Tests For Volatility Dynamics In Bivariate Stochastic Volatility Models
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Tests for Volatility Dynamics in Bivariate Stochastic Volatility Models
Recently Chiba and Kobayashi (2013) have proposed the Lagrange multiplier (LM) test for the null hypothesis that volatilities of two asset return processes are driven by only one stochastic volatility (SV) process in a bivariate SV model. They apply their LM test to Asian stock market index returns, and find that the null hypothesis is not rejected for several pairs of those returns. They, however, derive the test statistic under an unconventional assumption that the conditional distribution of log squared return is normally distributed conditional on the SV, which seems inappropriate for a model of financial assets returns. This paper develops their analysis in two aspects. First, by a method of simulation, we examine the performance of their LM test under a conventional (bivariate) SV model in that each return (not log squared return) follows a normal distribution conditional on the SV. Second, because the null hypothesis examined in Chiba and Kobayashi (2013) is restrictive for many financial time series, we propose two LM tests for less restrictive but plausible null hypotheses. Our simulation study demonstrates that even for the conventional SV model, the actual size of their LM test is reasonably close to the nominal sizes and its power is high enough so that their LM test is useful for practical applications. Moreover, our empirical analysis with newly proposed LM tests finds that for many Asian stock market index returns, although their volatility processes themselves are not identical, they are likely to be driven by a common (or perfectly positively correlated) shock.
Systemic Risk Tomography
In April 2010 Europe was shocked by the Greek financial turmoil. At that time, the global financial crisis, which started in the summer of 2007 and reached systemic dimensions in September 2008 with the Lehman Brothers' crash, took a new course. An adverse feedback loop between sovereign and bank risks reflected into bubble-like spreads, as if financial markets had received a wake-up call concerning the disregarded structural vulnerability of economies at risk.These events inspired the SYRTO project to "think and rethink the economic and financial system and to conceive it as an "ensemble of Sovereigns and Banks with other Financial Intermediaries and Corporations. Systemic Risk Tomography: Signals, Measurement and Transmission Channels proposes a novel way to explore the financial system by sectioning each part of it and analyzing all relevant inter-relationships. The financial system is inspected as a biological entity to identify the main risk signals and to provide the correct measures of prevention and intervention. - Explores the economic and financial system of Sovereigns, Banks, other Financial Intermediaries, and Corporations - Presents the financial system as a biological entity to be explored in order to identify the main risk signals and provide the right measures of prevention and interventions - Offers a new, systemic-based approach to construct a hierarchical, internally coherent framework to be used in developing an effective early warning system
Handbook of Economic Forecasting
Section headings in this handbook include: 'Forecasting Methodology; 'Forecasting Models'; 'Forecasting with Different Data Structures'; and 'Applications of Forecasting Methods.'.