Portfolio Loss Research Articles

Client restatement announcement, audit office human capital investment, and audit quality improvements

This paper examines audit offices’ human capital investment in response to client restatement announcements and the resulting effects on audit quality and audit office client base. We find that audit offices attempt to acquire human capital and talent by posting more audit-related job positions just after a client announces a restatement. The increase in job postings follows restatements with more negative announcement returns and restatements of annual financial statements, and is concentrated among more senior and experienced positions. Importantly, the increased job postings are not driven by replacement hiring due to individual auditor departures and reflect more auditors joining the office. These human capital investments reduce future client misstatements and losses in client portfolios, indicating improvements in actual and perceived audit quality. Audit offices are also more likely to replace audit managing partners and prevent the signing partners of the misstated financial statements from signing new audit reports after restatement announcements.

Contagion network, portfolio credit risk, and financial crisis

We develop a network-based nonlinear dynamical system model to study credit exposure risk at the portfolio level. This model captures the complex characteristics of contagion arising from the microstructural interdependencies among firms, especially looping effects. We find that when contagion features are not adequately modeled, portfolio credit risk is generally underestimated, but becomes overestimated during a crisis. This can partly explain the outbreak and subsequent intensification of a crisis such as the 2008 financial crisis. We also derive an expression for the portfolio loss of a regular network, which has implications for measurement and pricing of portfolio credit risk.

Monte Carlo Estimation of CoVaR

CoVaR is an important measure of financial systemic risk due to its ability to capture tail dependence between the losses of different portfolios and its capacity to predict financial crises. Estimating CoVaR is challenging because its definition involves a zero-probability event, which is unobservable in the data. The existing model-based methods address this issue by assuming simplified structural models, which introduce biases that are difficult to eliminate. In “Monte Carlo Estimation of CoVaR,” Huang, Lin, and Hong propose using Monte Carlo methods to estimate CoVaR, leveraging the modeling flexibility of Monte Carlo simulation. Specifically, they introduce a batching estimator applicable to a wide range of financial models and prove that its best rate of convergence is [Formula: see text], where n is the sample size. Under the widely used delta-gamma approximation model, they further introduce an importance sampling–inspired estimator and prove that its best rate of convergence can be improved to [Formula: see text].

Safe haven between European ESG and energy sector under Russian-Ukraine war: Role of sustainable investments for portfolio diversification

The shortage of oil and gas supply to Europe during the Russia-Ukraine conflict has triggered a price hike in the European energy sector. To mitigate the impact of higher energy prices on consumers, the EU introduced a price ceiling mechanism to curb gas prices from exceeding market norms. This interplay between supply shortages and price controls further heightened volatility in energy market prices. Consequently, our study aims to explore the efficacy of ESG indices as a safe haven against the highly volatile European oil and gas sectors to reduce portfolio losses during conflict. Using advanced analytical techniques such as cross-quantilogram to predict directional predictability, we find that bullish ESG markets are a safe haven against the bearish oil and gas markets in short, medium, and long-term investment horizons. Further, to identify the net receiver/transmitter of spillover between ESG indices and oil and gas sectors, our quantile time-frequency connectedness analysis shows ESG is a net receiver of spillover before the conflict and during wartime. Additionally, we explored the indicator of uncertainty that influences quantile spillover between ESG indices and European oil and gas sectors. We find that uncertainty's role in quantile spillover is significant and asymmetric, and it becomes more volatile during wartime. This feature tends to be amplified during short and long-term spillovers. Finally, the portfolio rebalance strategy suggests that a safe haven portfolio based on a one-month investment horizon yields positive returns during wartime. Hence, this study suggests investors include ESG indices as safe haven assets at times of crisis and for policymakers to understand the role of uncertainties in reducing price volatility and risk contagion among ESG and energy markets.

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.

Chinese Crude Oil Futures and Sectoral Stocks: Copula-Based Dependence Structure and Connectedness

Abstract China launched its first crude oil futures on 18th March 2018, with the primary objective of introducing its own hedging instrument in China. Our study responds to this objective by investigating the dependence structure based on time-variant and time-invariant copula, connectedness in various market conditions, and hedging effectiveness of oil futures with eleven GICS sectors in China using daily returns data from 19th June 2019 to 24th February 2024. Findings suggest that time-varying copula is a best fit for all the GICS sectors in China except for the Energy. Similarly, the results of time-varying (TVP-VAR) connectedness indicate that the oil futures are the net receiver of shocks in total, short (1–5 days), and long-time (5 to infinity) spillover. The oil futures can better offset portfolio losses during the down market than normal and up markets. Lastly, based on TVP-VAR, the hedging ratio, optimum portfolio weights, and hedge effectiveness are calculated. We find a positive hedge ratio across various pairs of assets, which reduces the assets’ volatility. Moreover, hedge ratios and optimum portfolio weights are non-constant over time. Our results significantly contribute to the scant literature on Chinese crude oil futures and have implications for investors and policymakers.

Asymptotics for credit portfolio losses due to defaults in a multi-sector model

Asymptotics for credit portfolio losses due to defaults in a multi-sector model

Stock market and the psychological health of investors

AbstractUtilizing a national individual‐level medical dataset and the home bias phenomenon in investment, our study shows a strong and robust link between declines in local stock returns and increased antidepressant consumption among investors. This effect intensifies in areas with higher per capita dividend income, suggesting a direct relationship between higher stock ownership and stronger responses. We confirm that portfolio losses, not local economic conditions, are responsible for increased antidepressant usage during market downturns. Using the frequency of psychotherapy sessions yields similar findings. Moreover, our study supports the loss aversion hypothesis as we find positive stock returns do not influence antidepressant usage.

Simulating Credit Loss Distributions: Empirical Versus the Vasicek Model

Because credit losses can be substantial, managing credit risk is a focus area of risk measurement and management. It is important for financial institutions to select credit risk models that accurately forecast losses. The Basel Committee on Banking Supervision (BCBS) chose the closed-form single risk factor Vasicek model for regulatory capital calculations. In this article, its forecast accuracy is compared with empirical loss distributions using simulated probabilities of default and losses given default. The effect of altering probabilities of default on asset correlations was analysed and how this affects credit portfolio loss distributions. The robustness of the Vasicek model against five different portfolios with unique compositions was explored: results highlight two key findings. Firstly, the Vasicek model is a good approximation of credit losses for a portfolio that does not contain dominating loans (it is, after all, based on the assumption of large-scale homogeneity). Secondly, the Vasicek model is a good approximation for expected loss (ELs) but lacks accuracy when determining extreme unexpected losses (ULs). Finally, credit capital requirements as a function of two variables are presented which reveals novel ways of viewing these values.

A probabilistic estimation model for seismic physical portfolio loss of a water supply pipeline system

Losses due to hazards are inevitable and numerical simulations for estimations are complex. This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alternative for numerical simulations. The common approach in other research shows average damage spots per mesh estimated statistically independent to one another. Spatially distributed lifeline systems, such as water supply pipelines, are interconnected, and seismic spatial variability affects the damages across the region; thus, spatial correlation of damage spots is an important factor in target areas for portfolio loss estimation. Generally, simulations are used to estimate possible losses; however, these assume each damage behaves independently and uncorrelated. This paper assumed that damages per mesh behave in a Poisson distribution to avoid over-dispersion and eliminate negative losses in estimations. The purpose of this study is to obtain a probabilistic portfolio loss model of an extensive water supply area. The proposed model was compared to the numerical simulation data with the correlated Poisson distribution. The application of the Normal To Anything (NORTA) obtained correlations for Poisson Distributions. The proposed probabilistic portfolio loss model, based on the generalized linear model and central limit theory, estimated the possible losses, such as the Probable Maximum Loss (PML, 90% non-exceedance) or Normal Expected Loss (NEL, 50 % non-exceedance). The proposed model can be used in other lifeline systems as well, though additional investigation is needed for confirmation. From the estimations, a seismic physical portfolio loss for the water supply system was presented. The portfolio was made to show possible outcomes for the system. The proposed method was tested and analyzed using an artificial field and a location-based scenario of a water supply pipeline system. This would aid in pre-disaster planning and would require only a few steps and time.

Efficient risk estimation via nested multilevel quasi-Monte Carlo simulation

We consider the problem of estimating the probability of a large loss from a financial portfolio, where the future loss is expressed as a conditional expectation. Since the conditional expectation is intractable in most cases, one may resort to nested simulation. To reduce the complexity of nested simulation, we present an improved multilevel Monte Carlo (MLMC) method by using quasi-Monte Carlo (QMC) to estimate the portfolio loss in each financial scenario generated via Monte Carlo. We prove that using QMC can accelerate the convergence rates in both the crude nested simulation and the multilevel nested simulation. Under certain conditions, the complexity of the proposed MLMC method can be reduced to O(ϵ−2(logϵ)2). On the other hand, we find that using QMC in MLMC encounters a high-kurtosis phenomenon due to the existence of indicator functions. To remedy this, we propose a smoothed method which uses logistic sigmoid functions to approximate indicator functions. Numerical results show that the optimal MLMC complexity O(ϵ−2) is almost attained even in moderate high dimensions.

Tail behavior of discounted portfolio loss under upper tail comonotonicity

Tail behavior of discounted portfolio loss under upper tail comonotonicity

Multi-period portfolio optimization: A parallel NSGA-III algorithm with real-world constraints

This study introduces an enhanced algorithm that integrates the parallel processing capabilities of PGAs with the multi-objective optimization strengths of NSGA-III, designed for multi-period optimization. We extend optimization objectives to T + 1 by minimizing risk over T periods and maximizing the terminal return, with a practical constraint on portfolio loss. It consistently outperforms the standard NSGA-III algorithm in both risk reduction and return optimization, especially when portfolios are adjusted quarterly. We also pinpoint optimal algorithmic parameters: a population size of 70 and 10 % migration rate. Overall, our research offers invaluable insights into real-world investment scenarios, serving both academic and industry interests.

Nested Simulation for Conditional Value-at-Risk with Discrete Losses

Nested simulation has been an active area of research in recent years, with an important application in portfolio risk measurement. While majority of the literature has been focusing on the continuous case where portfolio loss is assumed to follow a continuous distribution, monetary losses of a portfolio in practice are usually measured in discrete units, oftentimes due to the practical consideration of meaningful decimal places for a given level of precision in risk measurement. In this paper, we study a nested simulation procedure for estimating conditional Value-at-Risk (CVaR), a popular risk measure, in the case where monetary losses of the portfolio take discrete values. Tailored to the discrete nature of portfolio losses, we propose a rounded estimator and show that when the portfolio loss follows a sub-Gaussian distribution or has a sufficiently high-order moment, the mean squared error (MSE) of the resulting CVaR estimator decays to zero at a rate close to [Formula: see text], much faster than the rate of the CVaR estimator in the continuous case which is [Formula: see text], where [Formula: see text] denotes the sampling budget required by the nested simulation procedure. Performance of the proposed estimator is demonstrated using numerical examples.

The impact of hurricanes on a property portfolio: an empirical study based on loss data in Portugal

PurposeThe simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.Design/methodology/approachA complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.FindingsSeveral scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.Originality/valueA statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

Do Banks in Saudi Arabia Manage Earning Using Discretionary Components?

This study aimed to examine whether banks in Saudi Arabia used discretionary components for loan and investment portfolios in managing earnings as this study was conducted on all banks registered on Saudi Arabia Stock Market (TASI) for the period 2013-2022. The study also aimed to determine the impact of the discretionary component of the allocations for loan losses on both profitability metrics and market metrics. To achieve objectives of the study, two metrics were used as proxies for managing earnings. The first metric used both the discretionary components of the allocations for loan losses and the realized gains and losses of investment portfolio. The second metric used only the discretionary component of the allocations for loan losses of the credit portfolio. The study relied on the quantitative approach as the study used regression models and correlations to test the study’s hypotheses. The results of the first metric of earnings management indicated that the determinants of earnings management were financial leverage, total assets, net operating profit, GDP growth rate, while the capital adequacy ratio was not one of the determinants of earnings management. The results of the second metric of earnings management indicated that the discretionary component of the allocations for loan losses had positive impacts on return on assets, return on equity, earnings per share, share price and annual share returns. That is, investors realized that the allocations for loan losses contained a discretionary component that will be converted into future earnings and cash flows, and investors look at the discretionary component positively. According to the signal theory, Saudi banks used the allocations for loan losses to transmit positive signals about the levels of the profits in the future. The results of this study have significant implications on the decisions of investors, supervisory authorities, bank managers and external auditors.

Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation

Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (d) according to different metrics (i) to their respective variants that included stock return forecast information of d and stock return data of the days before d and (ii) to a GARCH model that included return prediction information of d and stock return data of the days before d. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces.

Integrating ESG risks into value-at-risk

This study addresses the regulatory request for sustainability-related risk integration into traditional financial risk measures. We propose a new risk metric that combines a traditional market risk measure expressed in terms of Value-at-Risk (VaR) and environmental, social, and governance (ESG) factors. The new metric, VaRESG, considers the orthogonality of the ESG criteria with fundamental variables, applying a perturbative approach and an entropy function of ESG factors. The pilot empirical application relies on a financial portfolio comprising approximately 3000 equities. The results show the predictive power of VaRESG to reduce unexpected losses (i.e., out-of-VaR). The results were confirmed especially under stress conditions, when the values of losses were higher (in terms of magnitude). To the best of our knowledge, this is the first attempt in the financial literature to effectively integrate ESG risks into the VaR measure to predict the expected losses of an equity portfolio. The measure of VaRESG can be useful to asset managers and institutional investors to reduce unexpected losses, and to supervisors interested in increasing the level of accuracy of VaR estimations.

Equity Investment Decisions of Operating Firms: Evidence from Property and Liability Insurers

During the 2007–2009 financial crisis, almost 10% of Property and Liability (P&L) insurers completely liquidated their equity portfolios, and more than half of them never resumed equity market investments. In contrast, those P&L insurers that continued investing in equities after the crisis, increased their portfolio allocation substantially. To understand these findings, we develop and estimate models that explain P&L insurers’ dynamic equity investment decisions, in terms of firm, group, and market characteristics over the period 2002–2018. We study three different approaches to equity investments, a pure investment strategy, internal capital market contributions, and an outsourcing option and find that the factors driving the decision to invest in equities differ from those that explain the extent of their equity investments. Moreover, we find that while equity portfolio losses drive the decision to temporarily cease investments in equities, the decision to permanently exit equity markets is driven by both equity market losses and underwriting losses. These findings shed some light on the factors driving the demand for equity investments by operating firms.

Stylized Model of Lévy Process in Risk Estimation

Risk management is a popular and important problem in academia and industry. From a small-scale system, such as city logistics, to a large-scale system, such as the supply chain of a global industrial or financial system, efficient risk management is required to prevent loss from uncertainty. In this paper, we assume that risk factors follow the Lévy process, and propose a stylized model, based on regression, that can estimate the risk of a complicated system under the framework of nest simulation. Specifically, portfolio risk estimation using the Lévy process is discussed as an example. The stylized model simplifies the risk factors artificially, and provides useful basis functions to fit the portfolio loss with little computational effort. Numerical experiments showed the good performance of the stylized model in estimating risk for the Variance Gamma process and the Normal Inverse Gaussian process, which are two examples of the Lévy process.