Asset Portfolio Research Articles

Extreme value techniques for stress scenario selection under elliptical symmetry and beyond

Abstract The paper considers the problem of stress scenario selection, known as reverse stress testing, in the context of portfolios of financial assets. Stress scenarios are loosely defined as the most probable values of changes in risk factors for a given portfolio that lead to extreme portfolio losses. We extend the estimator of stress scenarios proposed in [P. Glasserman, C. Kang and W. Kang, Stress scenario selection by empirical likelihood, Quant. Finance 15 (2015), 1, 25–41] under elliptical symmetry to address the issue of data sparsity in the tail regions by incorporating extreme value techniques. The resulting estimator is shown to be consistent, asymptotically normally distributed and computationally efficient. The paper also proposes an alternative estimator that can be used when the joint distribution of risk factor changes is not elliptical but comes from the family of skew-elliptical distributions. We investigate the finite-sample performance of the two estimators in simulation studies and apply them on two financial portfolios.

Exploration of Technological Innovation and Market Strategy of Auction Houses

The global art auction market has long been dominated by two major auction houses, Christie's and Sotheby's, whose global networks and expertise in high-end art transactions have established their leading position. However, in recent years, the rise of the Chinese market has injected new vitality into the global art auction landscape. Although local competitors such as China Guardian and Poly Auction have performed well in regional markets, they face cultural and economic context constraints in their global expansion. This study reviews the latest developments in the art auction industry, with a particular focus on the strategic choices of auction houses and potential opportunities for international competition, while exploring the profound impact of technological innovation on transaction efficiency, market expansion, and customer experience. The study found that Western auction houses rely on diversified asset portfolios and technological innovation to maintain their global advantages, while Chinese auction houses attract emerging consumer groups through specialization in traditional art fields and digital tools. However, the rapid development of technology may invalidate some of the research conclusions, and it is recommended to conduct long-term research in the future to reveal the continued impact of technological changes on the auction industry.

A semi-parametric dynamic conditional correlation framework for risk forecasting

We develop a novel multivariate semi-parametric framework for joint portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting. Unlike existing univariate semi-parametric approaches, the proposed framework explicitly models the dependence structure among portfolio asset returns through a dynamic conditional correlation (DCC) parameterization. To estimate the model, a two-step procedure based on the minimization of a strictly consistent VaR and ES joint loss function is employed. This procedure allows to simultaneously estimate the DCC parameters and the portfolio risk factors. The performance of the proposed model in risk forecasting on various probability levels is evaluated by means of a forecasting study on the components of the Dow Jones index for an out-of-sample period from December 2016 to September 2021. The empirical results support effectiveness of the proposed framework compared to a variety of existing approaches.

Financial development and regional bank instability in the United States: An analysis of bank balance sheet structures

This article demonstrates that financial development in the United States has heightened the vulnerability of both sides of regional banks’ balance sheets to financial market fluctuations, drawing on the concept of financial circulation of bank deposits. It first argues that financial development provides diversified short-term investment channels for non-financial sectors to manage their liquid asset holdings. Since banks intermediate money circulation, changes in liquid asset portfolios inevitably result in sudden inflows and outflows of deposits to these banks. These deposit flows are highly clustered during specific financial phases and are subject to financial volatilities rather than local business cycles, making regional banks’ liabilities volatile with market fluctuations. Second, to mitigate increased deposit instability, regional banks hold a higher proportion of marketable securities to manage liquidity. But the mark-to-market nature of security prices further puts potential instability on the asset side of banks’ balance sheets. Extremely, the sudden volatility in the financial markets could simultaneously cause deposit flights and choke off refinancing capability, causing a bank run.

Quantifying and hedging economic risk in disability income insurance portfolios

Abstract The association between economic variables and the frequency and duration of disability income insurance (DII) claims is well established. Across many jurisdictions, heightened levels of unemployment have been associated with both a higher incidence and a longer duration of DII claims. This motivated us to derive an asset portfolio for which the total asset value moves in line with the level of unemployment, thus, providing a natural match for the DII portfolio liabilities. To achieve this, we develop an economic tracking portfolio where the asset weights in the portfolio are chosen so that the portfolio value changes in a way that reflects, as closely as possible, the level of unemployment. To the best of our knowledge, this is the first paper applying economic tracking portfolios to hedge economic risk in DII. The methodology put forward to establish this asset-liability matching portfolio is illustrated using DII data from the UK between 2004 and 2016. The benefits of our approach for claims reserving in DII portfolios are illustrated using a simulation study.

Partnership Pattern in Bali Cattle Risk Management in Purification Cluster Area

Agricultural businesses, including beef cattle farming, are prone to risks and losses. Partnerships provide many benefits and reduce the risk of business losses through market assurance, added value, and an increased portfolio of productive assets. The Bali cattle cluster is a place of centralized development in cattle breeding to enhance high competitiveness. The study aims to build a partnership model for managing the risk of the Bali cattle business in the Bali cattle purification cluster. The research used a survey method with a descriptive qualitative approach. The results showed two partnership models in the research location: the partnership of the livestock company and the traditional partnership. Business cooperation is established in partnership with farmers by dividing profits among 50% farmers, 45% companies, and 5% farmer-livestock groups. As for traditional partnerships, farmers and owners get 50% of the profits each. So, the two business models of beef cattle farming have advantages and disadvantages. The partnership system has a good impact on farmers in managing their business risks because farmers feel helped by these two partnerships in the research location, especially for farmers who still have minimal business capital in the Bali cattle business. Future research will be maximized if it increases the number of respondents and the size of the research area to detect agricultural partnership patterns in rural areas.

Constructing Optimal Portfolios Using the Single Index Model and Markowitz Model: A Study on Cryptocurrencies

This study analyzes the formation of optimal portfolios on cryptocurrency assets using the single index model and the Harry Markowitz model. This study covers 79 cryptocurrencies with the largest market capitalization during the period June 2023–June 2024. We calculate the optimal portfolio using the single index model and Markowitz, and evaluate its performance using the Sharpe Ratio. The results show that the Harry Markowitz model produces better portfolio performance compared to the single index model. The Markowitz portfolio produces a positive Sharpe ratio (1.8496), a portfolio return rate of 7.678%, and lower risk (0.0415). Conversely, the single index model portfolio shows a negative Sharpe ratio (-2.0971), indicating lower returns than risk-free assets. In addition, the Markowitz model offers more efficient diversification than the single index model. However, in general, both the Single Index Model and the Markowitz Model have a significant effect on the formation of optimal portfolios, with the Sharpe Index proving to be a significant mediator in the relationship between the two models and the optimal portfolio. The R-squared value shows that the SIM variables, Markowitz Model, and Sharpe Index explain 48.4% of the variation in the optimal portfolio. This study recommends the use of the Harry Markowitz model for cryptocurrency investment because it can provide higher returns with more controlled risks. This study provides important insights for investors on the strategy of diversifying cryptocurrency asset portfolios.

Climate Change as a Business Risk: An Indonesian Banking Industry Perspective

Climate change has emerged as a new business risk that must be carefully evaluated, forecasted, mitigated and managed by all corporate organizations, including banks. Climate change has a profound effect on finance and business continuity, so all sectors, including the financial industry, must plan for it. In June 2022, the Basel Committee on Banking Supervision (BAS) announced general principles for banks around the world to manage and supervise climate change risks, entitled “Principles for the Effective Management and Supervision of Climate-Related Financial Risks”. Managing Climate-Related Financial Risks requires an acceptable management structure and information tools that can be used by banks to monitor, measure and manage asset portfolios affected by Climate-Related Financial Risks. In addition, this research also aims to assess the readiness of Indonesian banks in implementing an effective BCBS management framework for climate change-related financial risks. This research utilizes a qualitative research type. The research methodology used in this study is to use in-depth and semi-structured interviews to obtain narratives and subjective reflections from the participants. The main purpose of the interviews was to uncover significant meanings and structures inherent in their professional experiences. By using a sample method involving regulators and selecting banking executives who specialize in risk management. The results of this study provide an explanation of the perspective of Indonesian banks in dealing with business risks due to climate change, OJK's policies related to banking risk management due to climate change, OJK's response to the possibility of adopting an effective management framework for financial risks related to climate change developed by the BCBS in terms of, Bank Indonesia's Perspective on the Integration of Climate Risk into Macro Prudential Supervision, Impact on Financial Stability and Economic Resilience, and Policy Recommendations, and Banks' readiness to implement an effective financial risk management framework for financial risks related to climate change based on the risk management framework developed by the Basel Committee on Banking Supervision (BCBS).

Drawdown minimization in asset portfolio selection: MINLP models and efficient cross‐entropy algorithm

AbstractPortfolio management is an important research topic in finance and optimization. Drawdown as one of the measures in evaluating portfolios indicates the relative difference between the portfolio value in the current moment and its maximum value during a given time interval in the recent past. In this paper, first, the importance of this measure is discussed and then two mixed‐integer nonlinear programming (MINLP) models with the objectives of minimizing the expected drawdown and the maximum drawdown under real‐world constraints are presented. Due to the NP‐hardness of this problem, by utilizing the problem structure, an efficient cross‐entropy‐based algorithm is presented to solve it. An effective mechanism is suggested to calibrate the algorithm parameters. Computational results confirm the performance of the proposed algorithm from both solution quality and running time in comparison with MINLP solvers.

Investment Portfolio Allocation and Insurance Solvency: New Evidence from Insurance Groups in the Era of Solvency II

This study examines the effect of the investment portfolio structure on insurers’ solvency, as measured by the Solvency Capital Requirement ratio. An empirical sample of 88 EU-based insurance groups was analyzed to provide robust evidence of the portfolio’s impact on the Solvency Capital Requirement ratio from 2016 to 2022. Linear regression and supervised machine learning models, particularly extra trees regression, were used to predict the solvency ratios, with the latter outperforming the former. The investigation was supplemented with panel data analysis. Firm-specific factors, including, unit-linked and index-linked liabilities, firm size, investments in property, collective undertakings, bonds and equities, and the ratio of government bonds to corporate bonds and country-specific factors, such as life and non-life market concentration, domestic bond market development, private debt development, household spending, banking concentration, non-performing loans, and CO2 emissions, were found to have an important effect on insurers’ solvency ratios. The novelty of this research lies in the investigation of the connection of solvency ratios with variables that prior studies have not yet explored, such as portfolio asset allocation, the life and non-life insurance market concentration, and unit-linked and index-linked products, via the employment of a battery of traditional and machine enhanced methods. Furthermore, it identifies the relation of solvency ratios with bond market development and investments in collective undertakings. Finally, it addresses the substantial solvency risks posed by the high banking sector concentration to insurers under Solvency II.

Reinforcement Learning-Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision-Making

Abstract. This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.

The Bank of Mum & Dad – intergenerational transfers and first-time homeownership in Australia

Around 70 per cent of Australian households reside in owner occupied housing though ownership rates have begun to fall, especially for younger cohorts. Owner occupied housing generally represents the largest single asset in the household wealth portfolio and plays an important role in retirement planning and wealth accumulation. There is some anecdotal evidence that transfers from parents, often termed the ‘Bank of Mum and Dad’, are an increasingly important mechanism to facilitate entry into homeownership. In this paper we consider transfers in the form of bequests and inter vivos gifts from parents and examine the relationship between transfers, wealth inequality and entry into first-time homeownership. The empirical analysis indicates that bequests and inter vivos transfers hasten entry into homeownership, potentially alleviating some important liquidity constraints faced by households.

The Influence of Loan at Risk and Bank Spesific Variables on The Profitability of Commercial Banking in Indonesia Based on Core Capital: Period 2015 to 2022

This research aims to examine the influence of loan at risk (LAR) and other specific bank variables, namely size, leverage, and capital, on the profitability of commercial banks in Indonesia based on the classification of core capital set by banking authorities. The study utilizes panel data regression with a balanced fixed-effect model from a sample of 67 examined commercial banks, both in aggregate and based on the core capital classification of each bank, covering the research period from 2015 to 2022. The results indicate that the credit asset portfolio with LAR quality significantly affects the profitability of commercial banks negatively, both in terms of ROA and ROE, applying to the entire group of commercial banks studied. Meanwhile, other specific variables have varying degrees of influence and significance. Collectively, the independent variables in all research models have a significant impact on the profitability of all commercial banks in Indonesia. The findings of this study can contribute to the reference for bank management and other stakeholders in assessing the profitability performance of commercial banks, as well as expanding the literature in the field of banking.

A Study of the Impact of Crypto Assets on Portfolio Risk Management (2019–2022)

In this study, we examined the impact of crypto assets on the risk-return characteristics of portfolios consisting of traditional assets. First, we examined the optimal portfolio for maximising the Sharpe ratio and found that the Sharpe ratio improves when CMC Crypto 200 Index (CMC) are incorporated to a certain extent into a portfolio consisting of traditional stocks, bonds and commodity indices. In addition, we focused on individual assets comprising crypto assets instead of CMC and incorporated individual crypto assets into a portfolio of conventional traditional assets and found that the Sharpe ratio improved for the majority of individual crypto assets. This is an interesting result as it indicates that crypto assets may contribute to improving the risk-return characteristics of the portfolio. Second, we examined the optimal portfolio for minimising volatility and found that the percentage of crypto assets held was small. This may be due to the fact that the volatility of crypto assets is higher than that of other assets. In this sense, investing in cryptocurrencies is not suitable for investors who want to keep volatility low. JEL Codes: G10, G11, G12

Martingale Pricing and Single Index Models: Unified Approach with Esscher and Minimal Relative Entropy Measures

In this paper, we explore the connection between a single index model under the real-world probability measure and martingale pricing via minimal relative entropy or Esscher transform, within the context of a one-period market model, possibly incomplete, with multiple risky assets and a single risk-free asset. The minimal relative entropy martingale measure and the Esscher martingale measure coincide in such a market, provided they both exist. From their Radon–Nikodym derivative, we derive a portfolio of risky assets in a natural way, termed portfolio G. Our analysis shows that pricing using the Esscher or minimal relative entropy martingale measure is equivalent to a single index model (SIM) incorporating portfolio G. In the special case of elliptical returns, portfolio G coincides with the classical tangency portfolio. Furthermore, in the case of jointly normal returns, Esscher or minimal relative entropy martingale measure pricing is equivalent to CAPM pricing.

Capital Controls or Macroprudential Policies: Which is Better for Land Booms and Busts?

AbstractEmerging markets have experienced land booms and busts along with international capital inflows and outflows repeatedly. This study quantitatively examines the effectiveness of (i) macroprudential policies targeting land markets and (ii) capital controls targeting capital inflows and outflows. We analyze which policy better manages the coincidence between land booms (busts) and capital inflows (outflows). We build a small open economy NK-DSGE model in which banks choose their asset portfolio between physical capital and land subject to financial constraints. The quantitative results show that the superiority of the two policies depends on the type of shock impacting a small open economy. In the case of domestic land market shocks, macroprudential policies enhance welfare, whereas capital controls reduce welfare. Conversely, in the case of foreign interest rate shocks, the superiority of the two policies is reversed: capital controls enhance welfare, while macroprudential policies deteriorate welfare.

Bank Diversification Strategies Under Market Competition

This paper explores the impact of market competition on banks’ diversification strategies. Through a unique sample of Vietnamese commercial banks during 2007 to 2021, our most important finding is that the bank competition impact may depend on the specific type of diversification under analysis. Specifically, we document that banks are more likely to diversify their asset portfolios and income sources into non-lending activities and non-interest revenues amid high competition. In contrast, loan portfolios of banks facing more competition tend to be less diversified across different economic sectors. Besides, funding diversity in another vein may not react to changes in the banking market structure. Further empirical results reveal that the influence of market competition on bank business models is more pronounced at smaller and riskier banks, suggesting that a competitive market places greater pressure on more vulnerable banks and causes them to adopt a more aggressive diversification strategy. Based on these findings, critical policy implications include the development of frameworks to guide banks’ activity diversification strategies, monitoring sectoral concentration risks, and providing targeted support and guidance for specific groups of banks, particularly smaller and riskier institutions, within a competitive banking environment.

Can Chinese investors manage climate risk domestically and globally?

Climate risk is increasing and poses a risk for investors in China’s traditional stock market. In this paper, we construct a TVP-VAR-DY model and use benchmark regression and asset portfolio to explore the impact of climate risk on the Chinese stock market and whether assets with risk aversion attributes or green and low-carbon features can effectively manage climate risk. In particular, we compare China’s climate risk with global climate risk and explore transition and physical risks separately. The results show that climate risk does affect the Chinese stock market and that Chinese climate risk has a greater impact on the stock market than global climate risk. However, the good news is that the metal assets, as well as the equity-type and bond-type green assets, are effective in reducing cross-market spillovers under China’s climate risk, indicating that these assets can provide diversification benefits against climate-driven risks domestically. We also find that the metals and the equity-type assets could dampen the spillover effect against global transition risk, and the bond-type ones serve this function against global physical risk. The results further show that, for China’s stock market investors, adding green bonds to their portfolios could reduce the downside risk, regardless of climate risk type or origin. In addition, we can infer that investors are suggested to use traditional safe-haven assets and bond-type green ones to manage climate risk.

Multi-asset portfolio model optimization based on mean multifractal detrended cross correlation analysis

ABSTRACT In order to make the constructed investment portfolio model better adapt to the actual securities market, this paper incorporates the multifractal correlations into the portfolio model of multi-risk assets optimization. On the basis of using variable detrended covariance to measure multifractal correlations, the variable detrended covariance is embedded into the reward-risk criterion, the mean multifractal detrended cross correlation analysis portfolio (M-D) model of multi-risk assets is constructed, and the analytical solution of the M-D model of multi-risk assets is given. The empirical analysis shows that the M-D model not only can improve investment performance but also meet the return-risk criterion much more, reaching the goal of optimizing the multi-risk asset portfolio model.

Bitcoin's Effect on Selected Crypto Assets: Quantitative Evidence

The main objective of the study is to systematically investigate the effects of Bitcoin on specific crypto assets. In this context, a sample of 135 observations between 2020W01 and 2022W33 for seven crypto assets with the highest value in the financial markets, including Ethereum, Tether, USD Coin, BNB, XRP, and Cardano, has been included in the study sample. These crypto assets, along with Bitcoin, constitute almost 95% of the total crypto asset portfolio. This situation increases the importance of representing a broad universe in the study and contributes to the academic literature. To examine the effects of Bitcoin on the selected crypto assets, a Multivariate Dynamic VAR Model has been used. The model assumes causality among variables. Variables that do not meet this assumption have been excluded from the study sample. The results of the study demonstrate that Bitcoin exhibits significant and one-way effects on all cryptocurrencies. However, the impact of other crypto assets on Bitcoin has been negligible. In this context, it is emphasized that investment decisions obtained from two-way evaluations may be risky and misleading. In order to reduce investment risks for crypto assets to more acceptable levels, the use of comprehensive analysis methods is recommended.