International Journal of Financial Engineering

We propose a new computational method for a class of controlled jump-diffusions for financial applications. In the first step of our method, we apply piecewise constant policy approximation where we partition the time horizon into small time intervals and the control is constant on each interval. In the second step, we develop a Hilbert transform approach to solve a discrete time dynamic programming problem. We provide rigorous error bounds for the piecewise constant policy approximation for controlled jump-diffusions, generalizing previous results for diffusions. We also apply our method to solve two classical types of financial problems: option pricing under uncertain volatility and/or correlation models and optimal investment, including utility maximization and mean-variance portfolio selection. Through various numerical examples, we demonstrate the properties of our method and show that it is a computationally efficient choice for low-dimensional problems. Our method also compares favorably with some popular approaches.

This paper revisited the relationship between capital buffers and risk adjustments by showing the impact of the business cycle. Empirically, we used an unbalanced panel dataset from 426 banks of the BRIC countries (i.e., Brazil, Russia, India, and China) for the period 2007–2016. By using the two-step system GMM (2GMM), this study shows the results as: (i) capital buffers of Russia, India, and China behave counter-cyclically while it is pro-cyclical for Brazilian banks over the business cycle; (ii) in BRIC’s economy, credit risk, and bank financial stability is related to business cycle in counter and pro-cyclical fashion, respectively; (iii) capital buffers adjustment speed is the premier in China and India, shining banks accessibility to capital refill is much easier to Brazil and Russia. The adjustment speed is heterogeneous across countries; and (iv) financial stability in apex for the Chinese, Russian, and Indian banks apart from the Brazilian banks.

Our study makes use of a new approach to estimate time-varyingbetas with an application of the corrected Dynamic Conditional Correlation (cDCC) model. Our empirical methodology encompasses an examination of predictive relations between equity return and different specifications of dynamic conditional beta, using cross-sectional regression analysis at both the portfolio and firm levels. Our main finding is a significant, positive relation between equity excess return and an interactive cross product term of dynamic conditional beta and market excess return ($\beta r_m$); suggesting that equity return is largely determined by an interaction effect between dynamic beta and market return.

Despite a substantial growth in efficiency and profitability, South Asian region’s well-established banking system is likely to be incapable to grasp wide sections of the population, particularly the deprived ones. Numerous studies revealed that financial inclusion impact bank stability, but no significant empirical study has been made on the economies of South Asian region. The aim of the study is to explore the impact of financial inclusion on bank stability across South Asian region using data from 88 commercial banks from four economies (Bangladesh, India, Pakistan and Sri Lanka) over the period of 2012–2018. Results using two-step system GMM suggest that an increase in financial inclusion enhances bank stability across economies of South Asian region. This study contains some significant policy implications to generate real opportunities for financial inclusion to improve bank stability.

Prediction of financial distress for lending institutions has been a major concern since the financial crisis of 2008. The motivation for empirical research in bank bankruptcy prediction is clear — the early detection of financial distress and the use of corrective measures are preferable to protection under bankruptcy law. If it is possible to recognize failing banks in advance, then appropriate action can be taken to reverse the process before it is too late. This study uses panel multi-state Markov (MSM) chains to build a predictive model for financial distress of banks in Zimbabwe. Microeconomic factors and the CAMELS ratings were used in the construction of the MSM model. Distress probabilities were calculated using hazard ratios found by MSM and then the Altman $Z$-Scores were generated for each bank as a means of validating the built MSM model. The scores generated were very similar to the current CAMELS ratings.

The studies in recent era exhibit that the financial inclusion has become a scorching issue in playing its vital role while improving the lives of vulnerable and underprivileged households’ well-being. But, the microdata impact of financial inclusion on household welfare is not being analyzed to substantial extent in Pakistan yet. Therefore, it is imperative to examine the effects of financial inclusiveness on the living standards of individuals to persuade them toward formal financial inclusion. Thus, the study attempts to explore the link between an expanded formal financial system and poverty alleviation through smooth consumption levels, providing access to credit and investment facilities to the individuals. For this purpose, the household level data of Household Integrated Economic Survey (HIES) for the time-period of 2013–2014 to evaluate the microlevel effect of formal financial inclusion, realizing solitarily for the remittance receiving households have been used. The well-being of households receiving formal financial inflows got highly affected by facilitating the remittance receivers through instant, reliable and frequent transfer of funds. Therefore, the main objective of the study is to investigate the effect of formal financial transfers on the socio-economic status of remittance receiving and nonreceiving individuals. The outcomes of the study suggest that the household welfare is greatly influenced by the formal financial inclusiveness along with other variables such as annual income, education level of the family, number of earning members, loans borrowed by the family, land and building ownership, and the head employed are found to be positively and significantly determining the household’s welfare.

The aim of this paper is to derive a second-order asymptotic expansion for the price of European options written on two underlying assets, whose dynamics are described by multiscale stochastic volatility models. In particular, the second-order expansion of option prices can be translated into a corresponding expansion in implied correlation units. The resulting approximation for the implied correlation curve turns out to be quadratic in the log-moneyness, capturing the convexity of the implied correlation skew. Finally, we describe a calibration procedure where the model parameters can be estimated using option prices on individual underlying assets.

The purpose of this paper is to investigate the risk-return tradeoff allowing for the presence of noise traders, i.e., a subset of investors who either base their trading strategies on sentiment or hold unjustified optimistic/pessimistic views regarding market prospects. We measure noise traders’ sentiment relying on two sets of indices, namely the Baker and Wurgler sentiment index and the Michigan Consumer Confidence Index, in the US stock market. Under the assumption of the presence of noise traders’ sentiment, the risk-return tradeoff is tested through two sets of models: Merton’s Intertemporal CAPM and the GARCH-in-mean model. First, we find that the relationship between risk and return allowing for the presence of noise trader risk as measured by the Baker and Wurgler sentiment index is positive and statistically significant when tested through Merton’s Intertemporal CAPM. Second, the risk-return tradeoff tested through GARCH-in-mean models augmented by noise traders’ risk as measured through survey-based measures of sentiment establishes no clear evidence for a significant mean–variance relationship. Overall, we confirm Merton’s (1973) hypothesis that the more risk an investor bears, the greater his expected returns. This paper contributes to the asset pricing literature by trying to shed some light on the risk-return tradeoff from the standpoint of behavioral finance.

We investigate the consistency under inversion of jump diffusion processes in the foreign exchange market. That is, if the EUR/USD exchange rate follows a given type of dynamics, under which conditions will USD/EUR follow the same type of dynamics? After giving a numerical description of this property, we establish a suitable local volatility structure ensuring consistency. We subsequently introduce jumps and analyze both constant and random jump size. While in the first scenario consistency is automatically satisfied, the second case is more involved. A fairly general class of admissible densities for the jump size in the domestic measure is determined.

By applying the Lie–Trotter operator splitting method and the idea of the WKB method, we have developed a simple, accurate and efficient analytical approximation for pricing the constant elasticity of variance (CEV) spread options. The derived option price formula bears a striking resemblance to Kirk’s formula of the Black–Scholes spread options. Illustrative numerical examples show that the proposed approximation is not only extremely fast and robust, but it is also remarkably accurate for typical volatilities and maturities of up to two years.

In this paper, we develop a pricing model for weather derivatives at a single location and multiple locations. In the first model, a Lévy regime-switching temperature model for a single location is used to price futures written on cumulative average temperature and growing degree-days indices at a single location. To allow analytical pricing for futures on temperature basket, an $n$-dimension regime-switching temperature model whose driving noise is captured by a Brownian motion is developed. The correlation between the driving noise in each regime is assumed to be a function of the space between the different farming locations and increases with decreasing space. The temperature basket index assigns a weight to each location that is exposed to risk. However, a location with a higher risk is assigned a larger weight and vice versa.

By assuming that the regimes are independent to each other, the future is calculated for each regime model. The final futures price is calculated using the weighted sum of the individual regimes. These pricing models can be applied in the future markets to price weather derivatives for the agricultural sector. Research on spatial-temporal pricing model is vital to the development of the weather derivatives market, as investors are more vulnerable to temperature risk over different farming locations as compared to single location.

This paper proposes swaps on two important new measures of generalized variance, namely, the maximum eigenvalue and trace of the covariance matrix of the assets involved. We price these generalized variance swaps for Barndorff-Nielsen and Shephard model used in financial markets. We consider multiple assets in the portfolio for theoretical purpose and demonstrate our approach with numerical examples taking three stocks in the portfolio. The results obtained in this paper have important implications for the commodity sector where such swaps would be useful for hedging risk.

We propose a parsimonious model for financial pricing that incorporates the existence of a random environment; such construction can be though as an extension of the Cox–Ross–Rubinstein (CRR) model. Our model is motivated from the Sinai random walk, but we mention the difficulty of applying such model if we try to use it with the CRR procedure. As it was done with Sinai’s walk, we provide a method to connect the most visited sites of the model with the minimum points of a function of the environment. We present some simulations and a numerical experiment to bring a new perspective.