Search results

Financial returns are often modeled as stationary time series with innovations having heteroscedastic conditional variances. This paper seeks to derive the kurtosis of stationary processes with GARCH errors. The problem of hypothesis testing for stationary ARMA(p, q) processes with GARCH errors is studied. Forecasting of ARMA(p, q) processes with GARCH errors is also discussed in some detail.

Estimating‐function methodology was the principal method used for the research. The results were also illustrated using examples and simulation studies. Volatility modeling is the subject of the paper.

The kurtosis of stationary processes with GARCH errors is derived in terms of the model parameters (ψ), Ψ‐weights, and the kurtosis of the innovation process. Hypothesis testing for stationary ARMA(p, q) processes with GARCH errors based on the estimating‐function approach is shown to be superior to the least‐squares approach. The fourth moment of the l‐steps‐ahead forecast error is related to the model parameters and the kurtosis of the innovation process.

This paper will be of value to econometricians and to anyone with an interest in the statistical properties of volatility modeling.

To study stochastic volatility in the pricing of options.

Random‐coefficient autoregressive and generalized autoregressive conditional heteroscedastic models are studied. The option‐pricing formula is viewed as a moment of a truncated normal distribution.

Kurtosis for RCA and for GARCH process is derived. Application of random coefficient GARCH kurtosis in analytical approximation of option pricing is discussed.

Findings are useful in financial modeling.

The purpose of this research is to introduce a class of FRC (fuzzy random coefficient) volatility models and to study their moment properties. Fuzzy option values and the superiority of fuzzy forecasts over minimum mean‐square forecasts are also discussed in some detail.

Fuzzy components are assumed to be triangular fuzzy numbers. Buckley's data‐driven method is used to determine the spread of the triangular fuzzy numbers by using standard errors of the estimated parameters.

The fuzzy kurtosis of various volatility models is obtained in terms of fuzzy coefficients. Fuzzy option values and fuzzy forecasts are illustrated with examples. Fuzzy forecast intervals are narrower than the corresponding MMSE forecast intervals.

This paper will be of value to econometricians and to anyone with an interest in financial volatility models.

Option pricing based on Black‐Scholes model is typically obtained under the assumption that the volatility of the return is a constant. The purpose of this paper is to develop a new method for pricing derivatives under the jump diffusion model with random volatility by viewing the call price as an expected value of a truncated lognormal distribution.

Using Taylor series expansion the call price under random volatility is expressed as a function of kurtosis of the observed volatility process and applied to various class of GARCH models.

A modified option pricing formula is developed for jump diffusion process model with random volatility.

The main contribution of the paper is the development of a kurtosis‐dependent option pricing formula for a jump diffusion model with random volatility.

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.

This paper originally proposed the fuzzy option pricing method for green bonds. Based on the requirements of arbitrage equilibrium, this paper draws on Merton's corporate bond option pricing model.

Describing the asset value behavior of green bond issuing enterprises through diffusion-jump processes to reflect the uncertainty brought by carbon emission reduction policies and technologies, using approximation methods to get the analytical pricing formula and then, using a fuzzification technique of Choquet expectation under  λ-additive fuzzy measures after considering fuzzy factors, the paper provides fuzzy intervals for the parity coupon rates of green bonds with different subjective levels for investors.

The paper proposes and argues the classical and fuzzy option pricing methods in turn for both corporate ordinary bonds and green bonds, considering carbon risk or climate risk. It implements the scenario analysis varying with industry emission standards and discusses the sensitiveness of the related key parameters of the option.

The fuzzy option pricing for the green bonds provides the scope of the variable equilibrium values, operational theoretical supports and some policy implications of carbon reduction and promoting green funding.

The logic of introducing the fuzziness of the option pricing for the green bonds lies with considering the existence of fuzzy information about the project supported by the green bond and the subjectivity of investors and it also responds to changes in technological uncertainty and policy uncertainty in the process of “carbon peaking and carbon neutrality.”

The purpose of this paper is to consider the problem of using “black‐box” methods to forecast catastrophe events, and illustrate the value of independent peer review.

The problem with black‐box catastrophe forecasts is the absence of both extensive validation data and impartial peer review. These issues may be addressed by comparing black‐box forecasts with a set of naïve alternative forecasts provided by an independent party. To illustrate this approach, the historical hurricane forecasts of Dr William M. Gray, professor at Colorado State University, are considered and a simple ARIMA analysis is offered as a naïve alternative.

The analysis shows that Dr Gray's complex forecasting methodology does in fact provide reasonable forecasts, and may indeed offer value beyond a naïve alternative model.

The editorial identifies a major problem in catastrophe forecasting, and suggests one way to address this problem.

Utilising the job demands-resources (JD-R) model, the purpose of this paper is to examine how aspects of the psychosocial work environment (namely, job demands and resources) are associated with presenteeism, and in particular, whether they are indirectly related via burnout and work engagement.

A cross-sectional survey of 980 working Australians measured the relationships between job demands (i.e. workplace bullying, time pressure and work-family conflict), resources (i.e. leadership and social support), burnout, work engagement and presenteeism. Path analysis was used to test the proposed hypotheses whilst controlling for participant demographics (i.e. sex, age, work level, duration and education).

Higher job demands (workplace bullying, time pressure, and work-family conflict) and lower job resources (leadership only) were found to be indirectly related to presenteeism via increased burnout. While increased job resources (leadership and social support) were indirectly related to presenteeism via improved work engagement.

The findings are consistent with the JD-R model, and suggest that presenteeism may arise from the strain and burnout associated with overcoming excessive job demands as well as the reduced work engagement and higher burnout provoked by a lack of resources in the workplace. Intervention programmes could therefore focus on teaching employees how to better manage job demands as well as promoting the resources available at work as an innovative way to address the issue of rising presenteeism.

This study is important as it is one of the first to examine the theoretical underpinnings of the relationship between presenteeism and its antecedents.

The purpose of this paper is to show that subordinated debt regulatory proposals assume that transactions in the secondary market of subordinated debt can attenuate moral hazard on the part of management if secondary market prices are informative signals of the risk of the institution. Owing to the proprietary nature of dealer prices and the liquidity of secondary transactions, the practical value of information provided by subordinated debt issues in isolation is questionable.

A multivariate dynamic risk signal is proposed that combines fluctuations in equity prices, subordinated debt and senior debt yields. The signal is constructed as a coincident indicator that is based in a time series model of yield fluctuations and equity returns. The extracted signal monitors idiosyncratic risk of the intermediary because yields and equity returns are filtered from market conditions. It is also predictable because it is possible to construct a leading indicator based almost entirely on spreads to Treasury.

The signal for the Bank of America and Banker's Trust is implemented. For Bank of America, the signal points mainly to two events of uprising risk: January 2000 when the bank disclosed large losses in its bond and interest‐rate swaps portfolios; and November 2000 when it wrote off $1.1 billion for bad loans. For Banker's Trust, the signal points to October/November 1995 after the filing of federal racketeering charges against Banker's Trust; and October 1998 when the bank suffered substantial losses from its investments in emerging markets.

The signal is a complementary instrument for regulators and investors to monitor and assess in real time the risk profile of the financial institution.