Smooth nonparametric Bernstein vine copulas

We consider the problem of accurately modelling the distribution of the market risk of a multivariate financial portfolio. We employ a multivariate GARCH model in which the dependence structure between the assets is modelled via a vine copula. We address the problem of how the parametric pair-copulas in a vine copula should be chosen by proposing to use nonparametric Bernstein copulas as bivariate pair-copulas. An extensive simulation study illustrates that our smooth nonparametric vine copula model is able to match the results of a competing parametric vine model calibrated via Akaike’s Information Criterion while at the same time significantly reducing model risk. Our empirical analysis of financial market data demonstrates that our proposed model yields Value-at-Risk forecasts that are significantly more accurate than those of a benchmark parametric model.     

ControlML: A domain-specific modeling language in support of assessing internal controls and the internal control system

In this paper, we refine and extend an earlier language design to introduce a domain-specific modeling language (DSML) for internal control modeling as an extension to an enterprise modeling method. The language is aimed at supporting the assessment of a firm's internal control system through the use of conceptual models of internal controls. In the paper, we report on the design of the modeling language, on its integration with the enterprise modeling method, present the language specification, and discuss language applications in the context of the assessment of an internal control system.     

Using modeling languages in managerial accounting: An example for pricing decisions

This paper presents a modeling approach useful for accounting applications. The capabilities of modeling languages, like IFPS, enable managers to make complex analysis with relative ease and efficiency by focusing attention on management goals. The variable over which management has some degree of control can be manipulated to determine sensitivity of variable changes on desired outcome. The ease of manipulation and goal-seeking capabilities available in modeling languages produce greater insight into the decision making process. The application demonstrated in this paper is cost-volume-profit analysis viewed from the pricing perspective.     

Structure and estimation of Lévy subordinated hierarchical Archimedean copulas (LSHAC): Theory and empirical tests

Lévy subordinated hierarchical Archimedean copulas (LSHAC) are flexible models in high dimensional modeling. However, there is limited literature discussing their applications, largely due to the challenges in estimating their structures and their parameters. In this paper, we propose a three-stage estimation procedure to determine the hierarchical structure and the parameters of a LSHAC. This is the first paper to empirically examine the modeling performances of LSHAC models using exchange traded funds. Simulation study demonstrates the reliability and robustness of the proposed estimation method in determining the optimal structure. Empirical analysis further shows that, compared to elliptical copulas, LSHACs have better fitting abilities as well as more accurate out-of-sample Value-at-Risk estimates with less parameters. In addition, from a financial risk management point of view, the LSHACs have the advantage of being very flexible in modeling the asymmetric tail dependence, providing more conservative estimations of the probabilities of extreme downward co-movements in the financial market.     

On A Surplus Process Under A Periodic Environment

Abstract

The problem of modeling claims occurring in periodic random environments is discussed in this paper. In the classical approach of risk theory, the occurrence of claims is modeled by counting processes that do not account for claims following a periodic pattern. The author discusses how the use of the classical approach to model a periodic portfolio might lead to the miscalculation of important risk indices, namely the associated ruin probability.

He presents a periodic model, in terms of nonhomogeneous Poisson processes, that has potential practical applications. The discussion is based on some properties of the modeled periodic intensities. Existing simulation techniques are adapted to this periodic model, which provides a practical way to evaluate ruin probabilities.     

Optimal commodity asset allocation with a coherent market risk modeling

This paper fills a fundamental gap in commodity price risk management and optimal portfolio selection literatures by contributing a thorough reflection on trading risk modeling with a dynamic asset allocation process and under the supposition of illiquid and adverse market settings. This paper analyzes, from a portfolio managers' perspective, the performance of liquidity adjusted risk modeling in obtaining efficient and coherent investable commodity portfolios under normal and adverse market conditions. As such, the author argues that liquidity risk associated with the uncertainty of liquidating multiple commodity assets over given holding periods is a key factor in formalizing and measuring overall trading risk and is thus an important component to model, particularly in the wake of the repercussions of the recent 2008 financial crisis. To this end, this article proposes a practical technique for the quantification of liquidity trading risk for large portfolios that consist of multiple commodity assets and whereby the holding periods are adjusted according to the specific needs of each trading portfolio. Specifically, the paper proposes a robust technique to commodity optimal portfolio selection, in a liquidity-adjusted value-at-risk (L-VaR) framework, and particularly from the perspective of large portfolios that have both long and short positions or portfolios that consist of merely pure long trading positions. Moreover, in this paper, the author develops a portfolio selection model and an optimization-algorithm which allocates commodity assets by minimizing the L-VaR subject to applying credible operational and financial constraints based on fundamental asset management considerations. The empirical optimization results indicate that this alternate L-VaR technique can be regarded as a robust portfolio management tool and can have many uses and applications in real-world asset management practices and predominantly for fund managers with large commodity portfolios.     

The Event Study Methodology Since 1969

This paper discusses the event study methodology, beginning with FFJR (1969), including hypothesis testing, the use of different benchmarks for the normal rate of return, the power of the methodology in different applications and the modeling of abnormal returns as coefficients in a (multivariate) regression framework. It also focuses on frequently encountered statistical problems in event studies and their solutions.     

SV mixture models with application to S&P 500 index returns

Understanding both the dynamics of volatility and the shape of the distribution of returns conditional on the volatility state is important for many financial applications. A simple single-factor stochastic volatility model appears to be sufficient to capture most of the dynamics. It is the shape of the conditional distribution that is the problem. This paper examines the idea of modeling this distribution as a discrete mixture of normals. The flexibility of this class of distributions provides a transparent look into the tails of the returns distribution. Model diagnostics suggest that the model, SV-mix, does a good job of capturing the salient features of the data. In a direct comparison against several affine-jump models, SV-mix is strongly preferred by Akaike and Schwarz information criteria.     

Applied Nonparametric Regression Techniques: Estimating Prepayments on Fixed-Rate Mortgage-Backed Securities

We assess nonparametric kernel-density regression as a technique for estimating mortgage loan prepayments—one of the key components in pricing highly volatile mortgage-backed securities and their derivatives. The highly nonlinear and so-called irrational behavior of the prepayment function lends itself well to an estimator that is free of both functional and distributional assumptions. The technique is shown to exhibit superior out-of-sample predictive ability compared to both proportional-hazards and proprietary-practitioner models. Moreover, the best kernel model provides this improved predictive power utilizing a more parsimonious specification in terms of both data and covariates. We conclude that the technique may prove useful in other financial modeling applications, such as default modeling, and other derivative pricing problems where highly nonlinear relationships and optionality exist.     

Computation of Multivariate Barrier Crossing Probability and its Applications in Credit Risk Models

Abstract

In this paper we consider computational methods of finding exit probabilities for a class of multivariate diffusion processes. Although there is an abundance of results for one-dimensional diffusion processes, for multivariate processes one has to rely on approximations or simulation methods. We adopt a Large Deviations approach to approximate barrier crossing probabilities of a multivariate Brownian Bridge. We use this approach in conjunction with simulation methods to develop an efficient method of obtaining barrier crossing probabilities of a multivariate Brownian motion. Using numerical examples, we demonstrate that our method works better than other existing methods. We mainly focus on a three-dimensional process, but our framework can be extended to higher dimensions. We present two applications of the proposed method in credit risk modeling. First, we show that we can efficiently estimate the default probabilities of several correlated credit risky entities. Second, we use this method to efficiently price a credit default swap (CDS) with several correlated reference entities. In a conventional approach one normally adopts an arbitrary copula to capture dependency among counterparties. The method we propose allows us to incorporate the instantaneous variance-covariance structure of the underlying process into the CDS prices.     

Efficient Stochastic Modeling for Large and Consolidated Insurance Business: Interest Rate Sampling Algorithms

Abstract

One of the challenges of stochastic asset/liability modeling for large insurance businesses is the run time. Using a complete stochastic asset/liability model to analyze a large block of business is often too time consuming to be practical. In practice, the compromises made are reducing the number of runs or grouping assets into asset categories. This paper focuses on the strategies that enable efficient stochastic modeling for large and consolidated insurance business blocks. Efficient stochastic modeling can be achieved by applying effective interest rate sampling algorithms that are presented in this paper. The algorithms were tested on a simplified asset/liability model ASEM (Chueh 1999) as well as a commercial asset/liability model using assets and liabilities of the Aetna Insurance Company of America (AICA), a subsidiary of Aetna Financial Services. Another methodology using the New York 7 scenarios is proposed and could become an enhancement to the Model Regulation on cash flow testing, thus requiring all companies to do stochastic cash flow testing in a uniform, nononerous manner.     

Copulas: A Personal View

Copula modeling has taken the world of finance and insurance, and well beyond, by storm. Why is this? In this article, I review the early start of this development, discuss some important current research, mainly from an applications point of view, and comment on potential future developments. An alternative title of the article would be “Demystifying the copula craze.” The article also contains what I would like to call the copula must‐reads .     

Measuring large comovements in financial markets

A general, copula-based framework for measuring the dependence among financial time series is presented. Particular emphasis is placed on multivariate conditional Spearman's rho (MCS), a new measure of multivariate conditional dependence that describes the association between large or extreme negative returns—so-called tail dependence. We demonstrate that MCS has a number of advantages over conventional measures of tail dependence, both in theory and in practical applications. In the analysis of univariate financial series, data are filtered to remove temporal dependence as a matter of routine. We show that standard filtering procedures may strongly influence the conclusions drawn concerning tail dependence. We give empirical applications to two large data sets of high-frequency asset returns. Our results have immediate implications for portfolio risk management, derivative pricing and portfolio selection. In this context we address portfolio tail diversification and tail hedging. Amongst other aspects, it is shown that the proposed modeling framework improves the estimation of portfolio risk measures such as the value at risk.     

The financial econometrics of price discovery and predictability

This article reviews recent econometric developments in the literature on price discovery and predictability. For both areas, we discuss traditional approaches to econometric modeling, limitations to these approaches, and recent developments designed to overcome them. We also discuss the state of the art and suggest future research. Three main conclusions are drawn. First, while many recent empirical applications in price discovery and price predictability are on the frontier of econometric methods, further developments are needed to increase relaxation of relevant assumptions and push the boundaries of applications. Second, future research in econometric modeling needs to combine/synthesize recent developments across multiple econometric issues, rather than proceeding in a piecemeal manner, for instance, by integrating developments in the time series literature into panel-based frameworks. Third, recent econometric literature is generating findings that challenge long-held beliefs about apparent empirical regularities in price discovery and price predictability, thus presenting opportunities to develop relevant theory.     

A generalized probability framework to model economic agents' decisions under uncertainty

The applications of techniques from statistical (and classical) mechanics to model interesting problems in economics and finance have produced valuable results. The principal movement which has steered this research direction is known under the name of ‘econophysics’. In this paper, we illustrate and advance some of the findings that have been obtained by applying the mathematical formalism of quantum mechanics to model human decision making under ‘uncertainty’ in behavioral economics and finance. Starting from Ellsberg's seminal article, decision making situations have been experimentally verified where the application of Kolmogorovian probability in the formulation of expected utility is problematic. Those probability measures which by necessity must situate themselves in Hilbert space (such as ‘quantum probability’) enable a faithful representation of experimental data. We thus provide an explanation for the effectiveness of the mathematical framework of quantum mechanics in the modeling of human decision making. We want to be explicit though that we are not claiming that decision making has microscopic quantum mechanical features.     

Development of a case‐based decision support system for process modeling in BPR

This paper presents a knowledge‐based methodology for business process reengineering that uses a case‐based reasoning paradigm to provide decision support to its users in the modeling of a current problem and a redesign of critical business processes. As a process modeling tool for representing the business process, the event process chain (EPC) modeling method is used in this paper. We developed a CAPMOSS (CAse‐based Process MOdeling Supporting System) to support our proposed methodology. To reengineer a new business process problem, CAPMOSS retrieves from its case base the case that is most similar to the current problem. CAPMOSS uses a retrieved case to guide the structuring of AS‐IS models and TO‐BE models of a target business process. Using the transformational knowledge of a retrieved case, CAPMOSS helps the user to transform an AS‐IS model into a TO‐BE model for the target process with ease and the purchasing process in a government R&D institute is explained as an application of this approach. Copyright © 2000 John Wiley & Sons, Ltd.     

Are correlations constant? Empirical and theoretical results on popular correlation models in finance

Multivariate GARCH models have been designed as an extension of their univariate counterparts. Such a view is appealing from a modeling perspective but imposes correlation dynamics that are similar to time-varying volatility. In this paper, we argue that correlations are quite different in nature. We demonstrate that the highly unstable and erratic behavior that is typically observed for the correlation among financial assets is to a large extent a statistical artifact. We provide evidence that spurious correlation dynamics occur in response to financial events that are sufficiently large to cause a structural break in the time-series of correlations. A measure for the autocovariance structure of conditional correlations allows us to formally demonstrate that the volatility and the persistence of daily correlations are not primarily driven by financial news but by the level of the underlying true correlation. Our results indicate that a rolling-window sample correlation is often a better choice for empirical applications in finance.     

Exploring the effects of leaders’ individualized consideration in extreme contexts

Abstract

Role clarity and trust in leadership are two crucial factors supporting soldiers in coping with uncertainty and imperfect knowledge. The present study examined the effects of leaders’ individualized consideration on both factors in a sample composed of 161 paratroopers from a parachute division of the NATO Airborne Forces. Conditional process modeling revealed that (1) leaders showing individualized consideration increase followers’ role clarity and, in parallel, develop a trusting relationship with them; (2) critical upward communication and perceived workload mediated and moderated these relationships, respectively. The implications of these findings for safety research and their practical applications are outlined.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

Credit risk modeling with affine processes

This article combines an orientation to credit risk modeling with an introduction to affine Markov processes, which are particularly useful for financial modeling. We emphasize corporate credit risk and the pricing of credit derivatives. Applications of affine processes that are mentioned include survival analysis, dynamic term-structure models, and option pricing with stochastic volatility and jumps. The default-risk applications include default correlation, particularly in first-to-default settings. The reader is assumed to have some background in financial modeling and stochastic calculus.