Portfolio Risk Management Research Articles

Region-wide connectedness of Asian equity and currency markets

This study systemically analyzes the dynamics of interdependence between the Asian equity and currency markets. The novelty of our study is that unlike other studies that explore either co-movements among equity markets or co-movements among currency markets, we pay particular attention to the interdependence between the two in terms of both return and volatility connectedness. We find that the contribution of crossspillovers between the Asian equities and currencies is substantial for the region-wide connectedness of both the returns and volatilities. We also find that the short-term spillovers are far more important for the return spillovers, while the long-term spillovers are far more important for the volatility spillovers, presumably reflecting the long-lasting effects of volatility shocks. All the results consistently underline the pivotal role of cross-interdependence between equity and currency markets, both as channels for integrating Asian financial markets and as sources of financial contagion across these markets. Our findings will provide useful guidance for portfolio risk management to adopt better hedging strategies for foreign exchange risks involved in the international investment of Asian equities.

Управление рисками портфеля сетевой взаимосвязи проектов (на примере агрохолдинга)

The article is devoted to the problem of portfolio risk management of network project interdependencies in an agricultural company, as the lack of existing methods of project portfolio risk assessment and proper consideration of interdependencies are traced. During the study, we conducted an in-depth interview with the CFO of the company to identify the pool of ongoing projects of the company, determine the types of interproject dependencies, the likelihood of the corresponding risk in the project and the level of losses in monetary terms. Using the method of expert assessments and the noisy-OR gate tool, calculations were made in the GeNIe software product to determine the overall risk of the project portfolio after identifying high-risk projects and then recommendations were given for the application of a dynamic portfolio risk management model based on the software product used in the company.

A multivariate CVaR risk measure from the perspective of portfolio risk management

In this paper, we define a new multivariate conditional Value-at-Risk (MCVaR) risk measure. This MCVaR considers both individual risks and the aggregate risk of a portfolio, but prioritizes the aggregate risk. The new MCVaR risk measure is based on the minimization of the expectation of a multivariate loss function, which balances the shortfall and surplus risks of the aggregate risk and the individual risks in an overall risk of a portfolio. It is shown that the MCVaR risk measure holds the properties of positive homogeneity, translation invariance, subadditivity, and monotonicity under certain conditions. Numerical examples of the MCVaR risk measure are presented to illustrate the effect of dependence among individual risks on the MCVaR.

The impact of temperature increase on firm profitability. Empirical evidence from the European energy and gas sectors

Although temperature change is affecting economic activity, there are a few empirical studies on how it influences firm performance. Using panel data of 147 European listed firms active in the energy and gas sectors over the period 2009–2016, this study aims to analyze the impact of temperature change on firm profitability while controlling for firm-specific and energy market-related factors. The results of the quantile regression approach show that an increase in the annual temperature has a positive impact on firm profitability. However, this impact varies at different quantiles of the profitability distribution. A 1℃ degree increase in temperature is related to a 1.30% increase in operating profitability for the most profitable firms (Q90). However, the magnitude of this impact is lower for less profitable firms (Q10 and Q25). The empirical results revealed that cash flows, market opportunities, leverage, firm size, net asset turnover, as well as market concentration and energy prices, have a significant influence on firm profitability in the energy and gas sectors. These findings appear to be robust to several control variables and robustness tests. The study provides empirical evidence on the impact of climate change on the performance of firms active in climate-sensitive sectors (such as energy and gas sectors). The findings of this study are particularly helpful for portfolio risk managers, energy traders, and policymakers.

Covid-19 and high-yield emerging market bonds: insights for liquidity risk management

Around the apogee of the pandemic crisis in late March 2020, trading liquidity has evaporated out of high-yield (HY) bond markets across developing states. Concerned about this phenomenon, we assess emerging market (EM) debt liquidity as a combination of three metrics: (i) bid–ask spreads; (ii) relative liquidity score incorporating market depth, trading volumes, and time needed to liquidate an asset; and (iii) round-trip transaction costs—evidencing that all have worsened by the end of the first quarter of 2020. We complement our analysis by tracking the dynamics of the option-adjusted spreads of the EM HY bonds and document that the recovery trends of the credit and liquidity components in bonds spreads have decoupled in the aftermath of the Covid-triggered global meltdown. We evidence relevant differences in bond liquidity between chosen countries, representative of geopolitical regions. All the considered liquidity measures provide a coherent picture of the pandemic impact and allow for insights regarding the recovery from the crisis turmoil and the risk management of the EM HY bond portfolios throughout a systemic crisis.

Systemic risk spillover across global and country stock markets during the COVID-19 pandemic

Uncovering the tail risk spillover among global financial markets helps provide a more comprehensive understanding of the information transmission in extreme market conditions such as the COVID-19 outbreak. In this paper, we examine systemic distress risk spillover between the global stock market and individual stock markets in the countries most affected by the COVID-19 pandemic. Using two important measures of tail dependence risk: conditional value at risk (CoVaR) and delta conditional VaR (ΔCoVaR), we apply the bivariate dynamic conditional correlation (DCC) conditional autoregressive heteroscedastic (GARCH) model. The empirical results reveal that bivariate systemic risk contagion between the global stock market and each individual stock market evolved during the sample period and intensified as COVID-19 spread worldwide. During the COVID-19 period, the developed markets in Europe and North America transmitted and received more marginal extreme risk to and from the entire global market than Asian stock markets. Further analysis involving the connectedness among the value at risk (VaR) series of the sampled stock market indices and the global stock index, shows a high degree of integration in the extreme downside risk of the stock market system, especially during the COVID-19 period. These findings offer practical implications for regulators, policymakers, and portfolio risk managers during the unprecedented uncertainty period provoked by the COVID-19 pandemic.

Evaluation of Optimal and Coherent Risk-Capital Structures Under Adverse Market Outlooks Using Machine Learning Techniques

This paper broadens research literature associated with the assessment of modern portfolio risk management techniques by presenting a thorough modeling of nonlinear dynamic asset allocation and management under the supposition of illiquid and adverse market settings. This study analyses, from a fund manager’s perspective, the performance of liquidity adjusted risk modeling in obtaining optimal and coherent economic capital structures, subject to meaningful operational and financial constraints as specified by the fund manager. Specifically, the paper proposes a re-engineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (L-VaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both pure long positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios using machine learning techniques, over the holding period, with the aid of an appropriate scaling of the multiple-assets’ L-VaR matrix along with GARCH-M technique to forecast conditional volatility and expected return. The key methodological contribution is a different and less conservative liquidity scaling factor than the conventional root-t multiplier. Moreover, in this paper, the authors develop a dynamic nonlinear portfolio selection model and an optimization algorithm which allocates both economic capital and trading assets by minimizing L-VaR subject to the constraints that the expected return, trading volume and liquidation horizon should meet the budget limits set by the fund manager. In addition, the paper illustrates how the modified L-VaR method can be used by an equity trading unit in a dynamic asset allocation framework for reporting risk exposure, optimizing economic capital, and assessing risk reduction alternatives. The empirical results strongly confirm the importance of enforcing financially and operationally meaningful nonlinear and dynamic constraints, when they are available, on the L-VaR optimization procedure.

Oil and precious metals: Volatility transmission, hedging, and safe haven analysis from the Asian crisis to the COVID-19 crisis

This paper examines the volatility transmission between crude oil and four precious metals (i.e., gold, silver, platinum, and palladium) and investigates whether oil can be considered as a hedge or safe-haven asset against four precious metals. Our empirical analysis reveals several important findings. First, we determine that the volatility transmission was time-varying and that influence from the Asian crisis, the bursting of the dot-com bubble, the 2008 global financial crisis, the recent oil-price crash, and COVID-19 alternated between negative and positive values over the entire studied period. We further conclude that Brent oil is a diversifier and a weak safe haven for precious metals; and thus, that a combined portfolio composed of Brent-oil and precious-metals futures yields better hedging effectiveness. These findings indicate that oil futures are a useful investment that reduces downside risks and strengthens diversification benefits in portfolio risk management.

Spillovers and portfolio risk management of gold and stock markets: evidence from emerging Latin American markets

ABSTRACT This study examines the return and volatility transmission between gold and emerging Latin American stock markets during the full sample period, the global financial crisis, and the Chinese Stock market crash. Employing the VAR-AGARCH model to estimate spillovers, the results reveal the substantial return and volatility spillovers between the gold and emerging Latin American stock markets, but these spillovers vary across different gold-stock pairs and two crises. Lastly, we also provide the optimal weights and hedge ratios for all gold-stock pairs during all sample periods. Overall, these findings provide useful insights for portfolio diversification, asset pricing, and risk management.

‘Macro-finance determinants and the stock market development: evidence from Morocco’

ABSTRACT This paper focuses on the role of the macroeconomic outlook in explaining the returns volatility from 1998 to 2018 in Morocco. Our findings show that the inclusion of low-frequency macroeconomic information can improve the explaining ability, particularly for the long-term variance component. Information in some indicators which represent forward-looking variables (i.e. international economic situation, interest rates, exchange rates and inflation) seem to take into account the current economic situation, and remain useful in the Moroccan stock market development. Nevertheless, the historical realized volatility exceeds all models with macro-finance data in terms of explaining the long-term volatility. Therefore, it is noteworthy that investors and decision makers are considered to be more affected by the stock results, namely the past returns performance and the historical volatility. In general, modeling the long-term volatility component has a great potential and is very useful for portfolio selection, hedging decisions and macroeconomic risk management.

The influence of risk management on the project portfolio success – proposal of a risk intensity matrix

Abstract: This article aims to understand how the risk management influences the project portfolio success. Two methodological approaches were selected in this research: a bibliometric survey followed by a case study. The object of study was the new products project portfolio of an organization from the industrial sector, manufacturer of durable goods. The findings revealed a low intensity of project portfolio risk management. This is aligned with the bibliometric survey results and with the evidence from the investigation performed on the case study unit. In order to evaluate the risk management influence over the portfolio success, this article proposes a matrix which suggests the risk management intensity associated with the project portfolio management processes. The proposed matrix application can be considered a contribution element to deepen the knowledge of the risk management influence on the project portfolio success.

Risk Measures Induced by Efficient Insurance Contracts

The Expected Shortfall (ES) is one of the most important regulatory risk measures in finance, insurance, and statistics, which has recently been characterized via sets of axioms from perspectives of portfolio risk management and statistics. Meanwhile, there is large literature on insurance design with ES as an objective or a constraint. A visible gap is to justify the special role of ES in insurance and actuarial science. To fill this gap, we study characterization of risk measures induced by efficient insurance contracts, i.e., those that are Pareto optimal for the insured and the insurer. One of our major results is that we characterize a mixture of the mean and ES as the risk measure of the insured and the insurer, when contracts with deductibles are efficient. Characterization results of other risk measures, including the mean and distortion risk measures, are also presented by linking them to different sets of contracts.

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies

The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of dynamic equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio of cryptocurrencies by implementing backtesting techniques and for different risk measures: value-at-risk, expected shortfall and median shortfall. The results support our proposal showing that the SNP-DCC model has better performance for lower confidence levels than the SNP-DECO model and is more appropriate for portfolio diversification purposes.

Can We Reckon Bitcoin as a Hedge, a Safe Haven or a Diversifier for US Dollars?

This article aims to analyse the hedging, diversifier and safe-haven properties of Bitcoin for the US Dollar Index (USDI). We explore the long-term relationship between USDI and Bitcoin by estimating a Markov-switching autoregressive (MS-AR) model with two regimes. Thus, the data used cover the period from 18 January 2010 to 30 June 2017 for both USDI and Bitcoin. The empirical findings based on the analysis of the MS-AR model report that investing in Bitcoin involves more benefits than USDI even if the economy is in a recession. However, by examining Bitcoin and USDI volatility, the research findings underline positive dependency between the two. Such results denote that Bitcoin does not act as a hedge, and not even as a safe haven, against USDI. We found that Bitcoin is merely a diversifier for USDI. Accordingly, the outcomes will help investors and portfolio risk managers to make more up-to-date investment analyses and decisions.

How flood risks shape policies: flood exposure and risk perception in Swiss municipalities

Despite an increasing number of people exposed to flood risks in Europe, flood risk perception remains low and effective flood risk management policies are rarely implemented. It becomes increasingly important to understand how local governments can design effective flood risk management policies to address flood risks. In this article, we study whether high flood exposure and flood risk perception correlate with the demand for a specific design of flood risk management policies. We take the ideal case of Switzerland and analyze flood risk management portfolios in 18 flood-prone municipalities along the Aare River. We introduce a novel combination of risk analysis and public policy data: we analyze correlations between recorded flood exposure data and survey data on flood risk perception and policy preferences for selected flood risk management measures. Our results indicate that local governments with high flood risk perception tend to prefer non-structural measures, such as spatial planning and ecological river restoration, to infrastructure measures. In contrast, flood exposure is neither linked to flood risk perception nor to policy preferences. We conclude that flood risk perception is key: it can decisively affect local governments’ preferences to implement specific diversified policy portfolios including more preventive or integrated flood risk management measures. These findings imply that local governments in flood-prone areas should invest in raising their population’s awareness capacity of flood risks and keep it high during periods without flooding.

Managing Financial Risk Trade‐Offs for Hydropower Generation Using Snowpack‐Based Index Contracts

AbstractHydrologic variability poses an important source of financial risk for hydropower‐reliant electric utilities, particularly in snow‐dominated regions. Drought‐related reductions in hydropower production can lead to decreased electricity sales or increased procurement costs to meet firm contractual obligations. This research contributes a methodology for characterizing the trade‐offs between cash flows and debt burden for alternative financial risk management portfolios, and applies it to a hydropower producer in the Sierra Nevada mountains (San Francisco Public Utilities Commission). A newly designed financial contract, based on a snow water equivalent depth (SWE) index, provides payouts to hydropower producers in dry years in return for the producers making payments in wet years. This contract, called a capped contract for differences (CFD), is found to significantly reduce cash flow volatility and is considered within a broader risk management portfolio that also includes reserve funds and debt issuance. Our results show that solutions relying primarily on a reserve fund can manage risk at low cost but may require a utility to take on significant debt during severe droughts. More risk‐averse utilities with less access to debt should combine a reserve fund with the proposed CFD instrument in order to better manage the financial losses associated with extreme droughts. Our results show that the optimal risk management strategies and resulting outcomes are strongly influenced by the utility's fixed cost burden and by CFD pricing, while interest rates are found to be less important. These results are broadly transferable to hydropower systems in snow‐dominated regions facing significant revenue volatility.

Value at Risk and Market Risk: Case of the Regional Securities Exchange

The study of the BRVM market risk using the VaR method is a determining factor in assessing the performance of our equity portfolio composed of the BRVM composite index and the BRVM10 index. It has enabled us, with the help of Basel regulations, to use backtesting to determine the minimum amount of capital that an investor must hold per day to protect against risk. The kupiec test enables us to determine the reliability of VaR calculated at different confidence levels. The result of our study confirms, using the extreme VaR method, the robustness of our threshold-based portfolio risk management approach. It also confirms the problem of market attractiveness during times of financial crisis.

Gold, inflation and exchange rate in dollarized economies – A comparative study of Turkey, Peru and the United States

This paper conducts a comprehensive empirical study on gold’s hedging potential against adverse movements of inflation and exchange rates for three countries: Turkey (highly dollarized), Peru (low dollarized) and United States (benchmark). With the use of quantile-on-quantile regression (QQR) and quantile-on-quantile correlation (QQCOR) models, we find that gold can offer protection against currency movements and inflation fluctuation at all times for Turkey and the United States, but fail to do so during Turkey’s hyperinflationary period; in Peru, gold is a good hedge if the change of CPI is above 3.29% or currency depreciation rate is above 3.24%. We reveal that the conclusion of gold acting as a hedge (safe haven) against inflation or exchange rate volatility conjoin to both the gold market condition and the nature of macroeconomic shocks. Moreover, we use the QQCOR results into a portfolio hedging strategy and further confirm gold’s usefulness in portfolio risk management in terms of return accumulation and risk reduction with respect to a currency only investment.

주요국 주식시장간 상호의존성과 주식포트폴리오 위험관리

주요국 주식시장간 상호의존성과 주식포트폴리오 위험관리

Measuring and Managing Carbon Risk in Investment Portfolios

This article studies the impact of carbon risk on stock pricing. To address this, we consider the seminal approach of Gorgen et al. (2019), who proposed estimating the carbon financial risk of equities by their carbon beta. To achieve this, the primary task is to develop a brown-minus-green (or BMG) risk factor, similar to Fama and French (1992). Secondly, we must estimate the carbon beta using a multi-factor model. While Gorgen et al. (2019) considered that the carbon beta is constant, we propose a time-varying estimation model to assess the dynamics of the carbon risk. Moreover, we test several specifications of the BMG factor to understand which climate change-related dimensions are priced in by the stock market. In the second part of the article, we focus on the carbon risk management of investment portfolios. First, we analyze how carbon risk impacts the construction of a minimum variance portfolio. As the goal of this portfolio is to reduce unrewarded financial risks of an investment, incorporating the carbon risk into this approach fulfills this objective. Second, we propose a new framework for building enhanced index portfolios with a lower exposure to carbon risk than capitalization-weighted stock indices. Finally, we explore how carbon sensitivities can improve the robustness of factor investing portfolios.