index to explore the performance of small portfolios (1 to 30 assets) over two separate five-year periods (2007–2011 and 2013–2017) in 16 national markets. The analysis focuses on small portfolios of fewer than 30 assets because many investors (even institutional investors and funds) have portfolios that are relatively small in terms of number of assets. The results provide a globally consistent overview of how asset correlations and market risk affected small commercial real estate portfolios and may have implications for the way investors and managers think about diversification in their exposures. TOPICS:Real estate, risk management Key Findings • Simulating the performance of commercial real estate portfolios with up to 30 assets suggests that even small portfolios can substantially reduce the amount of expected tracking error and volatility. • However, asset correlations vary considerably across markets and over time, meaning that estimates of risk reduction and diversification potential for one market during a specific period may be of limited use when applied to another market or a different period. • Investors may also wish to consider the fact that while the average portfolio can be expected to achieve a reasonable amount of diversification, individual portfolios cannot always be expected to behave like the average portfolio due to the heterogeneity of the asset class.

Risk Reduction and Efficiency Increase in Large Portfolios: Leverage and Shrinkage

We investigate the effects of constraining leverage and shrinking covariance matrix in constructing large portfolios, both theoretically and empirically. Considering a wide variety of setups that involve conditioning or not conditioning the covariance matrix estimator on the recent past (multivariate GARCH), smaller vs. larger universe of stocks, alternative portfolio formation objectives (Global Minimum Variance vs. exposure to profitable factors), and various transaction cost assumptions, we find that a judiciously-chosen shrinkage method always outperforms an arbitrarily-determined leverage constraint. By extending the mathematical connection between leverage and shrinkage from static to dynamic, we provide a new theoretical explanation for our finding from the perspective of degrees of freedom. In addition, both simulation and empirical analysis show that the DCC-NL estimator results in risk reduction and effciency increase in large portfolios as long as a small amount of leverage is allowed, whereas tightening the leverage constraint often hurts a DCC-NL portfolio.

A Note on “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps”

ABSTRACTThis note corrects an error in the proof of Proposition 2 of “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraint Helps” that appeared in the Journal of Finance, August 2003.

VALUE AT RISK OF MOMENTUM INVESTMENT STRATEGY: INDONESIA'S LIQUID STOCKS PORTFOLIO

The capability of momentum investment strategy was explore through portfolio risk reduction by value at risk method at liquid shares in Indonesia stock exchange period 2008-2016. The purpose of this study are to analyse the value of momentum investment strategy risk reduction with the Value at Risk approach to historical-volatility approach and examine differences in risk reduction performance by winner and loser portfolios formed from a collection of liquid shares in the Indonesia stock exchange for the period 2008-2016. The stocks selection method in forming winners and losers portfolio done by Jegadesh and Titman procedure (1993) followed by calculation of risk reduction with the VaR-HisVol approach. The result show for quarterly and semester period winner portfolio has superior capacity of portfolio risk reduce than loser.
 Keywords—Investment; Strategy; Portfolio, VaR.
 Abstrak
 Kemampuan strategi investasi momentum dieksplorasi dalam teminologi pengurangan risiko portofolio dengan metode value at risk pada portofolio saham saham likuid di Bursa Efek Indonesia periode 2008-2016. Metode pemilihan saham pembentuk portofolio pemenang dan pecundang dilakukan dengan prosedur Jegadesh dan Titman (1993) dilanjutkan dengan kalkulasi pengurangan risiko dengan pendekatan VaR-HisVol. Hasil menunjukkan untuk portofolio pemenang periode triwulanan dan semester memiliki kapasitas superior mengurangi risiko portofolio daripada pecundang.
 Kata kunci— Investasi, Strategi, Portofolio, VaR

Optimal Consumption and Investment Problem Incorporating Housing and Life Insurance Decisions: The Continuous Time Case

AbstractThis study considers the optimal consumption‐investment‐insurance problem incorporating housing decisions of a household when interest rates and labor income are stochastic. Under the complete market assumption, we derive the closed‐form solution of the optimal insurance demand, portfolio choice, and housing consumption. We calibrate the model using data from the financial market of Taiwan. We find that the insurer's pricing strategy has a significant impact on the household's consumption pattern. Specifically, additional loading in insurance premium allows the life‐cycle model to produce hump‐shaped consumptions of both perishable goods and housing. Loading also creates an unfair background risk to households. However, we only find a small portfolio risk reduction, because households optimally choose a large coverage to mitigate the mortality exposure. This suggests empirical background risk studies overestimate the risk reduction when insurance is available.

When Rationality Meets Passion: On the Financial Performance of Collectibles

This article examines prior evidence and proposes an empirical study of the performance of passion investments in comparison with financial and real assets over the past 20 years. Over this period, classic cars and fine wines (but not visual art) display better returns than U.S. equity, fixed income, and real estate. Volatilities are, overall, low but increase once returns are adjusted for the inherent illiquidity on collectible markets. In a CAPM framework, only classic cars yield significant risk-adjusted returns with an annualized alpha of 5%. At the same time, correlations and systematic risk are low for all collectibles. This diversification benefit is confirmed by a 7% portfolio risk reduction following the inclusion of collectibles in a traditional financial portfolio. The authors further document that the inherent segmentation of collectible classes extends the benefits of cross-asset to intra-asset class diversification. Finally, they find that collectibles have performed slightly less well since the Global Financial Crisis.