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...

In this work we introduce the notion of implied Core Equity Tier 1 volatility and the concept of a risk-adjusted distance to trigger. Using a derivatives-based valuation approach, we are able to derive the implied CET1 volatility from the market price of a CoCo bond in a Black-Scholes setting....

Empirical evidence indicates that trades by institutional investors have sizable effects on asset prices, generating phenomena such as index effects, asset-class effects and others. It is difficult to explain such phenomena within standard representative-agent asset pricing models. In this...

A sharp increase in the popularity of commodity investing in the past decade has triggered an unprecedented inflow of institutional funds into commodity futures markets, referred to as the financialization of commodities. In this paper, we explore the effects of financialization in a model that...

The 27th SUERF Colloquium in Munich in June 2008: New Trends in Asset Management: Exploring the Implications was already topical in the Summer of 2008. The subsequent dramatic events in the Autumn of 2008 made the presentations in Munich even more relevant to investors and bankers that want to...

We examine the role of concurrent information in the striking increase in investor response to earnings announcements from 2001 to 2016, as measured by return variability and volume following Beaver (1968). We find management guidance, analyst forecasts, and disaggregated financial statement...

Recent price surge in commodity markets has stipulated the intensity of various factors which lead the price volatility. There are multiple-factors namely, traditional supply and demand, excess global liquidity (i.e., monetary inflows in commodity markets), and financialization i.e., financial...

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: i) The price impact of liquidity trades is higher in the...

We analyze the impact of high frequency (HF) trading in financial markets based on a model with three types of traders: liquidity traders (LTs), professional traders (PTs), and high frequency traders (HFTs). Our four main findings are: i) The price impact of liquidity trades is higher in the...