Asset Portfolio Research Articles

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 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.

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.

China’s public long-term care insurance and risky asset allocation among elderly households

China’s elderly households are characterized by higher holdings of cash and cash equivalents and lower holdings of stocks and bonds in their financial portfolios. We utilize the public Long-term Care Insurance (LTCI) reform and data from the China Health and Retirement Longitudinal Study (CHARLS) to examine how LTCI coverage affects risky asset holdings among newly insured elderly households. Employing a difference-in-differences methodology, our findings reveal that LTCI significantly increases the share of risky assets in the financial portfolios of older families. The increased preference for risky assets may be a result of a weakening incentive for precautionary savings. Decomposing risky assets into bonds and stocks, we find that the increase in the share of risky assets following the LTCI pilot comes mainly from bond investments rather than stocks, which indicates that LTCI has a limited effect on risk asset holdings among the Chinese elderly. Our study contributes to understanding the economic impacts of China’s public LTCI by showing that LTCI may lead to changes in asset allocation strategies among elderly households.

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.

Monetary policy transmission under pandemic uncertainty: Effect on banks' risk and capital adjustments

AbstractThis paper investigates the effects of monetary policy on the simultaneous adjustments in asset portfolio risk and capital of banks amidst the uncertainty of the COVID‐19 pandemic, focusing on the 12 largest economies from 2018 Q1 to 2021 Q4. Results indicate that banks show lower portfolio risk and capital levels when the monetary policy stance is eased. However, amid heightened pandemic uncertainty, the risk‐reducing effect of monetary policy on banks amplifies, while bank capital levels remain unchanged. Heterogeneity analyses reveal that banks with higher levels of diversification and herding are more responsive to interest rates amid pandemic uncertainty, exhibiting lower risk exposure in their asset portfolios. Banks in countries adopting negative interest rate policies also tend to assume greater asset risk to accommodate the intended stimulus of monetary policies.

Sustainable development and investor confidence: The safe‐haven appeal of green‐bond issuing firms

AbstractThis study investigates the performance of a green‐linked portfolio as a safe haven and hedge against traditional assets during periods of market turmoil, such as the COVID‐19 pandemic and the Russia‐Ukraine war. Utilizing a dynamic conditional correlation (DCC)‐generalized autoregressive conditional heteroscedasticity (GARCH) approach, the study analyzes the portfolio's correlations with global asse like SENSEX 30, BSE ESG 100, Gold, S&P Green Bond Index, and US 10‐year Treasury bonds, demonstrating its stability. Our findings demonstrate that the green‐linked portfolio outperforms both individual stocks and major indices as well as traditional safe havens like gold and US Treasury bonds. The portfolio exhibits superior risk‐adjusted returns and lower volatility, particularly during crisis periods. The portfolio's strong positive correlation with the S&P Green Bond Index highlights its alignment with the broader green market. Interestingly, with a 95% hedge effectiveness, it emerged as a viable safe haven investment, surpassing gold in stability during market uncertainties. Further, forecast matrix results and VAR analysis confirm the portfolio's potential as a diversifier and safe haven asset, characterized by significant positive returns. Additionally, the SAP‐LAP framework provides clarity to stakeholders on safe‐haven options within green markets. Moreover, while green bond markets are more mature in developed economies, our results suggest that green‐linked investments in emerging markets, such as India, offer a promising avenue for investors seeking both financial returns and environmental impact. Overall, the study provides empirical evidence supporting the inclusion of green‐linked assets in diversified portfolios to enhance risk management and capture long‐term growth opportunities.

Research on the Development of Investment Portfolio Theory

This paper provides a comprehensive analysis of portfolio theory evolution and its practical applications, tracing its development since Harry Markowitz introduced Modern Portfolio Theory (MPT) in 1952. The urgency to enhance capital market efficiency, optimize asset portfolios, and effectively measure and manage risks has intensified due to the rapid transmission of risk across global financial markets. This study reviews historical advancements and recent achievements in portfolio theory and practice. Furthermore, it constructs a theoretical research framework to guide the future development of quantitative investment strategies, focusing on active portfolio management. This framework also delineates several prospective research avenues. The advent of artificial intelligence and big data has transformed portfolio theory into a multidisciplinary nexus involving mathematical statistics, machine learning, and behavioral finance. This integration is crucial for refining asset portfolio optimization and understanding the dynamics of risk spillover and its determinants. The findings and theoretical advancements discussed herein are vitally important for fostering the sustainable development of capital markets and ensuring economic and financial stability. This study not only revisits foundational theories but also sets the stage for pioneering future research in financial analysis and portfolio management.

An Empirical Research on the Performance of Asset Pricing Models in the Chinese A-Share Market

Asset pricing remains a cornerstone of financial field, providing a framework to understand the anticipated returns of asset portfolios and individual assets. Its widespread application spans academic discourse and practical financial methodologies. This study conducts an empirical analysis of the Chinese A-share Market, utilizing transaction data from the A-share market spanning from 1994 to 2024. It examines the applicability of the traditional asset pricing models within the Chinese stock market. Through classic regression analyses of the CAPM, the Fama and French three-factor model (FF3), the Fama and French five-factor model (FF5), this study verifies that the FF5 provides a superior explanation of stock expectations for the A-share market during this period compared to other traditional asset pricing models. The conclusions of this study indicate that, among the CAPM, FF3, and FF5 models, the number of parameters taken into account by the model and its capacity to explain asset returns are positively correlated.

Global Capital Allocation

We survey the literature on global capital allocation. We begin by reviewing the rise of cross-border investment, the shift toward portfolio investment, and the literature focusing on aggregate patterns in multilateral and bilateral positions. We then turn to the recent literature that uses micro data to document patterns in global capital allocations. We focus on the importance of the currency of denomination of assets in international portfolios and the role that tax havens and offshore financial centers play in intermediating global capital. We conclude with directions for future research in this area.

Environmental sustainability from the perspective of assets diversification: A comparative empirical analysis for developed and developing countries

Assets have an important role in sustainability discussions. The substitutability aspects of assets deal with weak and strong sustainability criteria. Weak sustainability enables substitutability between natural and man-made assets while strong sustainability requires the maintenance of natural resources and individual assets. In this respect, this study conducts a comparative empirical analysis of 126 developed and developing countries over the period 1995 to 2018 to investigate the significance of diverse assets such as natural capital, produced capital, human capital, and financial capital on environmental sustainability. Ecological footprint and biological capacity are used as detailed environmental sustainability indicators. The results based on the Fixed Effects Model and Generalized Method of Moments suggest that natural capital has a great potential for the environment and produced capital is environmentally unsustainable while human and financial capital have heterogeneous effects on the environment in developed and developing countries. The study also employs adjusted net saving (as an environmental sustainability indicator) and income group analysis for robustness checks. The findings of the study suggest that the protection of renewable natural capital, including its ability to regenerate is a core aspect of sustainability and the diversification of assets can reduce pressure on natural capital. Cooperative climate policies can enhance the benefits of investing in a diverse portfolio of assets and can help in achieving environmental sustainability.

Portfolio default losses driven by idiosyncratic risks

We consider a portfolio of general defaultable assets with low individual default risk and study the probability of the portfolio default loss exceeding an arbitrary threshold. The latent variables driving defaults are modeled by a mixture structure that combines common shock, systematic risk, and idiosyncratic risk factors. While common shocks and systematic risk have been found by many studies to contribute significantly to portfolio losses, the role of idiosyncratic risks is often found to be negligible. Such conclusions are usually established under the assumption that the portfolio size tends to infinity and idiosyncratic risk factors are not dominant. We study under-investigated scenarios where the portfolio size is fixed and the idiosyncratic risk factors are heavy-tailed, exploring two distinct scenarios: an independence scenario and an asymptotic dependence scenario. The former is standard in the literature, while the latter is motivated by recent studies that have found the inadequacy of relying solely on common factors to capture default clustering. This consideration also reflects the possibility that idiosyncratic reasons can trigger contagion among firms with liabilities to each other. In the independence scenario, even with heavy-tailed idiosyncratic risk factors, the probability of a substantial portfolio loss remains low unless a single asset carries a disproportionately large weight. Conversely, in the asymptotic dependence scenario, the primary drivers of increased exceedance probability are the dependent idiosyncratic risk factors.

The Effects of the Introduction of Volume-Based Liquidity Constraints in Portfolio Optimization with Alternative Investments

Recently, liquidity issues in financial markets and portfolio asset management have attracted much attention among investors and scholars, fuelling a stream of research devoted to exploring the role of liquidity in investment decisions. In this paper, we aim to investigate the effects of introducing liquidity in portfolio optimization problems. For this purpose, first we consider three volume-based liquidity measures proposed in the literature and we build a new one particularly suited to portfolio optimization. Secondly, we formulate an extended version of the Markowitz portfolio selection problem, named mean–variance–liquidity, wherein the goal is to minimize the portfolio variance subject to the usual constraint on the expected portfolio return and an additional constraint on the portfolio liquidity. Thirdly, we consider a sensitivity analysis, with the aim to assess the trade-offs between liquidity and return, on the one hand, and between liquidity and risk, on the other hand. In the second part of the paper, the portfolio optimization framework is applied to a dataset of US ETFs comprising both standard and alternative, often illiquid, investments. The analysis is carried out with all the liquidity measures considered, allowing us to shed light on the relationships among risk, return and liquidity. Finally, we study the effects of the introduction of a Bitcoin ETF, as an asset with an extremely high expected return and risk.

Extended Least Squares Making Evident Nonlinear Relationships between Variables: Portfolios of Financial Assets

Extended Least Squares Making Evident Nonlinear Relationships between Variables: Portfolios of Financial Assets

Lessons from the Collapse of Silicon Valley Bank

The Federal Reserve's consecutive interest rate hikes led to a decline in the prices of US Treasuries and mortgage-backed securities (MBS), which comprised a significant portion of Silicon Valley Bank (SVB)'s asset portfolio. As a result, SVB experienced substantial floating losses, exceeding its owner's equity, creating immense pressure on its assets and liabilities. The analysis also highlights the simple deposit and asset structures of SVB, with a high proportion of demand deposits and a significant allocation to bonds. The aggressive interest rate hikes by the Federal Reserve, coupled with financing difficulties for startups, accelerated deposit consumption and intensified debt pressure for SVB. Furthermore, the article reveals the low coverage rate of deposit insurance, particularly at SVB, where only 7% of deposits are insured. This adds to the vulnerability of SVB and its depositors. In conclusion, this article examines SVB's balance sheet and interest rate risk exposure, identifies shortcomings in the management supervision system, and provides three reflections on the financial event.

Cashflow-driven investment beyond expectations

ABSTRACT This paper presents a computationally tractable optimization model for cashflow-driven investment where the aim is to find asset portfolios whose future payouts cover given liability payments as well as possible. While current industry solutions are largely based on expected future cash flows, we use a stochastic optimization model that seeks portfolios that give the best possible match across time as well as scenarios. Cashflow matching across scenarios is controlled by risk aversion while the timing is driven by illiquidity of money markets. When illiquidity increases, the hedging strategy quickly shifts towards portfolios suggested by deterministic models, but significant uncertainty remains. The model can incorporate hundreds of quoted instruments in the construction of optimal hedging strategies. The hedging strategies are able to employ any statistical connections between the liabilities and publicly quoted assets. The model is solved with simple Monte Carlo approximations and off-the-shelf convex optimization software. Besides optimal hedging strategies, we find the least cost of hedging which provides a market-consistent valuation based on the current quotes and the liquidity factors as well as the views and risk preferences of the investor/regulator. The approach is illustrated by pricing and hedging defined benefit pension liabilities which depend on uncertain longevity developments and the consumer price index. The hedging strategies are constructed from 128 publicly quoted instruments including index-linked bonds and equities. We find that the optimized hedging strategies achieve lower risk at a lower cost than the strategies obtained by matching expected cashflows. The hedging-based liability valuations are robust with respect to model parameters and the additional risk reduction achieved by optimization does not add much to the overall hedging cost.