International Journal of Financial Engineering

This paper adopts a parametric regression approach to model and calibrate implied volatility surface during the period of the global financial crisis. Due to its relatively low computational cost, it facilitates comparison across a great number of different competing models. The proposed regression models are backtested against historical S&P 500 prices during both volatile and non-volatile periods as proxied by the VIX index around the same time period, and the fits of the models are assessed. Furthermore both an equally weighted scheme and an alternative scheme based on observed implied volatilities as the weight are deployed and the results produced by these two schemes are contrasted and compared. Finally the concept of promptness, instead of the more traditional concept of time to maturity, is introduced as a covariate in the regression models to better capture the shape of the volatility surface during the period characterized by a prolonged low interest-rate environment.

Starting from CIR process, we build a new model for pricing discrete arithmetic Asian options with nonlinear transformation and stochastic time change. The new model introduces the nonlinearity in both drift and diffusion components of the underlying process and allows for flexible jump processes. We are able to derive the recursive formula for the moment generating function of average price by employing the eigenfunction expansion technique. The Asian option prices can then be implemented through a Fourier transform. We also investigate the sensitivities of option prices with respect to the parameters of the new model.

In this paper, we consider a strategic management of investment risks of an economy that faces financial crisis. The assets consider are multiple stocks and multiple fixed assets. Asset of the economy is a linear combination of portfolio weights and the expected stock returns plus a linear combination of the price of fixed and quantities of assets. Also, the debt profile, consumption and income growth of the economy are studied. The resulting optimization problem was solved by the method of Lagrangian multiplier. The aims of this paper are to determine the (i) mean–variance investment portfolio of the economy, (ii) optimal investment of the economy, (ii) optimal debt ratio of the economy, (iii) efficient frontier for the economy (iv) global minimum risks in the investment portfolio. Empirical results using real data collected from Nigerian Stock Exchange are considered.

The aim of this paper is to bring out a new perspective for Electricity price forecasting. Numerous studies have focused on forecasting the day-ahead or long-term price forecasting of electricity, rather than examine the relationship between energy commodities, by using various methods. Therefore, this study proposes a model-free approach for electricity price forcasting (EPF). The proposed approach is based on Partial Wavelet Coherency (PWC) and Multiple Wavelet Coherency (MWC) method. These methods are capable of uncovering the coherent time intervals simultaneously for time and frequency domains between the examined time series. VARMA uses the coherent time intervals and outperforms its univariate counterpart (ARMA), both in point and interval forecasting.

This paper explores a novel technique to compute the level of covariance between any two genuinely correlated assets by adopting the idea of random permutations by proposing an unbiased covariance estimator “${RP}_{\mathrm{\text{Avg}}}$” based on daily high-low prices. The main goal is to boost the relative efficiency of the estimator by increasing the number of random permutations. We validate this claim with the help of simulations later. Further, we prove theoretically and through simulations that the proposed estimator is unbiased for a pair of random walks. Upon empirically implementing the estimator in a dataset of three sets of stock indices: Nifty, FTSE100 and S&P500 after accounting for exchange effects (USDINR and GBPINR) over a sample period of 252 months (Jan 1996–Dec 2016), we do not find evidence of any bias in the estimator. Also, there is a visible asymmetry in the correlation between US-Indian markets from the two investor’s point of view.

The study adds new mechanism of earnings management which explores the mediation between financial policies and market value of firms. A comparative study conducted on manufacturing sectors in which sample of 857 companies for China and 150 companies for Pakistanis taken from lists of Stock Exchanges during the period 2012 to 2016. Discretionary accruals have been calculated using the modified Jones Model (1995) and finally Panel analysis was used to analyze the data. Result discloses that in both countries, managers’ Earning Management practices could significantly mediate between these financial policies and firm’s value as measured by Tobin’s Q and stock return. This is the first empirical evidence that make available importance of financial policies which could efficiently influenced by earnings management and ultimately influences the firm’s performance.

Using Martingale methods, we study the problem of optimal consumption-investment strategies in a complete financial market characterized by stochastic volatility. With Heston’s model as the working example, we derive optimal strategies for a constant relative risk aversion (CRRA) investor with particular attention to the cases where (i) she solely seeks to optimize her utility for consumption, and (ii) she solely seeks to optimize her bequest from investing in the market. Furthermore, we test the practical utility of our work by conducting an empirical study based on real market-data from the S&P500 index. Here, we concentrate on wealth maximization and investigate the degree to which the inclusion of derivatives facilitates higher welfare gains. Our experiments show that this is indeed the case, although we do not observe realized wealth-equivalents as high as expected. Indeed, if we factor in the increased transaction costs associated with including options, the results are somewhat less convincing.

In this paper, an analytical solution for the well-known Hamilton–Jacobi–Bellman (HJB) equation that arises from the Merton problem subject to general utility functions is presented for the first time. The solution presented in this paper is written in the form of a Taylor’s series expansion and constructed through the homotopy analysis method (HAM). The fully nonlinear HJB equation is decomposed into an infinite series of linear PDEs which can be solved analytically. Four examples are presented with the first two cases showing the accuracy of our solution approach; while the last two demonstrating its versatility.

This paper extends the simulation algorithm by Andreasen and Huge (2011) to the simulation of option prices and deltas on Lévy driven assets where the simulation is performed relying on the inverse transition matrix of the discretized partial integro differential equation (PIDE). We demonstrate how one can get accurate prices and deltas of European options on VG and CGMY via Monte Carlo simulations.