Financial contagion and systemic risk measures are commonly derived from conditional quantiles by using imposed model assumptions such as a linear parametrization. In this paper, we provide model free measures for contagion and systemic risk which are independent of the specifcation of...

This paper introduces a generalization of quantiles, order statistics, and concomitants that we term co-quantiles, and investigates their statistical properties. The probability density functions for the co-quantiles are obtained along with their moments under the assumption that the...

This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and...

In this paper we discuss the required return on equity for a simple project with a finite life. To determine a project's cost of equity, it is quite common to use Modigliani and Miller's ‘Proposition II' (1963). However, if the assumptions of MM do not hold, ‘Proposition II' will lead to...

In this paper, an attempt is made to examine the relationship between inflation and stock returns in India using spectral and time-frequency methods. Scale specific relation between inflation and stock returns is unraveled, allowing us to capture the relationship at varying investment horizons....

This paper examines overreaction hypothesis in four emerging Balkan stock markets (Bulgaria, Romania, Croatia, Turkey), using average returns of four developed markets (US, UK, Germany and Greece), during the period 2000-2007. The hypothesis tested is that developed market movements create...

Subordination is an often used stochastic process in modeling asset prices. Subordinated Levy price processes and local volatility price processes are now the main tools in modern dynamic asset pricing theory. In this paper, we introduce the theory of multiple internally embedded financial...

We present a framework for modeling and estimating dynamics of variance and skewness from time-series data using a maximum likelihood approach assuming that the errors from the mean have a non-central conditional t distribution. We parameterize conditional variance and conditional skewness in an...