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Recently, Stein et al. (2016) studied theoretical properties and parameter estimation of continuous time processes derived as solutions of a generalized Langevin equation (GLE). In this paper, the authors extend the model to a wider class of memory kernels and then propose a bond and bond option valuation model based on the extension of the generalized Langevin process of Stein et al. (2016).

Bond and bond option pricing based on the proposed interest rate models presents new difficulties as the standard partial differential equation method of stochastic calculus for bond pricing cannot be used directly. The authors obtain bond and bond option prices by finding the closed form expression of the conditional characteristic function of the integrated short rate process driven by a general Lévy noise.

The authors obtain zero-coupon default-free bond and bond option prices for short rate models driven by a variety of Lévy processes, which include Vasicek model and the short rate model obtained by solving a second-order Langevin stochastic differential equation (SDE) as special cases.

Bond and bond option pricing plays an important role in capital markets and risk management. In this paper, the authors derive closed form expressions for bond and bond option prices for a wider class of interest rate models including second-order SDE models. Closed form expressions may be especially instrumental in facilitating parameter estimation in these models.

The purpose of this chapter is to study the mathematisation of finance – excessive use of mathematical models in finance – which has been widely blamed for the recent financial and economic crisis. We argue that the problem might actually be the financialisation of mathematics, as evidenced by the gradual embedding of branches of mathematics into financial economics. The concept of embeddedness, originally proposed by Polanyi, is relevant to describe the sociological relationship between fields of knowledge. After exploring the relationship between mathematics, finance and economics since antiquity, we find that theoretical developments in the 1950s and 1970s lead directly to this embedding. The key implication of our findings is the realization that it has become necessary to disembed mathematics from finance and economics, and proposes a number of partial steps to facilitate this process. This chapter contributes to the debate on the mathematisation of finance by uniquely combining a historical approach, which chronicles the evolution of the relation between mathematics and finance, with a sociological approach from the perspective of Polyani’s concept of embedding.

The purpose of this paper is to research the optimal portfolio proportion for the optimal investment model and the optimal consumption investment strategies for the optimal consumption investment model under compound‐jump processes.

Traditionally, the price of risky security or asset is often modeled as geometric Brownian motion. However, the analysis of stock price evolution reveals sudden and rare breaks logically accounted for by exogenous events on information. It is natural to model such behavior by means of a point process, or, more simply, by a Poisson process, which has jumps of constant size occurring at rare and unpredictable intervals. Assume that the price of risky security stock is modeled by a compound‐jump process, the renew process theory is chosen to solve the optimal investment model, the HJB equation is chosen for the optimal consumption investment model.

Derive the analytical optimal portfolio proportion for the reduction model of optimal investment. The optimal consumption investment strategies are given by some equations for the optimal consumption investment model.

Accessibility and availability of data are the main limitations which model will be applied.

The results obtained in this paper could be used as a guide to actual portfolio management.

The new approach for the optimal portfolio model under compound‐jump processes. The paper is aimed at actual portfolio managers.

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.

The purpose of this research is to investigate sustainable strategies for skills development that is specific to the youth of South Africa. International and South African data are statistically analysed and quantified to provide inputs for the systems dynamics (SD)-based predictive skills model. The skills model simulates the impact of barriers and drivers on youth skills development towards identification of focus areas for improvement.

The research adopts a mixed-methods approach. The study begins with an explorative literature study on skills development, with the findings applied in developing (1) South African specific research instruments for small, medium and micro enterprises (SMMEs) and skills programme grant recipients and (2) a conceptual framework of the SD predictive skills model. The responses to the South African specific instruments are analysed via confirmatory factor analysis (CFA), which quantifies the input coefficients to the system dynamics model. To quantify the global inputs for the SD model, an in-depth literature review of the global skills development initiatives is conducted. The SD model output on skills, for the South African inputs, is comparatively evaluated against global inputs.

The paper details the results of the literature analysis, instrument analyses, CFA and SD model. The instrument results rank experience, skills and interactions with experts and work-based learning as most important. South African and global learners identify networking as the primary medium for identifying training and employment opportunities. South African and global learners also identify qualifications and work-based experience as key to finding employment. The quantified results of the SA and global analysis are used as inputs in the SD model to deliver a forecasting tool. The SD model finds that the global data provide for better development of the skills base than the South African inputs. The key focus areas identified for improvement in South Africa include networking, work-based experience and a reduction in administrative requirements.

The research's originality resides in the ability to predict the impact of drivers and barriers on skills development. This research sought to transform qualitative global and South African inputs into a consolidated, predictive systems-based model. The SD model can be adopted as an indicator of drivers and barriers focused towards the optimisation of skills development.

The purpose of this study was to test the effectiveness of purposeful attempts to cope with stress by senior educational administrators in an Australian state education department of over 2,000 schools and employing teaching and administrative staff in excess of 60,000. At the time of the study this department was undergoing the initial stages of large‐scale restructuring, moving from a centralized system of management to a school‐centred, decentralized structure. This provided a unique opportunity to examine the ways in which senior executives respond in a time of discontinuous change. Presents general findings of a self‐report coping strategies questionnaire and reveals some significant relationships between general wellbeing, personality type and coping strategies.

The purpose of this study is to analyse and compile the literature on various option pricing models (OPM) or methodologies. The report highlights the gaps in the existing literature review and builds recommendations for potential scholars interested in the subject area.

In this study, the researchers used a systematic literature review procedure to collect data from Scopus. Bibliometric and structured network analyses were used to examine the bibliometric properties of 864 research documents.

As per the findings of the study, publication in the field has been increasing at a rate of 6% on average. This study also includes a list of the most influential and productive researchers, frequently used keywords and primary publications in this subject area. In particular, Thematic map and Sankey’s diagram for conceptual structure and for intellectual structure co-citation analysis and bibliographic coupling were used.

Based on the conclusion presented in this paper, there are several potential implications for research, practice and society.

This study provides useful insights for future research in the area of OPM in financial derivatives. Researchers can focus on impactful authors, significant work and productive countries and identify potential collaborators. The study also highlights the commonly used OPMs and emerging themes like machine learning and deep neural network models, which can inform practitioners about new developments in the field and guide the development of new models to address existing limitations.

The accurate pricing of financial derivatives has significant implications for society, as it can impact the stability of financial markets and the wider economy. The findings of this study, which identify the most commonly used OPMs and emerging themes, can help improve the accuracy of pricing and risk management in the financial derivatives sector, which can ultimately benefit society as a whole.

It is possibly the initial effort to consolidate the literature on calibration on option price by evaluating and analysing alternative OPM applied by researchers to guide future research in the right direction.

In recent years, practitioners have identified a number of problems with traditional performance management (PM) systems, arguing that PM is broken and needs to be fixed. In this chapter, we review criticisms of traditional PM practices that have been mentioned by journalists and practitioners and we consider the solutions that they have presented for addressing these concerns. We then consider these problems and solutions within the context of extant scholarly research and identify (a) what organizations should do going forward to improve PM practices (i.e., focus on feedback processes, ensure accountability throughout the PM system, and align the PM system with organizational strategy) and (b) what scholars should focus research attention on (i.e., technology, strategic alignment, and peer-to-peer accountability) in order to reduce the science-practice gap in this domain.

This paper derives the analytic solutions of the pure discount bond price under the various types of -stable Levy process. It is well-known that only a few cases in-stable Levy process have the moment generating function. This paper extends the model to damped-stable Levy processes, which have artificial stable process with the moment generating function. This paper also extends models to stochastic volatility by time change method of Levy process.

The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Lévy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices.

The authors apply tail-based and Lévy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Lévy models. As the Lévy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs.

Both EV and Lévy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance.

To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Lévy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.