Modelling good and bad volatility

The returns of many financial assets show significant skewness, but in the literature this issue is only marginally dealt with. Our conjecture is that this distributional asymmetry may be due to two different dynamics in positive and negative returns. In this paper we propose a process that allows the simultaneous modelling of skewed conditional returns and different dynamics in their conditional second moments. The main stochastic properties of the model are analyzed and necessary and sufficient conditions for weak and strict stationarity are derived. An application to the daily returns on the principal index of the London Stock Exchange supports our model when compared to other frequently used GARCH-type models, which are nested into ours.Volatility, Skewness, GARCH, Asymmetric Dynamics, Stationarity

State Space Methods in Ox/SsfPack

The use of state space models and their inference is illustrated using the package SsfPack for Ox. After a rather long introduction that explains the use of SsfPack and many of its functions, four case-studies illustrate the practical implementation of the software to real world problems through short sample programs. The first case consists in the analysis of the well-known (at least to time series analysis experts) Nile data with a local level model. The other case-studies deal with ARIMA and RegARIMA models applied to the (also well-known) Airline time series, structural time series models applied to the Italian industrial production index and stochastic volatility models applied to the FTSE100 index. In all applications inference on the model (hyper-) parameters is carried out by maximum likelihood, but in one case (stochastic volatility) also an MCMC-based approach is illustrated. Cubic splines are covered in a very short example as well.

Business cycle and sector cycles

A methodology based on the multivariate generalized Butterwoth filter for extracting the business cycles of the whole economy and of its productive sectors is developed. The method is then illustrated through an application to the Italian gross value added time series of the main economic sectors.Business cycle, Butterworth filter, Unobserved components, Kalman Filter

Time Series Modeling with Duration Dependent Markov-Switching Vector Autoregressions: MCMC Inference, Software and Applications

Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes, which switches according to a two-state Markov chain with transition probabilities depending on how long the process has been in a state. In the present paper I propose a MCMC-based methodology to carry out inference on the model's parameters and introduce DDMSVAR for Ox, a software written by the author for the analysis of time series by means of DDMS-VAR models. An application of the methodology to the U.S. business cycle concludes the article.Markov-switching, Business cycle, Gibbs sampling, Duration dependence, Vector autoregression

Milan’s Cycle as an Accurate Leading Indicator for the Italian Business Cycle

A coincident business cycle indicator for the Milan area is built on the basis of a monthly industrial survey carried out by Assolombarda, the largest territorial entrepreneurial association in Italy. The indicator is extracted from three time series concerning the production level and the internal and foreign order book as declared by some 250 Assolombarda associates. This indicator is potentially very valuable in itself, being Milan one of the most dynamic economic systems in Italy and Europe, but it becomes much more interesting when compared to the Italian business cycle as extracted form the Italian industrial production index. Indeed, notwithstanding the deep differences in the nature of the data, the indicator for Milan has an extremely high coherence with the Italian cycle and the former leads the latter by approximately 4-5 months. Furthermore there is a direct relation between the amplitude of the cycle and the leading time of the Milan indicator.Leading indicator, unobserved components model, structural time series model, local business survey

A robust version of the KPSS test based on ranks

This paper proposes a test of the null hypothesis of stationarity that is robust to the presence of fat-tailed errors. The test statistic is a modified version of the KPSS statistic, in which ranks substitute the original observations. The rank KPSS statistic has the same limiting distribution as the standard KPSS statistic under the null and diverges under I(1) alternatives. It features good power both under thin-tailed and fat-tailed distributions and it turns out to be a valid alternative to the original KPSS and the recently proposed Index KPSS (de Jong et al. 2007).Stationarity testing, Time series, Robustness, Rank statistics, Empirical processes

Dynamic Conditional Correlation with Elliptical Distributions

The Dynamic Conditional Correlation (DCC) model of Engle has made the estimation of multivariate GARCH models feasible for reasonably big vectors of securities’ returns. In the present paper we show how Engle’s multi-step estimation of the model can be easily extended to elliptical conditional distributions and apply different leptokurtic DCC models to twenty shares listed at the Milan Stock Exchange.Multivariate GARCH, Correlation, Elliptical distributions, Fat Tails

Dynamic Conditional Correlation with Elliptical Distributions

The Dynamic Conditional Correlation model of Engle has made the estimation of multivariate GARCH models feasible for reasonably big vectors of securities’ returns. In the present paper we show how Engle’s twosteps estimate of the model can be easily extended to elliptical conditional distributions and apply different leptokurtic DCC models to some stocks listed at the Milan Stock Exchange. A free software written by the authors to carry out all the required computations is presented as well.Multivariate GARCH, Dynamic conditional correlation, Generalized method of moments

Estimating Marginal Costs and Market Power in the Italian Electricity Auctions

In this paper we examine the bidding behaviour of firm competing in the Italian wholesale electricity market where generators submit hourly supply schedule to sell power. We describe the institutional characteristics of the Italian market and derive generators' equilibrium bidding functions. We also discuss the main empirical strategies followed by the recent econometrical literature to obtain estimates of (unobservable) optimal bids. Then, we use individual bid data, quantity volumes and other control variables to compare actual bidding behaviour to theoretical benchmarks of profit maximization. We obtain estimates of generators' costs to be used in conjunction with hourly market equilibrium prices to derive some measures of the extent of market power in the Italian electricity sector and of its exploitation by firms.Bidding behaviour in Electricity markets, Estimates of optimal bid functions, Measures of market power

Visual object imagery and autobiographical memory: object imagers are better at remembering their personal past

In the present study we examined whether higher levels of object imagery, a stable characteristic that reflects the ability and preference in generating pictorial mental images of objects, facilitate involuntary and voluntary retrieval of autobiographical memories (ABMs). Individuals with high (High-OI) and low (Low-OI) levels of object imagery were asked to perform an involuntary and a voluntary ABM task in the laboratory. Results showed that High-OI participants generated more involuntary and voluntary ABMs than Low-OI, with faster retrieval times. High-OI also reported more detailed memories compared to Low-OI and retrieved memories as visual images. Theoretical implications of these findings for research on voluntary and involuntary ABMs are discussed