Polynomial diffusions and applications in finance

This paper provides the mathematical foundation for polynomial diffusions. They play an important role in a growing range of applications in finance, including financial market models for interest rates, credit risk, stochastic volatility, commodities and electricity. Uniqueness of polynomial diffusions is established via moment determinacy in combination with pathwise uniqueness. Existence boils down to a stochastic invariance problem that we solve for semialgebraic state spaces. Examples include the unit ball, the product of the unit cube and nonnegative orthant, and the unit simplex.     

Regime switching affine processes with applications to finance

We introduce the notion of a regime switching affine process. Informally this is a Markov process that behaves conditionally on each regime as an affine process with specific parameters. To facilitate our analysis, specific restrictions are imposed on these parameters. The regime switches are driven by a Markov chain. We prove that the joint process of the Markov chain and the conditionally affine part is a process with an affine structure on an enlarged state space, conditionally on the starting state of the Markov chain. Like for affine processes, the characteristic function can be expressed in a set of ordinary differential equations that can sometimes be solved analytically. This result unifies several semi-analytical solutions found in the literature for pricing derivatives of specific regime switching processes on smaller state spaces. It also provides a unifying theory that allows us to introduce regime switching to the pricing of many derivatives within the broad class of affine processes. Examples include European options and term structure derivatives with stochastic volatility and default. Essentially, whenever there is a pricing solution based on an affine process, we can extend this to a regime switching affine process without sacrificing the analytical tractability of the affine process.     

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.     

Additive subordination and its applications in finance

This paper studies additive subordination, which we show is a useful technique for constructing time-inhomogeneous Markov processes with analytical tractability. This technique is a natural generalization of Bochner’s subordination that has proved to be extremely useful in financial modeling. Probabilistically, Bochner’s subordination corresponds to a stochastic time change with respect to an independent Lévy subordinator, while in additive subordination, the Lévy subordinator is replaced by an additive one. We generalize the classical Phillips theorem for Bochner’s subordination to the additive subordination case, based on which we provide Markov and semimartingale characterizations for a rich class of jump-diffusions and pure jump processes obtained from diffusions through additive subordination, and obtain spectral decomposition for them. To illustrate the usefulness of additive subordination, we develop an analytically tractable cross-commodity model for spread option valuation that is able to calibrate the implied volatility surface of each commodity. Moreover, our model can generate implied correlation patterns that are consistent with market observations and economic intuitions.     

Quasi-Monte Carlo methods with applications in finance

We review the basic principles of quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variance-reduction techniques, the integration error and variance bounds obtained in terms of QMC point set discrepancy and variation of the integrand, and the main classes of point set constructions: lattice rules, digital nets, and permutations in different bases. QMC methods are designed to estimate s-dimensional integrals, for moderate or large (perhaps infinite) values of s. In principle, any stochastic simulation whose purpose is to estimate an integral fits this framework, but the methods work better for certain types of integrals than others (e.g., if the integrand can be well approximated by a sum of low-dimensional smooth functions). Such QMC-friendly integrals are encountered frequently in computational finance and risk analysis. We summarize the theory, give examples, and provide computational results that illustrate the efficiency improvement achieved. This article is targeted mainly for those who already know Monte Carlo methods and their application in finance, and want an update of the state of the art on quasi-Monte Carlo methods.      

Machine learning methods in finance: Recent applications and prospects

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.     

Smooth monotone covariance for elliptical distributions and applications in finance

Sample covariance is known to be a poor estimate when the data are scarce compared with the dimension. To reduce the estimation error, various structures are usually imposed on the covariance such as low-rank plus diagonal (factor models), banded models and sparse inverse covariances. We investigate a different non-parametric regularization method which assumes that the covariance is monotone and smooth. We study the smooth monotone covariance by analysing its performance in reducing various statistical distances and improving optimal portfolio selection. We also extend its use in non-Gaussian cases by incorporating various robust covariance estimates for elliptical distributions. Finally, we provide two empirical examples using Eurodollar futures and corporate bonds where the smooth monotone covariance improves the out-of-sample covariance prediction and portfolio optimization.     

Bayesian analysis of the factor model with finance applications

The factor analysis model has been widely applied to study finance problems. The purpose of this paper is to introduce a Bayesian approach for analysing the factor analysis model. The advantages of the proposed Bayesian approach over the classical maximum likelihood rest on its capability to incorporate additional prior information, to determine the number of factors in an objective manner, and to produce parameter and factor score estimates with good statistical properties. Based on recently developed tools in statistical computing, such as the Gibbs sampler and path sampling, methods for obtaining the Bayesian estimates of the parameters and factor scores, and a procedure for computing the Bayes factor for selecting the appropriate number of factors in the model, are developed. The proposed new methodologies are applied to analyse a data set taken from the Hong Kong stock security market. It is found that a three-factor model with a generic market factor can be used to describe the systematic components of asset returns.     

Fintech and access to finance

This article surveys research on the effects of digitalization on access to finance. We focus the review on access through fintech. We review the growth of three main fintech technologies, fintech lending (incl. peer-to-peer lending), crowdfunding and initial coin offerings. We discuss existing evidence on how fintech affects access to finance for firms and investors and consider the regulatory challenges it poses. We incorporate the papers in this special issue, underlining their significant contributions to our understanding of the digitalization of finance and its effects. Finally, we discuss the challenges of research in the digital finance area and propose some new avenues for future research.     

On an automatic and optimal importance sampling approach with applications in finance

Calculating high-dimensional integrals efficiently is essential and challenging in many scientific disciplines, such as pricing financial derivatives. This paper proposes an exponentially tilted importance sampling based on the criterion of minimizing the variance of the importance sampling estimators, and its contribution is threefold: (1) A theoretical foundation to guarantee the existence, uniqueness, and characterization of the optimal tilting parameter is built. (2) The optimal tilting parameter can be searched via an automatic Newton’s method. (3) Simplified yet competitive tilting formulas are further proposed to reduce heavy computational cost and numerical instability in high-dimensional cases. Numerical examples in pricing path-dependent derivatives and basket default swaps are provided.     

Meta-analysis in finance research: Opportunities,challenges, and contemporary applications

The number of empirical research studies in finance exhibits a strong upward trajectory, which often produces large differences in empirical results and impedes the drawing of consistent conclusions in relation to the phenomenon under examination. This creates demand for methods like meta-analysis that objectively consolidate and evaluate the empricial literature in a research field. Meta-analysis is a group of statistical methods to aggregate prior empirical studies, to discover and explain consistencies as well as inconsistencies within reported results, and to detect and filter out distorting effects from publication selection or model misspecification. While meta-analysis is a standard tool for research synthesis and evidence-based decisions in many related research disciplines, such as management, marketing, or economics, it has been rarely applied in finance. The goal of this article is to provide a comprehensive overview and discussion of the opportunities of meta-analytical research in finance, to present recent applications of meta-analysis in finance, as well as to discuss related challenges and limitations. Thereby, we aim at increasing the awareness and acceptance of meta-analysis and stimulating its future application in the finance field.     

Positivism in finance and its implication for the diversification finance research: Diversifying finance research: From financialization to sustainability

This paper is a complementary comment to the article recently published in IRFA by Thomas Lagoarde-Segot on the necessity of diversification of modelling in finance. In his claim, the author explained that financial concepts used by the mainstream are not neutral because they refer to a particular ethical judgement mainly focused on the shareholders' interest. In this comment, I explain that this ethical judgement historically results from the role playing by the Gaussian distribution in finance: while this statistical framework gave the first scientific foundations to finance in the 1960s, its symmetrical configuration implies that negative changes occur with the same probability than positive ones. In this context, all potential intervention (regulation) could only interfere (disturb) this “ethically fair situation” within the only perturbing element is the shareholder whose behaviours are likely to influence the market. After having explained that this reasoning is based on an a priori statement about observational facts (in opposition with positivism), I present this situation as an opportunity for current researchers in finance to clarify their implicit assumptions; which would open the door to a diversification of modelling in finance as Lagoarde-Segot promoted it in his IRFA article.     

如何解决金融争议

随着我国金融业的逐步放开,外资金融机构纷纷抢滩中国,伴随而来的是金融纠纷的数量和种类日益增多,金融交易主体对争议解决服务的要求呈现多样化。面对这些问题,作为法院诉讼外的一种重要争议解决方式——仲裁,其权威性和公正性日益受到市场主体的认可。中国仲裁界的知名机构——中国国际经济贸易仲裁委员会从今年5月8日起实施《金融争议仲裁规则》,那么,这一规则是在什么背景下产生的?有怎样的优势?包括哪些确定受理的争议范围?本刊记者艾亚就此采访了中国国际经济贸易仲裁委员会副主任、秘书长王生长。     

Control and legitimation in government accountability processes: the private finance initiative in the UK

This paper explores a number of dimensions of the accountability processes of governments. Accountability is associated with giving reasons for conduct for responsibilities or authority granted. A key argument of this paper is that governments make themselves accountable but only in a political, rather than managerial sense, resulting in, paradoxically, increasing, rather than decreasing forms of control over society. Due to their unique position in society, where their very existence is dependent upon them exercising control over other parts of society, anything they do has a controlling outcome. Coupling this with a lack of day by day control by the voting public, who have power to elect these bodies in western democracies but not a power to dictate practical action, leaves governments in a uniquely powerful position. Partly to avoid the searching questions from the public, resulting in more detailed forms of political accountability, and, following the logic of the paper, increasing control, governments have seen it appropriate to set up separate internal bodies (such as the auditor generals and the national audit offices) to demonstrate that they are subject to investigation. However, a further key argument of the paper is that, rather than providing an independent voice, auditor generals and the national audit offices provide legitimation to the original actions rather than a curtailment of these processes. The paper builds this complex argument conceptually and empirically. At a conceptual level it draws from a number of different literature bases to provide a “middle range" (Laughlin, 1995) theoretical schema. This is then amplified and developed through an empirical case in connection with the UK’s private finance initiative.     

商业化金融与合作性金融的并存发展--农村金融市场现状和发展

近年来，经济全球化对中国经济改革和金融改革提出了严峻挑战。根据中国政府加入世贸组织的承诺，2006年中国农业和金融业要进一步放开。中国要参与国际竞争，作为薄弱环节的农业和农村金融问题显得日益突出。     

Algorithms for handling soft constraints and their applications to staff scheduling problems

This paper presents a new model for dynamically deciding when, how, and to what extent soft constraints should be relaxed. The first part of the model is a depth-first search algorithm and a best-first repair algorithm which can generate partial schedules quickly. The second part is an iterative relaxation algorithm which can augment the generated partial schedules by slightly relaxing potentially relaxable constraints (i.e. soft constraints). The model guarantees that (1) a soft constraint will be relaxed only when no backtrack (repair) can be made within a time limit, (2) the relaxation of soft constraints can always deepen the search tree, and (3) the relaxation will only be made at dead nodes, and when the search algorithm can be continued the relaxed soft constraints will return to their initial states. The model has been successfully applied to two staff scheduling problems, dispatcher scheduling problem and crew scheduling problem. Copyright © 1999 John Wiley & Sons, Ltd.     

BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing

This systematic literature review summarizes the extant research in the Behavioral Finance (BeFi) and digital asset spaces to understand better the interactions of behavioral effects on the pricing of assets constructed, enabled, and exchanged in Decentralized Finance (DeFi) markets. We find that asset pricing in these rapidly evolving markets is better explained through BeFi than through traditional finance (TradFi) theory. Investor attention, sentiment, heuristics and biases, and network effects interact to form a highly volatile and dynamic market. We offer a deterministic research framework with propositions for future research. We further provide investors with a theoretically and empirically supported structure to better inform their decisions through an understanding of BeFi applications to DeFi.