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...

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...

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...

We model the complex global dependencies in international financial markets using spatial techniques. Our methodology allows us to go beyond conventional correlation analyses and volatility-spillover models confined to studying pairwise relationships, and improves the accuracy of return...

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning...

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...

The presence of risk premium is an issue that weakens the rational expectation hypothesis. This paper investigates changing behavior of time varying risk premium for holding 10 year maturity bond using a bivariate VARMA-DBEKK-AGARCH-M model. The model allows for asymmetric risk premia, causality...