Asset Portfolio Research Articles

Drawdown risk measures for asset portfolios with high frequency data

Drawdown risk measures for asset portfolios with high frequency data

Does cost-inefficiency in Islamic banking matter for earnings uncertainty?

PurposeThis paper aims to investigate a fundamental research question of whether the Islamic banking business model makes corporate earnings more uncertain. This question arises because prior research shows that Islamic banks do well in loan performance but incur more operational costs than conventional banks, indicating the systemic limitation of Islamic banks in business risk management.Design/methodology/approachThe study used a sample of banks to conduct the panel regression analysis with 15 years of data for 532 banks (129 Islamic and 403 conventional) from 23 Muslim countries across the world. The authors estimate earnings uncertainty in two ways: the spread and standard deviation of the country-adjusted return over the sample period and applied the difference-in-difference approach interacting cost to income ratio with the Islamic bank dummy, checking if Islamic bank’s high operational costs contribute to more earning uncertainty.FindingsIslamic banks’ returns on assets are significantly more uncertain than conventional banks due to higher operational costs. Consistent with earlier evidence, the study also finds that Islamic banks generally have fewer nonperforming loans than conventional banks. The authors conclude that Islamic banks trade-off between reducing credit risk and escalating business risk.Originality/valueThis study documents that the Islamic banking model helps build a safer asset portfolio but gives rise to the uncertainty of corporate earnings. Therefore, the choice between Islamic and conventional banking models involves a trade-off between credit and business risks. It is a new finding that we add to the literature body on Islamic finance.

Dynamic Interactions Between the Bitcoin Price Index and Widely Traded Financial Assets: Evidence from the Recent COVID-19 Crisis

We investigate the connectedness between the Bitcoin Price Index (BPI) and widely traded financial assets from different markets (equities, bonds, commodities and fiat currency). Using daily data from 5 August 2013 to 31 August 2021 (before and during the COVID-19 crisis), we run the VAR model estimation to investigate any connectedness between the BPI and financial assets, followed by the Granger causality test. We then test Diebold and Yilmaz’s (2012) framework to analyse the spill-over effects among our set of variables. Our empirical results provide strong evidence that the BPI index exhibits significant independence from widely traded financial assets. This independence is less pronounced during the COVID-19 crisis, but remains relatively important. To test whether Bitcoin constitutes a relevant diversification vehicle, we compute the Sharpe portfolio performance index of two portfolios of conventional assets with and without the BPI, before and during the COVID-19 crisis. Our results show that the introduction of the BPI index as a diversification asset does not improve portfolio performance. Overall, our empirical findings are robust to different robustness tests and provide several implications for hedgers, portfolio managers and policymakers.

Money, exchange rate and export quality

This paper theoretically examines the effect of an expansionary monetary policy on export quality and its ramifications on the aggregate employment of the unskilled workers in a competitive general equilibrium framework of a small open economy. Monetary policies are often pursued by the central bank of an economy to manage exchange rate fluctuations under a managed float regime, which may have adverse consequences for export-quality choices and thereby for export growth given the growing preference of buyers in richer nations for higher qualities of imports. Under optimal allocation of wealth over a portfolio of cash, domestic assets and foreign assets, we show that an increase in the domestic money supply affects the choice of export-quality through larger investment, capital formation and consequent endowment effect and through changes in the nominal exchange rate. Under less price-elastic demand for a non-traded good, export quality is upgraded when quality upgrading is relatively capital (than skill) intensive. The expansionary monetary policy may raise aggregate employment of unskilled workers due to larger investment and capital formation, but may lower it through changes in the quality of the export good. The overall effect is thus ambiguous. A larger initial size of bequests has a similar effect.

VOLATILITY SPILLOVER BETWEEN GERMANY, FRANCE, AND CEE STOCK MARKETS

The CEE stock markets are more and more integrated in the European financial markets. The growth of the integration of financial markets favours the volatility and return spillover between them. The current study analyses the volatility spillover among the stock markets in the countries from Central and East Europe (CEE) and Germany and France with the aim to identify the possibilities of reduction of a portfolio risk. A special attention is granted to the analysis during the pandemic caused by COVID-19. The time-varying parameter vector autoregressive (TVP-VAR) model on which is based the methodology proposed by Antonakakis and Gabauer (2017) is used to estimate the evolution in time of volatility spillover. The empirical results obtained for the period January 2001 – September 2021 highlight the increase in volatility spillover between the countries analysed when the pandemic caused by COVID-19 was confirmed. The lack of volatility integration of the markets analysed enables the making of arbitrages in order to reduce the risk of a portfolio. The results obtained are important in the management of financial asset portfolios.

Models for Determining the Expected Utility in the Placement of Asset Portfolios

Models for Determining the Expected Utility in the Placement of Asset Portfolios

A new rating of sustainability based on the Morningstar Sustainability Rating

Nowadays, investors seek beyond the financial performance of their investments, including Environmental, Social, and Governance (ESG) criteria in the decision-making process. To this effect, there are currently suppliers who offer different methodologies to evaluate the ESG factors that companies apply in their businesses. In 2016, Morningstar launched the Morningstar Sustainability Rating for mutual funds, which provides information on how well mutual funds incorporate sustainable assets. This rating is obtained from the Morningstar Portfolio Sustainability Score, the calculation system of which was modified and improved in 2018. However, an important drawback remains to be overcome because the score is only calculated if at least 67% of the portfolio assets have an ESG score and it does not take assets without an ESG score into account. This paper aims to provide a more reliable rating for investors because it proposes the inclusion of all the assets in the fund valuation, regardless of whether they have an ESG score, using fuzzy set theory and specifically triangular fuzzy numbers.

CAN GOLD HEDGE THE RISK OF FEAR SENTIMENTS?

This paper investigates the interaction between fear sentiments and gold price (GP) by utilising the full-sample and sub-sample rolling-window bootstrap causality tests. It can be observed that GP can hedge the risk of fear sentiments in a certain period. The result supports the inter-temporal capital asset pricing model, which demonstrates that the increase in fear sentiments can promote the rise in gold prices. Due to excessive panic, fear sentiments also have negative effects on GP. In contrast, GP positively impacts fear sentiments, which manifests that market sentiment can be forecasted based on changes in the gold market. In addition, the negative influences from GP to fear sentiments indicate there are diversified assets that can be alternatives to gold. In the complicated international environment and volatile market sentiments, investors can benefit by optimising their asset portfolio. The governments can mitigate the adverse effects of large fluctuations in both markets by grasping the movement of gold and fear sentiments.

Family Life Cycle, Asset Portfolio, and Commercial Health Insurance Demand in China

Based on the cross-sectional data of the China household finance survey (CHFS) in 2017, this paper aims to empirically examine the effects of the family life cycle, financial status, and asset portfolio on commercial health insurance demand (breadth and depth) by constructing Probit and Tobit models, respectively. Based on all of the samples, it has been found that family life cycle, family financial status, and family asset portfolio have different influences on the breadth and the depth of health insurance. In terms of the family life cycle, there is an "inverted U-shaped" relationship with the breadth and the depth of health insurance, and the effect is obvious. In terms of family financial status, total household consumption has positive and significant effects on the breadth and depth of health insurance. Total household income and total household debt only have a significant positive impact on health insurance breadth. The total household asset portfolio is only positively correlated with health insurance depth. In terms of the family asset portfolio, the share of real estate assets has a crowding out effect on the breadth and the depth of health insurance. The share of savings assets has no significant effect on the breadth and the depth of health insurance but is positively correlated with the former and negatively correlated with the latter. Both the share of vehicle assets and the share of investment assets only have a significant impact on the breadth of health insurance; however, the positive and negative correlation is different. Based on the sub-samples, it has been found that the breadth and the depth of health insurance vary greatly in the regions and household registration characteristics.

Behavioral Framework of Asset Price Bubbles: Theoretical and Empirical Analyses

Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model.

The impact of the Russia–Ukraine conflict (2022) on volatility connectedness between the Egyptian stock market sectors: evidence from the DCC-GARCH-CONNECTEDNESS approach

PurposeThis study investigates the impact of the Russia–Ukraine war (2022) on the volatility connectedness between Egyptian stock market sectors.Design/methodology/approachThis study employs the newest dynamic conditional correlation (DCC)-generalized autoregressive conditional heteroskedasticity (GARCH)-CONNECTEDNESS approach to examine volatility connectedness in a sample of ten sectors in the Egyptian stock market, namely banks, education, food, healthcare, industry, information technology, real estate, resources, transportation and travel, ranging from February 1, 2019 to May 31, 2022.FindingsThe findings show that connectedness among the Egyptian stock market sectors varies depending on the time. The average dynamic connectedness measure among sectors in Egypt is 73.24%. This average was 85.63% during the Russia–Ukraine War (2022). The author also shows that the transportation sector is the most significant net transmitter of volatility in the remaining sectors during the Russia–Ukraine War (2022).Practical implicationsThis study intends for policymakers to examine the co-movements, market variations and volatility spillover of stock markets, particularly during crises. Furthermore, the results help investors gain insight into diversifying the investors' portfolio assets to optimize profits.Originality/valueTo the best of the authors' knowledge, no study has investigated the implications of the war between Russia and Ukraine (2022) on sectoral interconnectedness within the stock markets in any country and discussion and empirical evidence from African countries are lacking. This study fills this gap in the literature. Additionally, the author uses the newest approach, the DCC-GARCH-CONNECTEDNESS approach, to describe the time-varying volatility spillover between economic sectors in Egypt.

Reflecting Sustainability in the Analysis of Highest and Best Use: Evidence from Polish Municipalities

Abstract The combination of policy concerns over climate and demographic change, energy shortages, resource efficiency and the natural environment, has led municipalities to be expected to reflect sustainability in different actions, including the decision-making on a considerable amount of their real property assets. As more and more municipalities, use the highest and best use analysis for reviewing the configuration of real property asset portfolio to achieve public goals, this provokes an examination of the reflection of sustainability (environmental, economic and social dimensions) in this kind of elaboration. Thus, this paper aims to investigate how Polish municipalities deal with the incorporation of sustainability into the highest and best use analysis and its operationalization in four tests (legally permissible, physically possible, financially feasible, and maximally productive). The research goal was pursued based on quantitative research using surveys conducted between April and May 2022 among eleven municipalities (creating the largest metropolitan areas in Poland) and qualitative research by the content analysis of HBU analyses prepared for them in previous years.

Do Nonfinancial Firms Use Financial Assets to Take Risk?

Abstract Using hand-collected data on financial asset portfolios and exploiting the 2014 oil price crisis as an exogenous cash flow shock, we investigate financial risk-taking at distressed firms. We find that distressed firms, with high debt rollover risk proxied for by short-term liabilities, substantially increase their investments in risky financial assets, including corporate debt, equity, and mortgage-backed securities. The effects are stronger for unhedged firms with low collateral assets. Overall, we provide new evidence that distressed firms take risk using financial assets camouflaged as cash reserves, which, compared to real assets, are less visible and carry lower transaction costs and accelerated payoffs. (JEL G32, G34)

Portfolio optimization in district heating: Merit order or mixed integer linear programming?

Long-term portfolio optimization is commonly used to find the most cost-effective design and operation of a district heating system, subject to technical, financial, and environmental restrictions. Optimizing a district heating system is not trivial and demands high accuracy and high computational speed. However, existing methods addressing this problem offer one or the other but not both at the same time. The state-of-the-art method for portfolio optimization is mixed integer linear programming (MILP), which is extensively used in industry and academia but can be computing and resource-intensive for large portfolio models. This limitation has motivated the development of various options to reduce the computation time while maintaining the accuracy to a large extent. An alternative method to MILP is the merit order (MO) method, which has been used especially for power generation applications due to its simplicity and faster computation but somewhat reduced accuracy. The aim of this paper is to investigate the potential advantages and disadvantages of MO models compared to MILP models in the context of optimizing the portfolio of assets supplying a district heating network. As a study case, we analyze a large portion of the district heating network in Berlin. Four MO model variants with different levels of complexity are proposed and compared to a reference MILP model. Results suggest that MO models variants including heat storage and describing CHP plants with significant detail have the potential to reduce calculation time by nearly three orders of magnitude compared to the reference MILP model, without significantly sacrificing accuracy. In fact, differences in heat generation and net present value (NPV) between the most accurate MO model and the reference MILP model account for ±4% and −6%, respectively. Moreover, results show that combining MO and MILP models is advantageous and offers high computational speed and at the same time high accuracy, especially when a large number of runs might be necessary. MO models could thus be used prior to MILP models to perform a pre-evaluation, an exploration of sensitivities, or for downsizing the initial optimization problem. Combining MO and MILP models could result in faster and more robust decision-making, which could otherwise not be attained with any of the two options individually.

Impact of Livelihood Strategies on the Dependence on Agriculture in Arid Regions of Andhra Pradesh, India

The livelihood risks tend to change households’ capital, assets, or resources by impacting the value of their asset portfolio. This study attempts to understand various risks and how these risks affect the farming community’s livelihoods in the designated areas. Also, this research attempts to understand the relationships between risks and livelihood capital to develop effective measures to improve their well-being and suggest various policies to help farmers cope with these risks in the most effective ways. The study used a sample of 827 farmers from Andhra Pradesh, India. The ordinal logistic regression findings revealed that knowledge capital (knowledge on nutrient deficiency, disease and pests and access to information), social capital (community membership and access to natural resources and services) and financial capitals have a significant impact on the selection of livelihood strategies.

Questioning the Wisdom of Crowds to Design Portfolio Diversification Strategies

For many years, bonds have provided investors in a traditional 60/40 asset mix with a positive carry hedge to equity market volatility and drawdowns. However, with interest rates at historic lows and an evolving inflation backdrop that could impact the negative correlation of stocks and bonds, multi-asset class investors are searching for additional tools to mitigate risk. One novel tool involves creating a long-short portfolio of uncrowded assets. In particular, crowding in stocks held by asset managers has a significant impact on stock performance. Crowded assets and strategies result in outperformance during trending markets but exhibit significant drawdowns and failures of diversification during spikes in volatility. Crowded stocks are expected to deliver negatively skewed market-adjusted returns and higher forward volatility once the market players give up on crowded trades. Conversely, uncrowded assets have the potential for positive skewness and long vol-like behavior. This article computes a stock-level crowding measure based on active bets by mutual and hedge funds. We construct a long-short equity portfolio based on a combination of a pure holdings-based crowding measure that is long volatility, has minimal equity beta, is not persistently correlated with common risk factors (style), and has a negative correlation to the most crowded stocks. We show how this long-short equity portfolio can be combined with a traditional 60/40 portfolio in order to provide better structural diversification.

Optimal allocation of bank resources and risk reduction through portfolio decentralization

The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.

Instabilities in multi-asset and multi-agent market impact games

We consider the general problem of a set of agents trading a portfolio of assets in the presence of transient price impact and additional quadratic transaction costs and we study, with analytical and numerical methods, the resulting Nash equilibria. Extending significantly the framework of Schied and Zhang (2019) and Luo and Schied (2020), who considered the single asset case, we prove the existence and uniqueness of the corresponding Nash equilibria for the related mean-variance optimization problem. We then focus our attention on the conditions on the model parameters making the trading profile of the agents at equilibrium, and as a consequence the price trajectory, wildly oscillating and the market unstable. While Schied and Zhang (2019) and Luo and Schied (2020) highlighted the importance of the value of transaction cost in determining the transition between a stable and an unstable phase, we show that also the scaling of market impact with the number of agents J and the number of assets M determines the asymptotic stability (in J and M) of markets.

Correlation scenarios and correlation stress testing

We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or Highest Density Regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.

Research on Financial Asset Allocation Based on Mathematical Programming Model

With the increase of the total amount of family finance and the continuous enrichment of the types of family financial assets, the awareness of family financial management is increasingly strengthened, and people's understanding of family financial assets and their choice behavior begin to change. Financial asset investment has been deeply involved in people's social business activities, especially commercial banks, investment institutions, foundations, etc., which must participate in the investment and management of valuable financial assets at home and abroad. If investors want to reduce the risk of assets without losing profits, they need to diversify their investments and allocate assets effectively. For specific family financial products, some situations often occur in the process of combination, for example, the specific share of insurance assets is gradually increasing, while the share of cash and savings deposits is gradually decreasing. In this paper, the dominant principle of investment strategy is established, and the mathematical programming model of financial asset allocation is established. On this basis, the method of selecting the optimal financial asset portfolio is put forward.