Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

We present a neural network-based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models—including second-generation stochastic volatility models and the rough volatility family—and a range of derivative contracts. Neural networks in this work are used in an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. The form in which information from available data is extracted and used influences network performance: The grid-based algorithm used for calibration is inspired by representing the implied volatility and option prices as a collection of pixels. We highlight how this perspective opens new horizons for quantitative modelling. The calibration bottleneck posed by a slow pricing of derivative contracts is lifted, and stochastic volatility models (classical and rough) can be handled in great generality as the framework also allows taking the forward variance curve as an input. We demonstrate the calibration performance both on simulated and historical data, on different derivative contracts and on a number of example models of increasing complexity, and also showcase some of the potentials of this approach towards model recognition. The algorithm and examples are provided in the Github repository GitHub: NN-StochVol-Calibrations.     

Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities

This paper examines the performance of panel data models in measuring cost-efficiency of electricity distribution utilities. Different cost frontier models are applied to a sample of 59 utilities operating in Switzerland from 1988 to 1996. The estimated coefficients and inefficiency scores are compared across different specifications. The results indicate that while the average inefficiency is not sensitive to the econometric specification, the efficiency ranking varies significantly across models. The reasonably low out-of-sample prediction errors suggest that panel data models can be used as a prediction instrument in order to narrow the information gap between the regulator and regulated companies.     

Are free trade agreements good for the environment? A panel data analysis

This study empirically examines the relationship between free trade agreements (FTAs) and greenhouse gas (GHG) emissions. For this purpose, we use three different FTAs: the Southern Common Market, the North American Free Trade Agreement, and the Australia–United States Free Trade Agreement. These FTAs are between developing, both developing and developed, and only developed countries, respectively. Panel unit root, panel cointegration, and fully modified ordinary least squares estimations are employed to examine the long‐run relationship between GHG emissions and trade liberalization. The results indicate that the environmental effects of FTAs depend on the different agreement types. When FTAs are between only developed or developing countries, overall there is no environmental damage, and these types of FTAs can be beneficial for the environmental quality in the long run. However, when developing and developed countries are in a trade agreement, overall environmental quality decreases due to increased GHG emissions.     

Do Tighter Loan Covenants Signal Improved Future Corporate Results? The Case of Performance Pricing Covenants

Covenants in corporate bonds and loan agreements mitigate agency conflicts between borrowers and lenders and may provide a signal of borrower quality to help resolve information asymmetry. Performance pricing covenants in bank loans specify automatic adjustments to loan spreads based on borrowers’ subsequent performance. Our covenant signaling framework views interest‐decreasing performance pricing as a tight covenant associated with borrowers’ private information on improved future performance accompanied by reduced credit risk. This positive signal is associated with larger positive loan announcement returns and greater improvements in future borrower performance. Further, in addition to signaling value, we find that the spread impact of this class of covenant also depends on its option value and reduction in transaction costs.     

Ex-post favoring ranks: a fairness notion for the random assignment problem

We introduce two notions of ex-post fairness, namely ex-post favoring ranks (EFR) and robust ex-post favoring ranks, which consider whether objects are received by those agents who have the highest rank for them. We examine their compatibility with standard properties of random assignments and state some impossibility theorems. We also propose and formalize a revised version of the Boston mechanism and prove that it provides an EFR random assignment.

     

Islamic Religious Atmosphere and Audit Pricing: Evidence from Iran

This study examines whether the Islamic religious atmosphere of local communities influences audit pricing. We use a comprehensive survey conducted by Iran's Ministry of Culture and Islamic Guidance to measure the Islamic religious atmosphere. Using a sample of 1,204 observations from firms listed in Iran's capital market, we find that firms located in regions with a strong Islamic religious atmosphere pay significantly lower audit fees. Furthermore, the study reveals that family ownership (auditor size) strengthens (weakens) the aforementioned relationship. However, we did not find a significant moderating role for the local community's social capital. We contribute to the ethics literature by providing a better understanding of the economic consequences of the religious atmosphere and offering policy, practical, and educational implications.     

Fingolimod versus natalizumab in patients with relapsing remitting multiple sclerosis: a cost-effectiveness and cost-utility study in Iran

Aims: Multiple sclerosis (MS) is a chronic, autoimmune, and inflammatory disease. If the first-line medicines are not effective enough, specialists will prescribe second-line medicines, such as natalizumab and fingolimod. This study aimed to compare the cost-effectiveness and cost-utility of fingolimod with those of natalizumab in patients with relapsing-remitting multiple sclerosis (RRMS) in Iran, Fars province in 2016.

Materials and methods: This study was a cost-effectiveness and cost-utility study in which a Markov model was used. The study used the census method to evaluate 81 patients with MS in Iran, Fars province who were being treated with fingolimod and natalizumab. In this study, costs were collected from the societal perspective, and the outcomes were the mean of relapse avoided rate and QALY. The cost data collection form, Kurtzke Expanded Disability Status Scale, and EQ-5D-3L questionnaire were used to collect the required data.

Results: The results showed that, compared to natalizumab, patients who used fingolimod had decreased costs (58,087 vs 201,707), increased QALYs (8.09 vs 7.37), and a better relapse avoided rate (6.27 vs 5.83) per patient over the lifetime. The results of the sensitivity analysis showed that the results of the study were robust. Also, the results of the scatter plots showed that fingolimod was more cost-effective based on the QALY and relapse avoided rate in 62% and 56%, respectively, of the simulations for the thresholds below $15,657 for the studied patients.

Conclusions: According to the results of this study, the cost-effectiveness and cost-utility of fingolimod were higher than those of natalizumab. Therefore, it is recommended that treatment with fingolimod be the first priority of second-line treatment for MS patients, and policy-makers and health managers are encouraged to make efforts in order to increase insurance coverage and reduce the out-of-pocket payments of these patients.     

Developing a multiple-criteria decision analysis for green economy transition: a Canadian case study

ABSTRACT

Identifying planning strategies for the transition to a green economy is a formidable challenge. We proposed a novel multiple-criteria decision analysis model which can quantitatively identify the socio-economic and environmental impacts of various government and public policies. We applied the model to four practical scenarios in Canada for determining the optimal final demand that maximizes the country's GDP and employment while minimizing GHG emissions for small, short-term changes. As a result, the model suggested potential ways to simultaneously achieve a GDP growth of 2.5 billion CAD and creation of over 25,000 new jobs, and a saving of 2514 kt CO₂. As per the final demand, the electrification of domestic heating and transport should be more promoted. The proposed analysis tool will provide decision-makers with the ability to explore the design and effects of policy reforms, regulatory changes, and targeted public expenditure strategies, thereby overcoming barriers towards a green economy.