An additional analysis of estimation techniques for the degree of financial leverage

This study compares three different empirical proxies for the financial leverage component of a systematic risk‐composition model employed in prior financial research. We consider one static accounting measure and two elasticity‐based measures. We find that the traditional static accounting measure of financial leverage provides statistically different estimates of financial leverage when compared to estimates from elasticity‐based measures of the degree of financial leverage. The findings are important because the elasticity‐based models for the degree of financial leverage have clear theoretical links to market‐based models of systematic risk, while the static accounting measure of financial leverage does not. Practitioners and researchers should carefully consider why they are estimating financial leverage and choose the appropriate method for doing so given the goals and potential consequences for biased estimation.     

How to choose the return model for market risk? Getting towards a right magnitude of stressed VaR

Value at Risk (VaR) and stressed value at Risk (SVaR) or expected shortfall are important risk measures widely used in the financial services industry for risk management and market risk capital computation. Fundamental to any (S)VaR model is the choice of the return type model for each risk factor. Because the resulting SVaR numbers are highly sensitive to the chosen return type model it is important to make a prudent choice on the return type modelling. We propose to estimate the return type model from historic data without making an a priori model assumption on the return model. We explain the fundamentals of return type modelling and how it impacts the magnitude of SVaR. We further show how to obtain a global return type model from a set of similar return type models by using geometric calculus. Numerical simulations and illustrations are provided. In this paper, we consider interest rate data, but the proposed methodology is general and can be applied to any other asset class such as inflation, credit spread, equity or fx.     

An Extension of Spatial Dependence Models for Estimating Short-Term Temperature Portfolio Risk

Temperature risk is any adverse financial outcome caused by temperature outcomes. The Chicago Mercantile Exchange lists a series of financial products that link payments to temperature outcomes, and these products can help buyers manage temperature risk. Financial institutions can also hold a portfolio of these products as counterparty to the buyers facing temperature risk. Here we take an actuarial perspective to measuring the risk by modeling the daily temperatures directly. These models are then used to simulate distributions of future temperature outcomes. The model for daily temperature is a spatial ARMA-EGARCH statistical model that incorporates dependence in both time and space, in addition to modeling the volatility. Simulations from this model are used to build up distributions of temperature outcomes, and we demonstrate how actuarial risk measures of the portfolio can then be estimated from these distributions.     

The time-varying asymmetry of exchange rate returns: A stochastic volatility – stochastic skewness model

While the time-varying volatility of financial returns has been extensively modelled, most existing stochastic volatility models either assume a constant degree of return shock asymmetry or impose symmetric model innovations. However, accounting for time-varying asymmetry as a measure of crash risk is important for both investors and policy makers. This paper extends a standard stochastic volatility model to allow for time-varying skewness of the return innovations. We estimate the model by extensions of traditional Markov Chain Monte Carlo (MCMC) methods for stochastic volatility models. When applying this model to the returns of four major exchange rates, skewness is found to vary substantially over time. In addition, stochastic skewness can help to improve forecasts of risk measures. Finally, the results support a potential link between carry trading and crash risk.     

An Out-of-Sample Analysis of Investment Guarantees for Equity-Linked Products

Abstract

In this paper we analyze the risk underlying investment guarantees using 78 different econometric models: GARCH, regime-switching, mixtures, and combinations of these approaches. This extensive set of models is compared with returns observed during the financial crisis in an out-of-sample analysis, bringing a new perspective to the study of equity-linked insurance. We find that despite the very good fit of recent models, too few of them are capable of consistently generating low returns over long periods, which were in fact observed empirically during the financial crisis. Moreover, tail risk measures vary significantly across models, and this emphasizes the importance of model risk. Most insurance companies are now focusing on dynamically hedging their investment guarantees, and so we also investigate the robustness of the Black-Scholes delta hedging strategy. We find that hedging errors can be very large among the top fitting models, implying that model risk must be taken into consideration when hedging investment guarantees.     

Modeling and Management of Nonlinear Dependencies–Copulas in Dynamic Financial Analysis

We study the influence of nonlinear dependencies on a non-life insurer's risk and return profile. To achieve this, we integrate several copula models in a dynamic financial analysis framework and conduct numerical tests. We also test risk management strategies in response to adverse outcomes. Nonlinear dependencies have a crucial influence on the insurer's risk profile that can hardly be affected by the analyzed management strategies. We find large differences in risk assessment for the ruin probability and for the expected policyholder deficit. This has important implications for insurers, regulators, and rating agencies that use these measures as a foundation for internal risk models, capital standards, and ratings.     

Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece

The purpose of this paper is to test the extent to which client (corporate) performance measures can be used to enhance the ability to discriminate between the choice of a qualified or unqualified (clean) audit report. Audit firms face the risk of losing the client if they issue a qualification. On the other hand, failing to qualify exposes the auditor to potential lawsuits and loss of reputation. We examined the financial statements, auditors' opinions, and financial statements notes for companies in Greece that received a qualified audit report and for those that received an unqualified audit report. We modeled the auditor's qualification using a multicriteria decision aid classification method (UTADIS—UTilités Additives Discriminates) and compared it with other multivariate statistical techniques such as discriminant and logit analysis. The qualification decision is explained by financial ratios and by nonfinancial information such as the client litigation. The developed models are accurate in classifying the total sample correctly with rates of almost 80%.     

Management of Portfolio Depletion Risk through Optimal Life Cycle Asset Allocation

Members of defined contribution (DC) pension plans must take on additional responsibilities for their investments, compared to participants in defined benefit (DB) pension plans. The transition from DB to DC plans means that more employees are faced with these responsibilities. We explore the extent to which DC plan members can follow financial strategies that have a high chance of resulting in a retirement scenario that is fairly close to that provided by DB plans. Retirees in DC plans typically must fund spending from accumulated savings. This leads to the risk of depleting these savings, that is, portfolio depletion risk. We analyze the management of this risk through life cycle optimal dynamic asset allocation, including the accumulation and decumulation phases. We pose the asset allocation strategy as an optimal stochastic control problem. Several objective functions are tested and compared. We focus on the risk of portfolio depletion at the terminal date, using such measures as conditional value at risk (CVAR) and probability of ruin. A secondary consideration is the median terminal portfolio value. The control problem is solved using a Hamilton-Jacobi-Bellman formulation, based on a parametric model of the financial market. Monte Carlo simulations that use the optimal controls are presented to evaluate the performance metrics. These simulations are based on both the parametric model and bootstrap resampling of 91 years of historical data. The resampling tests suggest that target-based approaches that seek to establish a safety margin of wealth at the end of the decumulation period appear to be superior to strategies that directly attempt to minimize risk measures such as the probability of portfolio depletion or CVAR. The target-based approaches result in a reasonably close approximation to the retirement spending available in a DB plan. There is a small risk of depleting the retiree’s funds, but there is also a good chance of accumulating a buffer that can be used to manage unplanned longevity risk or left as a bequest.     

Futures prices as risk-adjusted forecasts of monetary policy

Many researchers have used federal funds futures rates as measures of financial markets’ expectations of future monetary policy. However, to the extent that federal funds futures reflect risk premia, these measures require some adjustment. In this paper, we document that excess returns on federal funds futures have been positive on average and strongly countercyclical. In particular, excess returns are surprisingly well predicted by macroeconomic indicators such as employment growth and financial business-cycle indicators such as Treasury yield spreads and corporate bond spreads. Excess returns on eurodollar futures display similar patterns. We document that simply ignoring these risk premia significantly biases forecasts of the future path of monetary policy. We also show that risk premia matter for some futures-based measures of monetary policy shocks used in the literature.     

A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an up-to-date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.     

Ex ante CEO severance pay and risk-taking in the financial services sector

We examine 533 CEO severance contracts for financial services firms from 1997 to 2007 and find that ex ante severance pay is positively associated with risk-taking after controlling for the incentive effects provided by equity-based compensation. We report a positive causal relation between the amount of severance pay and risk-taking using popular market-based risk measures as well as the distance-to-default and the Z-score. We also find that severance pay encourages excessive risk-taking using metrics such as tail risk and asset quality. Our results are consistent with the risk-shifting argument and provide support for recent reforms on severance pay in the financial sector.     

A comprehensive approach to measuring the relation between systemic risk exposure and sovereign debt

Using an integrated model to control for simultaneity, as well as new risk measurement techniques such as Adapted Exposure CoVaR and Marginal Expected Shortfall (MES), we show that the aggregate systemic risk exposure of financial institutions is positively related to sovereign debt yields in European countries in an episodic manner, varying positively with the intensity of the financial crisis facing a particular nation. We find evidence of a simultaneous relation between systemic risk exposure and sovereign debt yields. This suggests that models of sovereign debt yields should also include the systemic risk of a country's financial system in order to avoid potentially important mis-specification errors. We find evidence that systemic risk of a country's financial institutions and the risk of sovereign governments are inter-related and shocks to these domestic linkages are stronger and longer lasting than international risk spillovers. Thus, the channel in which domestic sovereign debt yields can be affected by another nation's sovereign debt is mostly an indirect one in that shocks to a foreign country's government finances are transmitted to that country's financial system which, in turn, can spill over to the domestic financial system and, ultimately, have a destabilizing effect on the domestic sovereign debt market.     

Early warning systems using dynamic factor models: An application to Asian economies

This study develops an early warning system for financial crises with a focus on small open economies. We contribute to the literature by developing macro-financial dynamic factor models that extract useful information from a rich but unbalanced mixed frequency data set that includes a range of global and domestic economic and financial indicators. The framework is applied to several Asian countries—Thailand, South Korea, Singapore, Malaysia, the Philippines and Indonesia. Logit regression models that use the extracted factors and other leading indicators have significant power in predicting systemic events. In-sample and out-of-sample test results indicate that the extracted factors help to improve the predictive power over a model that uses only sufficiently long history indicators. Importantly, models that include the dynamic factors yield consistently better out-of-sample crisis prediction results for key performance measures such as a usefulness index, the noise to signal ratio, and AUROC.     

VaR and expected shortfall: a non-normal regime switching framework

We have developed a regime switching framework to compute the Value at Risk and Expected Shortfall measures. Although Value at Risk as a risk measure has been criticized by some researchers for lack of subadditivity, it is still a central tool in banking regulations and internal risk management in the finance industry. In contrast, Expected Shortfall is coherent and convex, so it is a better measure of risk than Value at Risk. Expected Shortfall is widely used in the insurance industry and has the potential to replace Value at Risk as a standard risk measure in the near future. We have proposed regime switching models to measure value at risk and expected shortfall for a single financial asset as well as financial portfolios. Our models capture the volatility clustering phenomenon and variance-independent variation in the higher moments by assuming the returns follow Student-t distributions.     

Developing a Trading Rule from the FTSE-100 Stock Index Futures Contract: Evidence in Support of the EMH

An extensive literature documents the predictability of both short and long horizon returns, over a wide range of sample periods, frequencies and markets. This predictability may represent weak form inefficiency, or it may be caused by a failure to account for a time-variation in risk. We develop statistically reliable ex ante models of the returns on the FTSE-100 stock index futures contract and test a simple trading rule based on the out-of-sample predictions from these models. We interpret the failure of our ex ante model to produce abnormal returns for a risk neutral investor as evidence in favour of the EMH. Our trading rule results clearly suggest that we should be careful in interpreting such ex ante models as evidence of financial market inefficiency.     

Market Risk and Model Risk for a Financial Institution Writing Options

Derivatives valuation and risk management involve heavy use of quantitative models. To develop a quantitative assessment of model risk as it affects the basic option writing strategy that might be followed by a financial institution, we conduct an empirical simulation, with and without hedging, using data from 1976 to 1996. Results indicate that imperfect models and inaccurate volatility forecasts create sizable risk exposure for option writers. We consider to what extent the damage due to model risk can be limited by pricing options using a higher volatility than the best estimate from historical data.     

The Determinants of REIT Institutional Ownership: Tests of the CAPM

This study examines the determinants of institutional investment demand for REIT common stock. We estimate the demand function for financial institutions using the mean return and CAPM risk measures (beta and standard error) for REIT stocks. The objective is to determine whether institutional investment decisions are influenced by CAPM model attributes. In addition, we examine the predicatability of REIT institutional ownership based on the factors in our model. We employ conventional OLS forecasting techniques, as well as two neural network models in order to deal with possible nonlinearities in the relationships.     

Double Entry Bookkeeping and the Distributional Properties of a Firm's Financial Ratios

Our purpose here is to assess whether the innate properties of the double entry bookkeeping system are such that financial ratios, calculated from the balance sheet summary measures implied by it, will be generated by distributional forms with non‐convergent moments. Our analysis begins with a brief summary of some important analytical properties of the debt and equity components of the double entry bookkeeping system. We then use these to determine the time series and distributional properties of the debt to equity ratio itself. Our analysis shows that even when the evolution of balance sheet summary measures like debt and equity can be described by 'well behaved' distributional processes, there is a distinct possibility that ratios derived from them will evolve in terms of distributional forms with non‐convergent moments. We argue that this has serious implications for parameter estimation as well as the integrity of the regression and/or discriminant procedures which underscore bankruptcy and financial distress prediction models based on financial ratios derived from the double entry bookkeeping system.     

Performance ratio-based coherent risk measure and its application

Utilizing a specific acceptance set, we propose in this paper a general method to construct coherent risk measures called the generalized shortfall risk measure. Besides some existing coherent risk measures, several new types of coherent risk measures can be generated. We investigate the generalized shortfall risk measure’s desirable properties such as consistency with second-order stochastic dominance. By combining the performance evaluation with the risk control, we study in particular the performance ratio-based coherent risk (PRCR) measures, which is a sub-class of generalized shortfall risk measures. The PRCR measures are tractable and have a suitable financial interpretation. Based on the PRCR measure, we establish a portfolio selection model with transaction costs. Empirical results show that the optimal portfolio obtained under the PRCR measure performs much better than the corresponding optimal portfolio obtained under the higher moment coherent risk measure.     

我国金融机构尾部风险影响因素的非线性研究——来自面板平滑转换回归模型的新证据

“十三五”期间,我国防范化解金融风险攻坚战取得决定性成就,而在“十四五”规划开局之际,我国的金融风险形势面临新的挑战,防范风险仍是金融业的永恒主题。在此背景下,本文采用相对重要性分析技术方法,考察机构规模以及相关基本面因素对我国上市金融机构尾部风险的贡献程度。接着,本文结合边际效应分析技术考察机构规模对风险的异质性效应,深入分析“太大而不能倒”假说在中国的适用性。在此基础上,进一步运用前沿的面板平滑转换估计模型,研究机构规模与尾部风险的非线性关系,并分析基本面因素对该异质性效应的影响力度。研究结果表明,我国上市银行等金融机构规模的增加能够有效缓释我国金融系统的尾部风险,但该影响效应将随着特许权价值、资产质量、杠杆水平、成本水平、收入结构、贷款结构等基本面指标的变化而出现显著的非线性转变。在此基础上,对强化我国金融系统中的风险防控薄弱环节、提高金融机构的风险吸收能力提出建议,以期为我国深化金融业改革开放、推动高质量发展提供理论分析与实证检验的参考依据。