Davide Raggi

Long memory and nonlinearities in realized volatility: A Markov switching approach

Realized volatility is studied using nonlinear and highly persistent dynamics. In particular, a model is proposed that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. Inference is based on an efficient Markov chain Monte Carlo (MCMC) algorithm that is used to estimate parameters, latent process and predictive densities. The in-sample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results at several forecast horizons show that introducing these nonlinearities produces superior forecasts over those obtained using nested models.

Comparing stochastic volatility models through Monte Carlo simulations

Stochastic volatility models are important tools for studying the behavior of many financial markets. For this reason a number of versions have been introduced and studied in the recent literature. The goal is to review and compare some of these alternatives by using Bayesian procedures. The quantity used to assess the goodness-of-fit is the Bayes factor, whereas the ability to forecast the volatility has been tested through the computation of the one-step-ahead value-at-risk (VaR). Model estimation has been carried out through adaptive Markov chain Monte Carlo (MCMC) procedures. The marginal likelihood, necessary to compute the Bayes factor, has been computed through reduced runs of the same MCMC algorithm and through an auxiliary particle filter. The empirical analysis is based on the study of three international financial indexes.

MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model

In this paper we present a stochastic volatility model assuming that the return shock has a Skew-GED distribution. This allows a parsimonious yet flexible treatment of asymmetry and heavy tails in the conditional distribution of returns. The Skew-GED distribution nests both the GED, the Skew-normal and the normal densities as special cases so that specification tests are easily performed. Inference is conducted under a Bayesian framework using Markov Chain MonteCarlo methods for computing the posterior distributions of the parameters. More precisely, our Gibbs-MH updating scheme makes use of the Delayed Rejection Metropolis-Hastings methodology as proposed by Tierney and Mira (1999), and of Adaptive-Rejection Metropolis sampling. We apply this methodology to a data set of daily and weekly exchange rates. Our results suggest that daily returns are mostly symmetric with fat-tailed distributions while weekly returns exhibit both significant asymmetry and fat tails.

Investigating asymmetry in US stock market indexes: evidence from a stochastic volatility model

This study provides empirical evidence on asymmetry in financial returns using a simple stochastic volatility model which allows a parsimonious yet flexible treatment of both skewness and heavy tails in the conditional distribution of returns. In particular, it is assumed that returns have a Skew-GED conditional distribution. Inference is conducted under a Bayesian framework using Markov Chain Monte Carlo methods for estimating the properties of the posterior distributions of the parameters. One is also able to perform some specification testing via Bayes factors. The data set consists of daily and weekly returns on the DJ30, S&P500 and Nasdaq US stock market indexes. The estimation results are consistent with the presence of substantial asymmetry and heavy tails in the distribution of US stock market indexes.

Can Equity Enhance Efficiency? Lessons from the Kyoto Protocol

This paper analyses the relationship between different equity rules and the incentives to sign and ratify a climate agreement. A widespread conjecture suggests that a more equitable ex-ante distribution of the burden of reducing emissions would provide the right incentives for more countries - particularly big emitters - to accept an emission reduction scheme defined within an international climate agreement. This paper shows that this conjecture is only partly supported by the empirical evidence that can be derived from the Kyoto Protocol. Even though more equitable burden sharing rules provide better incentives to sign and ratify a climate agreement than the burden-sharing rule implicit in the Kyoto Protocol, a stable global agreement cannot be achieved. A possible strategy to achieve a global agreement without free-riding incentives is a policy mix in which global emission trading is coupled with a transfer mechanism designed to offset ex-post incentives to free ride.

Can Equity Enhance Efficiency? Some Lessons from Climate Negotiations

This Paper analyses the relationship between different equity rules and the incentives to sign and ratify a climate agreement. A widespread conjecture suggests that a more equitable distribution of the burden of reducing emissions would enhance the incentives for more countries – particularly big emitters – to accept an emission reduction scheme defined within an international climate agreement. This Paper shows that this conjecture is only partly supported by the empirical evidence that can be derived from the recent outcomes of climate negotiations. Even though an equitable sharing of the costs of controlling GHG emissions can provide better incentives to sign and ratify a climate agreement than the burden sharing implicit in the Kyoto agreement, a stable global agreement cannot be achieved. A possible strategy to achieve a global agreement without free-riding incentives is a policy mix in which global emission trading is coupled with a transfer mechanism designed to offset incentives to free ride.

POLICY RULES, REGIME SWITCHES, AND TREND INFLATION: AN EMPIRICAL INVESTIGATION FOR THE UNITED STATES

This paper estimates Taylor rules featuring instabilities in policy parameters and switches in policy shocks’ volatility for the post-World War II (WWII) U.S. economy. We contrast a rule embedding a fixed-inflation target with another featuring trend inflation, i.e., a time-varying inflation target. The rule embedding trend inflation turns out to be (a) empirically superior according to a marginal likelihood-based comparison and (b) more able to pin down some relevant episodes of the post-WWII U.S. monetary policy history. Estimates conducted with Greenbook data confirm the empirical superiority of the rule featuring a time-varying inflation target. A comparison with recently published estimates of trend inflation is also conducted.

The Lemons Problem in a Labor Market with Intrinsic Motivation: When Higher Salaries Pay Worse Workers

We study the Lemons Problem when workers have private information on both their skills and their intrinsic motivation. When workers are motivated, ine¢ ciencies due to adverse selection are mitigated and a change in salaries may have unexpected consequences. With a su¢ ciently strong and positive association between motivation and productivity, a wage increase may attract less motivated and also less productive workers. When the association is positive but small, it instead may attract more productive and also more motivated workers. Our theoretical analysis reconciles contrasting empirical evidence on vocational sectors such as for public servants, teachers, health professionals and politicians. Our results also inform the current policy debate on whether it is possible to improve the overall quality of workers by changing their salary.

Volatility, Jumps and Predictability of Returns: a Sequential Analysis

In this article we propose a Monte Carlo algorithm for sequential parameter learning for a stochastic volatility model with leverage, nonconstant conditional mean and jumps. We are interested in estimating the time invariant parameters and the nonobservable dynamics involved in the model. Our simple but effective idea relies on the auxiliary particle filter algorithm mixed together with the Markov Chain Monte Carlo (MCMC) methodology. Adding an MCMC step to the auxiliary particle filter prevents numerical degeneracies in the sequential algorithm and allows sequential evaluation of the fixed parameters and the latent processes. Empirical evaluation on simulated and real data is presented to assess the performance of the algorithm. A numerical comparison with a full MCMC procedure is also provided. We also extend our methodology to superposition models in which volatility is obtained by a linear combination of independent processes.