Pricing Stock Options with Stochastic Interest Rate

This paper constructs a closed-form generalization of the Black-Scholes model for the case where the short-term interest rate follows a stochastic Gaussian process. Capturing this additional source of uncertainty appears to have a considerable effect on option prices. We show that the value of the stock option increases with the volatility of the interest rate and with time to maturity. Our empirical tests support the theoretical model and demonstrate a significant pricing improvement relative to the Black-Scholes model. The magnitude of the improvement is a positive function of the option's time to maturity, the largest improvement being obtained for around-the-money options

Minimum Description Length Hopfield Networks

Associative memory architectures are designed for memorization but also offer, through their retrieval method, a form of generalization to unseen inputs: stored memories can be seen as prototypes from this point of view. Focusing on Modern Hopfield Networks (MHN), we show that a large memorization capacity undermines the generalization opportunity. We offer a solution to better optimize this tradeoff. It relies on Minimum Description Length (MDL) to determine during training which memories to store, as well as how many of them.Comment: 4 pages, Associative Memory & Hopfield Networks Workshop at NeurIPS202

Pricing Stock Options with Stochastic Interest Rate

This paper constructs a closed-form generalization of the Black-Scholes model for the case where the short-term interest rate follows a stochastic Gaussian process. Capturing this additional source of uncertainty appears to have a considerable effect on option prices. We show that the value of the stock option increases with the volatility of the interest rate and with time to maturity. Our empirical tests support the theoretical model and demonstrate a significant pricing improvement relative to the Black-Scholes model. The magnitude of the improvement is a positive function of the option's time to maturity, the largest improvement being obtained for around-the-money options

Seroprevalence and molecular detection of Bovine Parainfluenza-3 Virus (BPI-3V)

The study aims to investigate the presence of Bovine Parainfluenza-3 Virus (BPI-3V) by using direct Enzyme-Linked Immunosorbent Assay (ELISA) and Real-Time- quantitative Polymerase Chain Reaction RT-qPCR technique and evaluation some clinical and epidemiological features of the disease. One hundred forty-seven (147) animals of different age (6 months to 8 years) and sex from different regions of Al-Diwaniya governorate that showed respiratory signs were examined between November 2012 and April 2013. Results of the clinical study showed that there was an increase in body temperature up to 40 C0, serous watery nasal discharge, increased respiratory rate, abnormal breath sound (loud, harsh sound, whistling or wheezing), and coughing. The infection rate by using direct ELISA test was (30.26 %). The spreading rate of BPIV-3 in relation to ages, regions and months of the years was (48%) in the age group 6 months-3 years, highest rate (60%) in December as compared with other months and Sedeer region recorded infection rate (40%). The results of Real Time-qPCR showed a high infection rate of BPI-3 virus 55.13% in cattle population as high sensitivity of this technique. Higher percentage recorded in tracheal tissue sample 60.60 % as compared with lungs tissue and nasal swabs in the percentage of 54.54% and 50%, respectively. In conclusion, there was a characteristic epidemiological feature of spreading of BPIV-3 in depending on age groups, different regions, and different months of the year

Effect of the economic outturn on the cost of debt of an industrial enterprise

The cost of debt is referred to as the key factor determining profitability. It is a decisive factor in decision making of the management, especially in strategy development. The purpose of this paper is to establish the relationship between the volume of debt and the economic outturn of industrial enterprises. Using artificial neural networks, the relationship between interest costs and three profit categories is examined. Data of 5622 Czech processing enterprises in the years 2015-2017 are used. Multilayer perceptron neural networks and neural networks of basic radial functions are used for processing. A total of 10,000 neural structures are generated for each cost-interest relationship and the corresponding profit, of which 5 are retained, showing the best results. The results indicate that in all cases of profit there is no dependence between the interest and the amount of profit generated. Profiting companies do not get debt cheaper than other businesses

Non-Standard Errors

In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants