International Journal of Financial Engineering

A framework for measuring and managing the risk of embedded options in non-traded credit is developed. For typical bank clients there is no market information related to their ability to pay (bond or CDS spreads) available. The absence of market information is a key assumption of this paper. In this case, a bank has to rely solely on statistical data to judge the credit quality of a borrower. To value a loan with embedded options like prepayment rights, a model is proposed that combines an interest rate derivatives pricing model with statistical information on default and recovery rates. Using this for evaluating the risk of embedded options in loans, it is shown how the concepts of credit risk management can be applied after defining a suitable concept of risk. It turns out that this modeling framework combines the theories of derivatives pricing and credit risk modeling in the sense that derivatives pricing theory measures the costs for hedging optional components in loans while credit risk modeling measures the risk that these hedging costs turn out to be inadequate. This risk depends not only on the single loan’s risk characteristics but also on the dependence structure and the granularity of the total loan portfolio.

This paper aims to develop a deep learning-based numerical method for option prices for the Barndorff–Nielsen and Shephard model, a representative jump-type stochastic volatility model. Using that option prices for the Barndorff–Nielsen and Shephard model satisfy a partial-integro differential equation, we will develop an effective numerical calculation method even in settings where conventional numerical methods are unavailable. In addition, we will implement some numerical experiments.

We study the problem faced by a wage earner with an uncertain lifetime who has access to a Black–Scholes-type financial market consisting of one risk-free security and one risky asset. His preferences relative to consumption, investment and life insurance purchase are described by a robust expected utility. We rewrite this problem in terms of a two-player zero-sum stochastic differential game and we derive the wage earner optimal strategies for a general class of utility functions, studying the case of discounted constant relative risk aversion utility functions with more detail.

This study attempts to compare the performance of Fama–French three-factor model (FFTFM) and Fama and French six-factor model (FFSFM) in predicting the variations in expected returns of Nifty-100 listed stocks. Only 5/6 of the total listed companies are chosen, while the remaining 1/6 are ignored because they are not listed for the whole study period. The stocks are divided into two size groups and three groups based on B/M, OP, Inv and MOM using independent sorts to create 24 portfolios. The monthly average returns of the MC-MOM portfolios increase as momentum increases, in contrast to MC-B/M and MC-Inv portfolios. Almost all the portfolios with high returns are paired with significant risk, apart from the BM portfolio in size and profitability group. The findings prove that the FFSFM outperforms FFTFM on all the GRS test parameters. However, there is no significant improvement in explanatory power over the FFTFM.

We study the optimal order placement strategy with the presence of a liquidity cost. In this problem, a stock trader wishes to clear her large inventory by a predetermined time horizon T. A trader uses both limit and market orders, and a large market order faces an adverse price movement caused by the liquidity risk. First, we study a single period model where the trader places a limit order and/or a market order at the beginning. We show the behavior of optimal amount of market order, $m^{*}$, and optimal placement of limit order, $y^{*}$, under different market conditions. Next, we extend it to a multi-period model, where the trader makes sequential decisions of limit and market orders at multiple time points.

This paper primarily examines the quadratic effect of banks’ size on capital regulation and risk-taking behavior by using simultaneous equation approach. This paper primarily examines the quadratic effect of banks’ size on capital regulation and risk-taking behavior by using simultaneous equation approach. To carry out the objective, this study has been built on the two-stage least squares (2SLS) method for a dynamic unstructured panel data of 85 banks from the Central European banks for the period 2012–2017. There is a positive and significant relation between regulatory capital and risk. Also, higher risk-taking behavior causes banks to sacrifice their stability. This study also finds that there is a negative correlation between bank size and capital, indicating that larger the bank size lower tendency to keep capital more. In similar way, there is also a negative association between bank size and risk taking, indicating that lower tendency of taking risk by large banks and vice-versa. Finally, this paper can be used as a medium of information for the stakeholders of banks and others financial institutions of the country. There is a dearth of literature which was built on the quadratic effect of bank size regarding recent financial regulation and risk.

This examination endeavors to divulge the effects of the COVID-19 first and second waves on daily sectoral indices and volatility spillover in Indian markets. The empirical outcomes of the analysis reveal that the outbreaks of the first and second waves had a heterogeneous impact on the average value of eleven major sectoral indices. It also produces sufficient evidence of the assorted impacts of the first and second waves on volatility. The empirical outcome also confirms that the COVID-19 first wave led to greater uncertainty and intensification of volatility in the financial market as compared to the second wave. Finally, our research shows that the auto, banking, and Fast-moving consumer good (FMCG) sectors in India experienced higher volatility during the first wave of COVID-19, as these sectors contributed to the volatility in other sectors. However, consumer durables, metals, and oil and gas appear to have had the greatest impact on sectoral indices during the first wave, as they are a net recipient of volatility in the Indian financial market. On the other hand, on the basis of relevant outcomes, it can be surmised that the media, FMCG, and banking sectors demonstrated a high level of dominance on the other indices during the second wave of COVID-19 in India. However, consumer durables, pharmaceuticals, real estate, and information technology sectoral indices appeared mostly affected during the first and second waves of COVID-19, as they are net receivers of volatility in the Indian financial market.

The Monte Carlo method, thanks to its flexibility in designing even extremely complex payoffs, is assuming an increasingly important role in quantitative analysis. Its main limitation is the high computational cost linked to its modest speed of convergence to the fair value of the product. One of the best-known statistical techniques is to replace the random number generator with “low discrepancy” deterministic numerical sequences, producing a Quasi-Monte Carlo. Through its implementation for the analysis of three investment certificates featuring different characteristics and different stochastic processes used for the underlying simulation, the study demonstrates the possibility of achieving interesting results in terms of performance even for pricing these structured products ever more popular in the financial industry.

Determining stock price movements is a challenging problem because stock prices are often influenced by multiple factors such as economic, political, business, and human behavior. In this paper, we will attempt different modeling methods for two types of data, a total of 40 Dow Jones Industrial Index components, to verify the effectiveness of daily and high-frequency data for stock price prediction. Furthermore, we will attempt to validate the performance of LSTM model in stock price prediction, and also try to improve its performance by incorporating an attention mechanism. We assume that adding an attention layer to LSTM model would improve model performance in our data sets, especially in high-frequency data, since the data set would contain a huge amount of noise. Our results indicate that the simple LSTM performs better than the attention-based LSTM for both data types of prediction tasks with a benchmark of the number of stock prediction outcomes that outperform the number of those in other model, which is 24 out 40 stocks, which refutes our initial assumptions and does not validate whether adding attention mechanism is useful for solving the shallow layers and gradient vanishing problem and thus improving the LSTM model performance.

We consider the option pricing problem when the underlying asset price is driven by a continuous time autoregressive moving average (CARMA) process, time changed a Lévy subordinator or/and an absolutely continuous time change process. We derive the analytical formulas for the option prices by employing the orthogonal polynomial expansion method. Our method is based on the observation that the CARMA process belongs to the class of polynomial diffusion and the time variable and underlying state variables enter the polynomial expansion separately. We demonstrate the accuracy of the method through a number of numerical experiments. We also investigate the price sensitivities with respect to the key parameters that govern the dynamics of the underlying state and time change variables.

Development financing focusing on climate hazards has become necessary in recent decades as a result of the rise in emissions of greenhouse gases (GHG). This study investigates how the Congo Basin’s greenhouse gas emissions are affected by using renewable energy sources and climate risk-related development financing. Multiple conclusions are drawn from panel regression analysis. First, there’s a slight but substantial rise in GHG emissions when climate risk-related development finance increases. Second, a boost in climate risk-related mitigation finance substantially encourages the introduction of renewable energy. Third, greater utilization of renewable energy results in a diminution in GHG emissions. Finally, greater utilization of the renewable energy minimizes the influence of the climate risk-related development finance. The research recommends creating a monitoring system to guarantee the effective use of climate funding for generating renewable energy sources, including wind, biomass, geothermal, hydropower, and solar energy. Additionally, it urges donor economies and authorities to provide emerging economies with a supplementary consistent and steady flow of financing for development mitigation connected to climate risk.