International Journal of Financial Engineering

In the present paper, we study the optimal execution problem under stochastic price recovery based on limit order book dynamics. We model price recovery after execution of a large order by accelerating the arrival of the refilling order, which is defined as a Cox process whose intensity increases by the degree of the market impact. We include not only the market order, but also the limit order in our strategy in a restricted fashion. We formulate the problem as a combined stochastic control problem over a finite time horizon. The corresponding Hamilton–Jacobi–Bellman quasi-variational inequality is solved numerically. The optimal strategy obtained consists of three components: (i) the initial large trade; (ii) the unscheduled small trades during the period; (iii) the terminal large trade. The size and timing of the trade is governed by the tolerance for market impact depending on the state at each time step, and hence the strategy behaves dynamically. We also provide competitive results due to inclusion of the limit order, even though a limit order is allowed under conservative evaluation of the execution price.

The open-end fund industry controls significant assets. At the same time, the US centric mutual funds scandal of 2003 highlighted some of the short comings of the structure of these securities. In this paper, we revisit some issues relating to this scandal and provide a quantitative framework to inspect for arbitrage opportunities in open-end funds. The framework presented is a hierarchical graphical model which allows parameter posteriors to be inferred by a variational Bayesian approach. This enables prior knowledge about the funds to be incorporated into the model at the same time as being computationally cheap to run. This class of models falls under the heading of Bayesian pattern recognition. Using linear Gaussian basis functions, a regression model is used to generate a predictive distribution for the high-frequency fair price of an open-end fund. This fair price can be used to estimate a latent pricing time for the fund. Using this information, and conditional on the cut-off time for purchase of the fund, a trading signal for arbitraging the open-end fund can be designed. This framework is applied to a sample of the 1,296 most highly capitalized open-end funds from Western Europe and simulations carried out. The trading strategy has a post-cost mean Sharpe ratio of over 1.6 when considered across the sample. The results suggest that the open-end fund industry in Europe still has lessons to learn from the scandal in the US a decade earlier.

The present paper is conceived to expose a new model of bank capital adequacy. Our analysis highly relies on the dynamics of the capital adequacy ratio (CAR) as computed from balance sheet items, namely, loans, securities and bank regulatory capital, in a stochastic dynamic setting. In addition, an attempt is made to demonstrate how the CAR can be optimized in terms of asset allocation and the rate at which the bank remains solvent. Consequently, a dynamic programming principle has been applied to solve the Hamilton–Jacobi–Bellman (HJB) equation explicitly in the case of CRRA utility function. The parameters’ estimation is based on the maximum likelihood method, using Tunisian data relevant to the period 2004–2012. As regard computations, they are carried out in MATLAB. The simulation results have shown the model relevance as a decision tool for bank risk management. Moreover, the relationship persisting between the macroeconomic activity and the bank capital adequacy has also been discussed.

This paper has been made to analyze the linkage between corporate governance and corporate social responsibility. From analysis, it is found that Eastern Bank Ltd. (EBL) performs better than other selected banks but not enough in practicing corporate social responsibility. While, conventional banks are more imperative than Islamic banks as all the indicators cover its benchmark apart from return on total assets. It has proved that there is a significant relationship among return on equity, earnings per share, corporate governance and corporate social responsibility but corporate social responsibility has shown little impact on corporate performance.

In this paper, we propose a hybrid method that combines a hazard rate model with the CreditGrades model to value credit default swap (CDS) contracts. The CreditGrades model is considered an industry benchmark for analyzing credit derivatives. The hybrid method makes use of the default probability generated by the CreditGrades model to determine the hazard rate specific to the bond issuing firm. In this way, the hybrid method is empirically shown to produce better CDS forecast.

Recent academic literature in finance documents both risk-return trade-off and gradual information diffusion (ID). Motivated by these two financial theories, this paper proposes the ARCH-M model augmented by an ID indicator to forecast the U.S. stock market returns. Empirical studies performed on the monthly S&P500 index show that our approach is useful in both statistical and economic sense. Further analysis shows that the ID provides complementary information to risk-return trade-off effect. Our findings confirm that financial theories are valuable for stock return forecasting.

We apply adjoint algorithmic differentiation (AAD) to the risk management of securities when their price dynamics are given by partial differential equations (PDE). We show how AAD can be applied to forward and backward PDEs in a straightforward manner. In the context of one-factor models for interest rates or default intensities, we show how price sensitivities are computed reliably and orders of magnitude faster than with a standard finite-difference (FD) approach. This significantly increased efficiency is obtained by combining (i) the adjoint forward PDE for calibrating model parameters, (ii) the adjoint backward PDE for derivatives pricing, and (iii) the implicit function theorem to avoid iterating the calibration procedure.

In this paper, we present a dynamic risk model that can assess the stochastic credit quality of senior tranches of collateralized mortgage obligations (CMO) while supported by any number of junior bond classes. We design the model to be universal and to embed common hazards and retreats within the U.S. housing market. This deployment assists us in resolving real problems when gauging the dynamic creditworthiness of CMOs’ senior bond tranches. Resulting from our subsequent theoretical simulations, we discover the boundaries of these structured financial instruments when exposed to relatively modest probabilities of a broad economic crisis. We demonstrate that despite their diverse supportive structures, CMOs are not as protective as originally thought by many investors when a widespread housing calamity progresses.

An alternative and simple algorithm for valuating the price of discrete barrier options is presented. This algorithm computes the price just exactly the same as the Cox–Ross–Rubinstein (CRR) model. As opposed to other pricing methodologies, this recursive algorithm utilizes only the terminal nodes of the binomial tree and it captures the intrinsic property, the knock-in or knock-out feature, of barrier options. In this paper, we apply the algorithm to compute the price of an Up and Out Put (UOP) barrier option and compare the results obtained from the CRR model. We then determine the time complexity of the algorithm and show that it is $\mathrm{\Theta }(n^2)$.

A class of positive Azéma–Yor martingales was first introduced in the option pricing context by Peter Carr (2014). In this paper, we present a rigorous study of the short maturity asymptotics for Asian, upper barrier, lower barrier and European options with continuous-time averaging, under the assumption that the underlying asset follows a Azéma–Yor martingale.