### Professor Valentina Corradi

### Biography

### Biography

Valentina Corradi obtained a PhD in Economics in 1994 at the University of California, San Diego. She held positions at University of Pennsylvania, Queen Mary-University of London, University of Exeter and University of Warwick.

Her work has been published on Journal of Econometrics, Econometric Theory, Journal of the American Statistical Association, Review of Economic Studies, International Economic Review and Journal of Monetary Economics.

Valentina's current research interests include: (i) modelling and testing for jumps in financial assets (ii) evaluation of trading strategies (iii) financial analysts bias (iv) bandwidth selection for non-stationary processes (v) heaping and measurement error in child mortality data.

### Research interests

- Econometric Theory
- Financial Econometrics
- Time Series: Predictive evaluation
- Realized measures and Jumps
- Data driven procedure for bandwidth selection
- Moment inequalities
- Factor Models
- Conditional CAPM.

### Teaching

- Econometrics for PhDs.

### Departmental duties

- PhD Programme Director.

### My publications

### Publications

component, and is a direct test for Hawkes diffusions (see e.g., Aït-Sahalia, Cacho-Diaz and Laeven (2015)). The limiting distributions of the proposed statistics are analyzed via use of a double asymptotic scheme, wherein the time span goes to infinity and the discrete interval approaches zero; and the distributions of the tests are normal and half normal, respectively. The results from a Monte Carlo study indicate that the tests have good finite sample properties.

the daily quadratic variation, which we estimate using intraday data. To avoid sequential bias distortion, we

do not pretest for the presence of jumps. If the null is true, our test statistic based on daily integrated jumps

weakly converges to a Gaussian random variable if both assets have jumps. If instead at least one asset

has no jumps, then the statistic approaches zero in probability. We show how to compute asymptotically

valid bootstrap-based critical values that result in a consistent test with asymptotic size equal to or smaller

than the nominal size. Empirically, we study jump linkages between US futures and equity index markets.

We find not only strong evidence of jump cross-excitation between the SPDR exchange-traded fund and

E-mini futures on the S&P 500 index, but also that integrated jumps in the E-mini futures during the

overnight period carry relevant information.

The thesis also considers the residential demand for gas. Utilising microdata from the UK Living Costs and Food Survey 2013?2016, an attempt is made to estimate the household demand for gas and to determine the significance of government energy and climate change policies. Fitting the data to the model, a tobit censored regression model is employed to estimate residential gas demand. In addition to policy effects, seasonal, socioeconomic, dwelling type, tenure and heating equipment type effects are considered in the model. It is discovered that price elasticity ranges from -0.246 to ?0.327 for the restricted model and -0.422 to -0.491 for the unrestricted model. The increase in consumer response to price changes can be attributed to government policy.

Finally, it can be concluded that over the short to medium term gas prices are set to remain oil indexed and driven by shocks in the global oil market. Domestically, it is important to identify the different variations in seasonality, housing characteristics, family and income demographics that determine consumer behaviour to better understand the impact of government energy & climate change policies.

The first chapter, investigates the forecast ability of Random Forest, Functional Non-parametric and Dynamic Nelson-siegel models. Results of this study indicate the superiority of the Random Forest model in forecasting short end of the yield curve, and Dynamic Nelson-Siegel model in predicting yields of bonds with long term to maturity. In line with the literature, results recommend the employment of external source of information such as macroeconomic variables.

The second chapter examines the relative ability of Generalized Autoregressive Conditional Heteroskedastic (GARCH) models in volatility prediction. The existing literature produce conflicting results regarding this topic, which is mainly attributed to the choice of loss functions in forecast evaluation in this field. This chapter exploits the recent Robust Forecast Comparison (RFC) test of Jin et al. (2017) to evaluate the out-of-sample forecast performance of 11 GARCH type models. The advantage of using the test of Jin et al. (2017) is that no predetermined form of loss function is required. This study concludes that, on average, all competing models over-predict the volatility, and that the simple GARCH model is outperformed in predicting the volatility.