The Future of Financial Reporting: Insights from Research

Research offers insights that, if heeded and built upon, could improve financial reporting, thereby contributing to a more prosperous society. In particular, research suggests improvement could stem from greater use of fair value; a performance statement designed to reveal the information embedded in changes in fair value; better information to help investors assess the value of intangible assets; more specific information about risk and uncertainty; information that is unbiased, rather than conservative; acceptance of a degree of earnings management together with a determination of how much earnings management is acceptable; and a financial reporting package designed to convey information users need, which presumably is broader than today's financial statements.     

Moments of renewal shot-noise processes and their applications

In this paper, we study the family of renewal shot-noise processes. The Feynmann–Kac formula is obtained based on the piecewise deterministic Markov process theory and the martingale methodology. We then derive the Laplace transforms of the conditional moments and asymptotic moments of the processes. In general, by inverting the Laplace transforms, the asymptotic moments and the first conditional moments can be derived explicitly; however, other conditional moments may need to be estimated numerically. As an example, we develop a very efficient and general algorithm of Monte Carlo exact simulation for estimating the second conditional moments. The results can be then easily transformed to the counterparts of discounted aggregate claims for insurance applications, and we apply the first two conditional moments for the actuarial net premium calculation. Similarly, they can also be applied to credit risk and reliability modelling. Numerical examples with four distribution choices for interarrival times are provided to illustrate how the models can be implemented.     

Expected exponential utility maximization of insurers with a Linear Gaussian stochastic factor model

In this paper, we consider the problem of optimal investment by an insurer. The wealth of the insurer is described by a Cramér–Lundberg process. The insurer invests in a market consisting of a bank account and m risky assets. The mean returns and volatilities of the risky assets depend linearly on economic factors that are formulated as the solutions of linear stochastic differential equations. Moreover, the insurer preferences are exponential. With this setting, a Hamilton–Jacobi–Bellman equation that is derived via a dynamic programming approach has an explicit solution found by solving the matrix Riccati equation. Hence, the optimal strategy can be constructed explicitly. Finally, we present some numerical results related to the value function and the ruin probability using the optimal strategy.     

寿险公司的业务同质化与风险联动性

单家机构之间的业务趋同是否是金融业系统性风险的一个来源？近些年中国单家寿险公司的产品分布和地理分布均更加分散,但是公司之间的产品分布和地理分布均有“同质化”趋势,且公司之间在赔付风险、投资风险和破产风险上的联动性均提高。对此,本文分析了寿险公司业务同质化引起风险联动性的机理,探讨了几种可能的效应。基于公司配对样本,并从时间上和地理上引入工具变量处理业务同质度的内生性,发现:(1)当两家寿险公司的产品分布从完全差别到完全相同时,它们的投资风险联动性和破产风险联动性均将提高十几个百分点;(2)产品同质度对赔付风险联动性没有显著影响;(3)地理同质化对3种风险联动性均没有显著影响。     

ETF Premiums and Liquidity Segmentation

Exchange traded funds (ETFs) provide a means for investors to access assets indirectly that may be accessible at a high cost otherwise. I show that liquidity segmentation can explain the tendency for ETFs to trade at a premium to net asset value (NAV) as well as the life‐cycle pattern in premiums. ETFs with larger NAV tracking error standard deviations (TESDs) tend to trade at higher premiums and the liquidity benefits offered by foreign ETFs and fixed income ETFs are revealed to be the most valuable to investors. Further tests validate that TESD has the desirable properties of a liquidity segmentation measure.     

Sociology,risk and the environment: a material-semiotic approach

Abstract

Sociology has made significant contributions to the conceptualisation of risk and critique of technical risk analysis. It has, however, unintentionally reinforced the division of labour between the natural/technical and social sciences in risk analysis. This paper argues that the problem with conceptualisations of risk is not a misplaced emphasis on calculation. Rather, it is that we have not adequately dealt with ontological distinctions implicit in both sociological and technical work on risk between material or objective risks and our socially mediated understandings and interpretations of those risks. While acknowledging that risks are simultaneously social and technical, sociologists have not, in practice, provided the conceptual and methodological tools to apprehend risk in a less dualistic manner. This limits our ability both to analyse actors and processes outside the social domain and to explore the recursive relationships between risk calculus, social action and the material outcomes of risk. In response, this paper develops a material-semiotic conceptualisation of risk and provides an assessment of its relevance to more sociologically informed risk governance. It introduces the ideas of co-constitution, emergent entities and enactment as instruments for reconciling the material and social worlds in a sociological study of risk. It further illustrates the application of a material-semiotic approach using these concepts in the nuclear industry. In deconstructing social–material dualisms in the sociology of risk, this paper argues that a material-semiotic conceptualisation of risk enables both technical and social perspectives on risk not only to coexist but to collaborate, widening the scope for interdisciplinary research.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

金融行业对实体行业的尾部风险传导研究——基于实体企业金融化视角

本文基于我国金融资本超额回报率的事实,在市场套利分析框架下实证检验了实 体企业金融化是抑制还是加剧了尾部金融风险溢出。数值模拟结果表明：从杠杆率来看,实体 企业金融化对杠杆率具有“双刃剑”效应,然而,实证结果发现,从长期经济后果来看,实体 企业金融化却显著提高了杠杆率,基于Altman-Z值的风险分析进一步发现企业破产风险显著 上升,从而加剧了尾部金融风险溢出。文章的研究结论有利于全面观察实体企业金融化带来的 的实际效果,也揭示了金融行业对实体行业的风险传导过程中的一个风险源,对于实体经济和 金融领域关于企业金融化效应的研究具有参考价值。     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Bailout uncertainty in a microfounded general equilibrium model of the financial system

This paper develops a micro-founded general equilibrium model of the financial system composed of ultimate borrowers, ultimate lenders and financial intermediaries. The model is used to investigate the impact of uncertainty about the likelihood of governmental bailouts on leverage, interest rates, the volume of defaults and the real economy. The distinction between risk and uncertainty is implemented by applying the multiple priors framework to beliefs about the probability of bailout.Results of the analysis include: (i) An unanticipated increase in bailout uncertainty raises interest rates, the volume of defaults in both the real and financial sectors and may lead to a total drying up of credit markets. (ii) Lower exante bailout uncertainty is conducive to higher leverage, which in turn raises moral hazard and makes the economy more vulnerable to expost increases in bailout uncertainty. (iii) Bailout uncertainty affects the likelihood of bubbles, the amplitude of booms and busts as well as the banking and the credit spreads. (iv) Higher bailout uncertainty is associated with higher returns’ variability in diversified portfolios and higher systemic risks, (v) Pre-crisis expansionary monetary policy reinforces those effects by inducing higher aggregate leverage levels. (vi) The larger the change in bailout uncertainty and the change in aversion to this uncertainty, the stronger the pre-crisis buildup and the deeper the ensuing crisis.A central policy implication of the analysis is that the vaguest is bailout policy prior to a crisis, the lower is the magnitude of investments destroyed or missed due to errors in evaluating bailout and other intervention policies. On the other hand, the clearer is bailout policy upon the eruption of a crisis, the smaller the contraction of credit and the destruction of investment activity.