Abstract:

Regulators charged with monitoring systemic risk need to focus on sentiment as well as narrowly defined measures of systemic risk. This paper describes techniques for jointly monitoring the co-evolution of sentiment and systemic risk, proxied by Expected Marginal Shortfall. To measure sentiment, we apply a behavioral extension of traditional pricing kernel theory, which we supplement with external proxies. We illustrate the technique by analyzing the dynamics of sentiment before, during, and after the global financial crisis which erupted in September 2008.Using stock and options data for the S&P 500 during the period 2002–2009, our analysis documents the statistical relationship between sentiment and systemic risk. The misperception of risk by the representative investor causes a reversal of the perceived risk-expected return trade-off.

Abstract:

We present a simple model of diffusions coupled through their drifts in a way that each component mean-reverts to the mean of the ensemble. In particular, we are interested in the number of components reaching a "default" level in a given time. This coupling creates stability of the system in the sense that there is a large probability of "nearly no default". However, we show that this "swarming" behavior also creates a small probability that a large number of components default corresponding to a "systemic risk event". The goal is to illustrate systemic risk with a toy model of lending and borrowing banks, using mean-field limit and large deviation estimates for a simple linear model.

Abstract:

Following the global financial crisis, a new macro environment has emerged. It is characterized by lower returns and greater volatility. This has become known as the "new normal" environment. This macro environment points to pressure on top line earnings, which is compounded by the additional pressure that is coming out of new regulations and indirect intervention as governments play a more active role in the sector and the broader economy. Our best defense against the “new normal” environment is to maintain a strong balance sheet and to target resilient earnings. The objective of this presentation is to showcase a holistic view of the balance sheet and the resultant earnings streams. The aim is to optimise the balance sheet management framework - to structure the assets, liabilities and capital in such a way that it protects and enhances the financial performance, and finally, outperforms the macro factors that typically drive business.

Abstract:

What is the value of a security, and how do we know? The financial crisis made evident the intrinsic limitation and subjectivity of our models and pricing assumptions. It also highlighted the need for pricing complex risks such as counterparty exposures. Prior to 2007, the credit valuation adjustment (CVA) was generally ignored or too small to be noticed by most participants. Today it has become an integral part of accounting rules and Basel III. Although conceptually simple, calculating the CVA of a typical portfolio is akin to pricing a very complex "illiquid" instrument, and cannot be achieved with the same accuracy as standard (liquid) derivatives pricing. Furthermore accounting rules, banking regulation and industry best practices differ substantially.

In this talk, we discuss several lessons and best practices that we are relearning as the global banking system copes with the legacy of an ever extending crisis. When liquidity is thin, dealer quotes are unreliable and model parameters cannot be estimated based only on observed market prices. As liquidity dries up, market participants are generally left in the dark, not able to reliably determine the value the portfolios or to analyse their risk. Models are most necessary in these situations, yet we must acknowledge the necessary "heroic" assumptions embedded in them and the limitation of the information which we can reasonably extract from the market. In practice, we must effectively incorporate fundamental market and credit information, historical data and expert judgment into the process. When dealing with complex structures and markets with limited liquidity, it is important to understand the meaning and use of a price. Given the inevitable limitations of our models, we stress the importance of a model risk and scenario analysis framework, which help us understand better the behavior of instruments and portfolios, together with their risks and the "Knightean" uncertainties we could be facing.

Abstract:

Al margen del marco regulatorio, se expone un caso práctico de gestión de balance, con especial énfasis en el riesgo estructural de liquidez. La gestión de los riesgos estructurales se contempla desde una visión global, al objeto de optimizar la estructura de balance, la cobertura de los riesgos y la sostenibilidad del margen. Para ello, se presentan las principales medidas de gestión del ALCO, así como las funciones que las diferentes unidades prestan en la toma y ejecución de decisiones.

Abstract:

Classical Portfolio Choice Theory may be extended so as to involve Generalized Sharpe Ratios, i.e., return/risk ratios such that the role of the standard deviation is played by a Coherent Measure of Risk.

Surprisingly, if one deals with a Complete Pricing Model (Black and Scholes, Heston, other Stochastic Volatility Models, etc.), then for every Coherent and Expectation Bounded Risk Measure (DPT, CVaR, Wang, etc.) the Generalized Sharpe ratio is unbounded and cannot be optimized. In other words, the Sharpe ratio may become as large as desired, and it clearly outperforms the Market Portfolio Generalized Sharpe ratio. Furthermore, this phenomenon also holds if the risk is measured with the Value at Risk, despite the fact that this measure of risk is not coherent.

From a practical point of view, the obvious interpretation is that traders might reach the desired expected return with zero risk.

This talk deals with this paradox, some possible theoretical solutions to this caveat, and the empirical performance of those sequences of portfolios whose Generalized Sharpe ratio tends to infinite.