We provide evidence that modelling both fat tails and stochastic volatility are important in improving in-sample fit and out-of-sample forecasting performance. To show this, we construct a VAR model where the orthogonalised shocks feature Student's t distribution as well as time-varying variance. We estimate the model using US data on industrial production growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference is especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student's t-distributed disturbances may lead to improved forecast accuracy.
We provide evidence that modelling both fat tails and stochastic volatility are important in improving in-sample fit and out-of-sample forecasting performance. To show this, we construct a VAR model where the orthogonalised shocks feature Student's t distribution as well as time-varying variance. We estimate the model using US data on industrial production growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference is especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student's t-distributed disturbances may lead to improved forecast accuracy.
In this paper we develop an index to monitor the intensity of financial stress in the UK over a period of 45 years. By aggregating various market-based indicators of financial stress from six major markets, we allow each indicator to be assessed in terms of its systemic importance. This enables the index to capture the interconnectedness of financial markets. The index successfully captures three episodes of heightened stress in UK financial history. We also attempt to determine how much a financial shock to the UK economy is amplified in a period of stress vis-à-vis a tranquil period. It involves exploring the dynamic relationship of the index with the UK real economy by two specifications of threshold vector auto-regression models. We find empirical evidence for the existence of feedback loops in the shock propagation between the real and the financial sector in the United Kingdom.
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