Paulo Rodrigues

Estimating the macroeconomic effects of active labour market policies using spatial econometric methods

Purpose - The paper aims to present an analysis of the indirect and direct effects of active labour market policy measures at the regional level for Western Germany. Design/methodology/approach - Most evaluation studies of active labour market policy focus on the microeconometric treatment effect using individual data and do not account for possible indirect effects like deadweight and substitution effects. The present study uses a dynamic specification of the augmented matching function at the regional level. A dynamic panel data model is estimated using monthly and regional variation of different labour market programmes as explanatory variables. Furthermore, spatial interactions are taken into account by adding a spatially correlated error term. Findings - Almost no significant negative effects are found of the stock of participants in programmes of labour market policy on the number of outflows from unemployment into regular jobs. Thus, contrary to findings at the individual level, no lock-in effect is found. The number of programme participants does not reduce the number of outflows from unemployment. On the other hand when looking not at the stocks but on the outflows from programmes, no positive effects on outflows from unemployment at the regional level are found. Research limitations/implications - Because of data limitations only a period up to six months after completing a programme is used. Originality/value - The authors distinguish between the effects of the stock of programme participants and of the outflows from programmes. Furthermore, the authors account for spatially correlated error terms by using a GM estimator proposed by Mutl in 2006.

Empirical Analysis of Affine Versus Nonaffine Variance Specifications in Jump-Diffusion Models for Equity Indices

This article investigates several crucial issues that arise when modeling equity returns with stochastic variance. (i) Does the model need to include jumps even when using a nonaffine variance specification? We find that jump models clearly outperform pure stochastic volatility models. (ii) How do affine variance specifications perform when compared to nonaffine models in a jump diffusion setup? We find that nonaffine specifications outperform affine models, even after including jumps.

Stochastic Volatility and Jumps: Exponentially Affine Yes or No? An Empirical Analysis of S&P500 Dynamics

This paper analyzes exponentially affine and non-affine stochastic volatility models with jumps in returns and volatility. Markov Chain Monte Carlo (MCMC) technique is applied within a Bayesian inference to estimate model parameters and latent variables using daily returns from the S&P 500 stock index. There are two approaches to overcome the problem of misspecification of the square root stochastic volatility model. The first approach proposed by Christo ersen, Jacobs and Mimouni (2008) suggests to investigate some non-affine alternatives of the volatility process. The second approach consists in examining more heavily parametrized models by adding jumps to the return and possibly to the volatility process. The aim of this paper is to combine both model frameworks and to test whether the class of affine models is outperformed by the class of non-affine models if we include jumps into the stochastic processes. We conclude that the non-affine model structure have promising statistical properties and are worth further investigations. Further, we find affine models with jump components that perform similar to the non affine models without jump components. Since non affine models yield economically unrealistic parameter estimates, and research is rather developed for the affine model structures we have a tendency to prefer the affine jump diffusion models.

Does the Institutionalization of Derivatives Trading Spur Economic Growth

It is a widespread view that derivatives played a crucial role during the recent financial and economic crisis. This opinion manifested in headlines such as “Why Derivatives Caused Financial Crisis” and derivatives have been termed “Financial Weapons of Mass Destruction”. However, the analysis of the role of derivatives in the economy requires a much more differentiated discussion as the statements given above imply. In this paper we analyze the effect of institutionalization of derivatives trading on economic growth and economic growth volatility; measuring growth in GDP per capita. The relationship between the institutionalization of derivatives trading and economic growth is investigated by using a panel data set comprising of 45 countries observed over 39 years. Our results show a statistically and economically significant positive effect of the establishment and existence of a domestic derivatives exchange on economic growth. These results are robust to different model specifications and to controlling for financial reforms. The effect of institutionalized derivatives trading on growth volatility is analyzed by means of an EGARCH model and is found to be negative and significant.

Dynamic Panel Data Models with Spatial Correlation

Summary This paper presents an overview of recently developed estimation methods for dynamic panel data models with spatial correlation. We discuss the specification, the main assumptions and the implications of the model. The most important estimation strategy is the application of Generalized Method of Moments (GMM). The focus lies on the derivation of the moment conditions, the estimation of the degree of spatial correlation and the specification of the optimal weighting matrix. Finally we estimate an augmented matching function to analyze the effects of active labour market policy programs in Germany using two different weighting schemes.

Out-of-Sample Performance of Jump-Diffusion Models for Equity Indices: What the Financial Crisis Was Good for

Out-of-sample performance of continuous time models for equity returns is crucial in practical applications such as computing risk measures like value at risk, determine optimal portfolios or pricing derivatives. For all these applications investors need to model the return distribution of an underlying at some point in time in the future given current information. In this paper we analyze the out-of-sample performance of exponentially affine and non-affine continuous time stochastic volatility models with jumps in returns and volatility. Our analysis evaluates the density forecasts implied by the models. In a first step, we find in general that the good in-sample fits reported in the related literature do not carry over to the out-of-sample performance. In particular the left tail of the distribution poses a considerable challenge to the proposed models. In a second step, we analyze the models by using a rolling window approach. We find that using estimation periods that include high market stress events improve forecasting power considerably. In a third step, we apply parameters estimated on the sub period including the financial crisis (period with highest market stress) to all other forecasting sub periods. This approach further increases overall forecasting power and results in an outperformance of affine compared to non-affine models and an outperformance of jump models.

Level and Slope of Volatility Smiles in Long-Run Risk Models

We propose a long-run risk model with stochastic volatility, a time-varying mean reversion level of volatility, and jumps in the state variables. The special feature of our model is that the jump intensity is not affine in the conditional variance but driven by a separate process. We show that this separation of jump risk from volatility risk is needed to match the empirically weak link between the level and the slope of the implied volatility smile for S&P 500 options.