Realised higher moments: theory and practice

This paper examines the incorporation of higher moments in portfolio selection problems utilising high-frequency data. Our approach combines innovations from the realised volatility literature with a portfolio selection methodology utilising higher moments. We provide an empirical study of the measurement of higher moments from tick by tick data and implement the model for a selection of stocks from the DOW 30 over the time period 2005–2011. We demonstrate a novel estimator for moments and co-moments in the presence of microstructure noise.     

Assessing the role of option grants to CEOs: How important is heterogeneity?

This paper revisits the question of whether CEO compensation practices are in keeping with those justified by agency theory. We develop and analyze a new panel Tobit model, estimated by modern Bayesian methods, in which the heterogeneity of covariate effects across firms is modeled in a hierarchical way. We find that our specification of heterogeneity provides a significantly improved fit to the data. Our results show support for the hypothesis that companies increase option awards to their CEOs when agency problems become more pronounced. We also find that liquidity constraints matter in defining the cash–option mix of CEO compensation.     

Is newer better? Penn World Table Revisions and their impact on growth estimates

This paper sheds light on two problems in the Penn World Table (PWT) GDP estimates. First, we show that these estimates vary substantially across different versions of the PWT despite being derived from very similar underlying data and using almost identical methodologies; that the methodology deployed to estimate growth rates leads to systematic variability, which is greater: at higher data frequencies, for smaller countries, and the farther the estimate from the benchmark year. Moreover, this variability matters for the cross-country growth literature. While growth studies that use low frequency data remain robust to data revisions, studies that use annual data are less robust. Second, the PWT methodology leads to GDP estimates that are not valued at purchasing power parity (PPP) prices. This is surprising because the raison d’être of the PWT is to adjust national estimates of GDP by valuing output at common international (purchasing power parity [PPP]) prices so that the resulting PPP-adjusted estimates of GDP are comparable across countries. We propose an approach to address these two problems of variability and valuation.     

美国洪水保险计划的运营启示

在我国,洪水灾害是主要的自然灾害之一。然而,洪水保险制度却迟迟未能建立。本文搜集和分析有关美国洪水保险计划(NFIP)的最新文献,对以NFIP为代表的政府主导洪水保险运营模式进行了历史脉络的梳理,研究了其法制发展、运营现状、模式特色以及出现的问题,最后对洪水保险的政府运营模式进行了启发式的总结和建议。     

Inferring Trade Direction from Intraday Data

This paper evaluates alternative methods for classifying individual trades as market buy or market sell orders using intraday trade and quote data. We document two potential problems with quote-based methods of trade classification: quotes may be recorded ahead of trades that triggered them, and trades inside the spread are not readily classifiable. These problems are analyzed in the context of the interaction between exchange floor agents. We then propose and test relatively simple procedures for improving trade classifications.     

Endogenous problems in cross-sectional valuation models based on accounting information

The main purpose of this paper is to deal with the analysis of the scale effect in the value-relevance of accounting numbers. We examine the impacts of widely used deflators on the adjustment of scale effect. We find that most of the usual deflators employed in the literature generate endogeneity problems. In this paper we recommend the use of exogenous deflator such as the number of employees in market-based accounting research models. This alternative deflator produces, at least for the USA and Canada data, slightly better statistical results than other (endogenous) deflators such as the market value, the book value of equity, or the total assets.     

Biases in option prices : Evidence from the foreign currency option market

This paper examines a European call model of option pricing over a data set which does not suffer from the early exercise problems that have plagued earlier studies of call options on common stocks. We specifically examine a data set of American call prices on spot foreign exchange for which it is plausible to apply an adjusted version of the Garman-Kohlhagen (1983) and Grabbe (1983) European call option model. We make adjustments for interest rate risk and find that the model is nearly unbiased in the valuation of foreign currency options. We conclude that the Geske-Roll (1984) conjecture about dividend uncertainty creating biases in stock option prices holds analogously in the foreign currency option market. Interest rate differential risk (analogous to risky dividends) thus appears to be an important element in the valuation of foreign currency options.     

Pricing of vanilla and first-generation exotic options in the local stochastic volatility framework: survey and new results

Stochastic volatility (SV) and local stochastic volatility (LSV) processes can be used to model the evolution of various financial variables such as FX rates, stock prices and so on. Considerable efforts have been devoted to pricing derivatives written on underliers governed by such processes. Many issues remain, though, including the efficacy of the standard alternating direction implicit (ADI) numerical methods for solving SV and LSV pricing problems. In general, the amount of required computations for these methods is very substantial. In this paper, we address some of these issues and propose a viable alternative to the standard ADI methods based on Galerkin-Ritz ideas. We also discuss various approaches to solving the corresponding pricing problems in a semi-analytical fashion. We use the fact that in the zero correlation case some of the pricing problems can be solved analytically, and develop a closed-form series expansion in powers of correlation. We perform a thorough benchmarking of various numerical solutions by using analytical and semi-analytical solutions derived in the paper.     

Market Imperfections, Investment Flexibility, and Default Spreads

This paper develops a structural model that determines default spreads in a setting where the debt's collateral is endogenously determined by the borrower's investment choice, and a demand variable with permanent and temporary components. We also consider the possibility that the borrower cannot commit to taking the value‐maximizing investment choice, and may, in addition, be constrained in its ability to raise external capital. Based on a model calibrated to data on office buildings and commercial mortgages, we present numerical simulations that quantify the extent to which investment flexibility, incentive problems, and credit constraints affect default spreads.     

Derivative usage and firm value: The influence of agency costs and monitoring problems

Using derivative usage data on over 1746 firms headquartered in the U.S. during the 1991 through 2000 time period, we find that firms with greater agency and monitoring problems (i.e., firms that are less transparent, face greater agency costs, have weaker corporate governance, larger information asymmetry problems, and overall poorer monitoring) exhibit a negative association between Tobin's Q and derivative usage. The negative valuation effect is also economically significant with an impact of -8.4% on Tobin's Q from a one standard deviation change in the firm monitoring index. The results are robust to alternative specifications, time varying estimates, econometric procedures that correct for potential clustering of errors, endogeneity problems, and sample selection biases among other robustness checks discussed in the paper. We conclude that derivative usage has a negative impact on firm value in firms with greater agency and monitoring problems.     

Do foreign banks take more risk? Evidence from emerging economies

This paper addresses the impact of foreign ownership on the risk-taking behavior of banks. Using bank-level panel data of more than 1300 commercial banks in 32 emerging economies during 2000–2013, we find that foreign owned banks take on more risk than their domestic counterparts. We further examine several factors that may potentially contribute to foreign banks’ differentiated riskiness from four perspectives, namely, foreign banks’ informational disadvantages, agency problems, the contagious effect of parent banks’ financial conditions and the disparity between home and host markets. We find supportive evidence that these factors play a significant role in affecting foreign banks’ risk-taking.     

Robust estimation of historical volatility and correlations in risk management

Financial time series have two features which, in many cases, prevent the use of conventional estimators of volatilities and correlations: leptokurtotic distributions and contamination of data with outliers. Other techniques are required to achieve stable and accurate results. In this paper, we review robust estimators for volatilities and correlations and identify those best suited for use in risk management. The selection criteria were that the estimator should be stable to both fractionally small departures for all data points (fat tails), and to fractionally large departures for a small number of data points (outliers). Since risk management typically deals with thousands of time series at once, another major requirement was the independence of the approach of any manual correction or data pre-processing. We recommend using volatility t-estimators, for which we derived the estimation error formula for the case when the exact shape of the data distribution is unknown. A convenient robust estimator for correlations is Kendall's tau, whose drawback is that it does not guarantee the positivity of the correlation matrix. We chose to use geometric optimization that overcomes this problem by finding the closest correlation matrix to a given matrix in terms of the Hadamard norm. We propose the weights for the norm and demonstrate the efficiency of the algorithm on large-scale problems.     

Genetische Untersuchungen im Versicherungsbereich – Offene Fragen im Rahmen von § 18 GenDG

The paper refers to legal questions that arise in connection with the collection and use of genetic data in the context of private insurance. The content and meaning of § 18 GenDG is described. Then, § 18 GenDG is set in relation to notification requirements out of the VVG. The paper exemplarily emphasizes that, despite the codification of GenDG, specific legal problems arise. Accordingly, the paper provides solutions to overcome these problems. Particular problems refer to the role of genetic data of third parties and according relations to the family history, possibilities to communicate genetic data by the insured in case of medical indication, the ratio of predictive and diagnostic genetic testing and the range of the total limits of § 18 I 2 GenDG.     

A Dynamic Model of Optimal Capital Structure

This paper presents a continuous time model of a firm that candynamically adjust both its capital structure and its investmentchoices. In the model we endogenize the investment choice aswell as firm value, which are both determined by an exogenousprice process that describes the firm's product market. Withinthe context of this model we explore cross-sectional as wellas time-series variation in debt ratios. We pay particular attentionto interactions between financial distress costs and debtholder/equityholderagency problems and examine how the ability to dynamically adjustthe debt ratio affects the deviation of actual debt ratios fromtheir targets. Regressions estimated on simulated data generatedby our model are roughly consistent with actual regressionsestimated in the empirical literature.     

Financial and Demographic Risks of a Portfolio of Life Insurance Policies with Stochastic Interest Rates

Abstract

We refer to a recent paper by G. Parker (1997) in which the risk of a portfolio of life insurance policies (namely the risk related to the entire contractual life) is studied by separating the demographic component from the financial component. In our paper, after making a brief summary of Parker’s model, we propose two additional contributions: 1. We first give the problem a different formalization, thus allowing a portfolio risk analysis by management periods and a study of the risk due to the interactions among years;

2. We elaborate on a powerful and flexible algorithm for calculating the probability distribution of the sum of random variables that proves useful to solve not only the problems discussed in this paper concerning the risk analysis but also various other problems.

In the paper, we also show, for both contributions, some applications made under the same financial and demographic assumptions taken by Parker; we also compare our results with Parker’s results.     

公司特征、股权结构与股票流动性

股票流动性是金融市场微观结构的核心议题,其影响因素问题受到了人们的广泛关注。然而,以往的研究存在两个方面的重要缺陷:一是多数研究仅关注了股权结构对股票流动性的影响,而忽略了公司特征因素的作用;二是以往股权结构视角的研究由于存在研究设计上的问题,使得实证结论的可靠性不高。本文使用2003-2009年沪深两市仅发行了A股的上市公司为样本,采用高频交易数据构造买卖价差以衡量股票流动性,考察了公司特征、股权结构与股票流动性之间的关系,以及股权分置改革对这一关系的影响,得到了与以往研究不同的实证结论。针对这些结论,本文给出了符合我国制度背景的理论阐释。     

Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias

In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the “superior predictive ability” (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without potential data snooping bias. It is shown analytically and by simulations that the stepwise SPA test is more powerful than the stepwise Reality Check test of Romano and Wolf (2005, Econometrica). We then apply the proposed test to examine the predictive ability of technical trading rules based on the data of growth and emerging market indices and their exchange traded funds (ETFs). It is found that technical trading rules have significant predictive power for these markets, yet such evidence weakens after the ETFs are introduced.     

“The first mile problem”: Deriving an endogenous demand for auditing in blockchain-based business processes

In this paper, we model the business process within which blockchain applications operate in order to extract an endogenous demand for auditing in that environment. We begin by undertaking a case study of the pharmaceutical drug industry supply chain, exploring both the proposed blockchain initiatives in it initiated by the FDA and startup companies, and the problems in the supply chain that blockchain cannot address. Drawing on this analysis, we derive an endogenous demand for auditing to overcome what we label the “first mile problem” (FMP) with blockchains: ensuring that the data stored on the blockchain distributed ledger is isomorphic with the real life data that it purports to represent. The first mile problem arises only when the blockchain is used to store data about physical items, especially ones involving a service component, rather than being native digital, as is the case with the genesis blockchain application, bitcoin. We show that unless it is feasible to store a “digital twin” of the item (Roberts, 2017), there is a role for auditors, with the training in professional skepticism, to help alleviate the first mile problem. There is no guarantee, however, that this new demand for auditing will be met by the traditional financial statement auditors.     

SCENARIO GENERATION FOR OPERATIONAL RISK

Operational risk is an increasingly important area of risk management. Scenarios are an important modelling tool in operational risk management as alternative viable methods may not exist. This can be due to challenging modelling, data and implementation issues, and other methods fail to take into account expert information. The use of scenarios has been recommended by regulators; however, scenarios can be unreliable, unrealistic and fail to take into account quantitative data. These problems have also been identified by regulators such as Basel, and presently little literature exists on addressing the problem of generating scenarios for operational risk. In this paper we propose a method for generating operational risk scenarios. We employ the method of cluster analysis to generate scenarios that enable one to combine expert opinion scenarios with quantitative operational risk data. We show that this scenario generation method leads to significantly improved scenarios and significant advantages for operational risk applications. In particular for operational risk modelling, our method leads to resolving the key problem of combining two sources of information without eliminating the information content gained from expert opinions, tractable computational implementation for operational risk modelling, improved stress testing, what‐if analyses and the ability to apply our method to a wide range of quantitative operational risk data (including multivariate distributions). We conduct numerical experiments on our method to demonstrate and validate its performance and compare it against scenarios generated from statistical property matching for comparison. Copyright © 2013 John Wiley & Sons, Ltd.