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To assess the Bank of England’s Monetary Policy Committee decisions on the official bank rate under forecast uncertainty, I estimate simple forecast-based interest rate rules augmented by the exact forecast standard deviations recovered directly from the Inflation Report fan charts. I find that forecast inflation uncertainty strongly intensifies the reaction of the interest rate decisions to a forecast deviation of inflation from target. Conversely, forecast output growth uncertainty attenuates the reaction of the interest rate decisions to a forecast deviation of output growth from its long-run mean. Asymmetries in forecast uncertainty are highly relevant for inflation. Forecast upward risks to inflation contribute strongly to the intensifying effect of forecast inflation uncertainty, while forecast downward risks have hardly any significant impact. Moreover, I find that forecast risks to inflation have a direct effect on the interest rate decisions, in particular when inflation is forecast close to target. Uncertainty forecasts obtained from the Survey of External Forecasters, though, contain no explanatory power.

Intra-daily financial durations time series typically exhibit evidence of long range dependence. This has motivated the introduction of models able to reproduce this stylized fact, like the Fractionally Integrated Autoregressive Conditional Duration Model. In this work we introduce a novel specification able to capture long range dependence. We propose a three component model that consists of an autoregressive daily random effect, a semiparametric time-of-day effect and an intra-daily dynamic component: the Mixed Autoregressive Conditional Duration (Mixed ACD) Model. The random effect component allows for heterogeneity in mean reversal within a day and captures low frequency dynamics in the duration time series. The joint estimation of the model parameters is carried out using MCMC techniques based on the Bayesian formulation of the model. The empirical application to a set of widely traded US tickers shows that the model is able to capture low frequency dependence in duration time series. We also find that the degree of dependence and dispersion of low frequency dynamics is higher in periods of higher financial distress.

The paper presents an alternative real time adaptive learning algorithm in the presence of signal-to-noise ratio uncertainty. The main innovation of this algorithm is that it uses a gain which is determined within the model: it continuously depends on the extent of misevaluation of parameters embedded in the forecast error. We show that in the presence of signal-to-noise ratio misevaluation, the usage of the proposed learning algorithm is a significant improvement on the Kalman Filter learning algorithm. In a full information case, the Kalman Filter learning algorithm is still the optimal tool.

We use transfer entropy to quantify information flows between financial markets and propose a suitable bootstrap procedure for statistical inference. Transfer entropy is a model-free measure designed as the Kullback-Leibler distance of transition probabilities. Our approach allows to determine, measure and test for information transfer without being restricted to linear dynamics. In our empirical application, we examine the importance of the credit default swap market relative to the corporate bond market for the pricing of credit risk. We also analyze the dynamic relation between market risk and credit risk proxied by the VIX and the iTraxx Europe, respectively. We conduct the analyses for pre-crisis, crisis and post-crisis periods.

Portfolio risk estimation requires appropriate modeling of fat-tails and asymmetries in dependence in combination with a true downside risk measure. In this survey, we discuss computational aspects of a Monte Carlo based framework for risk estimation and risk capital allocation. We review different probabilistic approaches focusing on practical aspects of statistical estimation and scenario generation. We discuss value-at-risk and conditional value-at-risk and comment on the implications of using a fat-tailed Monte Carlo framework for the reliability of risk estimates including model risk and Monte Carlo variability.