Using high frequency data, we have studied empirically the change of volatility, also called volatility derivative, for various time horizons. In particular, the correlation between the volatility derivative and the volatility realized in the next time period is a measure of the response...

We show that a class of microeconomic behavioral models with interacting agents, derived from Kirman (1991, 1993), can replicate the empirical long-memory properties of the two first conditional moments of financial time series. The essence of these models is that the forecasts and thus the...

This paper presents a novel method for estimating the likely welfare effects of competition reforms for both current and new consumers. Using household budget survey data for 2015/16 for Ethiopia and assuming a reform scenario that dilutes the market share of the state-owned monopoly to 45...

Hedging of illiquid financial instruments is carried out with liquid instruments that, as a rule, have simpler payoff functions. For example, hedging of Asian or long-dated put options is carried out with vanilla puts, hedging of Bermuda swaptions is done with vanilla swaptions, etc. This kind...

We present an embarrassingly simple method for supervised learning of SABR model's European option price function based on lookup table or rote machine learning. Performance in time domain is comparable to generally used analytic approximations utilized in financial industry. However, unlike the...

Artificial Neural Networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between...

Newspapers, movie tickets, and concession stand items typically charge prices that facilitate rapid, simple transactions: their prices often coincide with available monetary units, require few pieces of money, or require little change. In this sense, these prices are more convenient than other...

In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate...

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We...

Potential Future Exposure (PFE) is a standard risk metric for managing business unit counterparty credit risk but there is debate on how it should be calculated. The debate has been whether to use one of many historical ("physical") measures (one per calibration setup), or one of many...