Federico Nardari

Markov chain Monte Carlo methods for stochastic volatility models

This paper is concerned with simulation-based inference in generalized models of stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of freedom) and exogenous variables in the observation and volatility equations and a jump component in the observation equation. By building on the work of Kim, Shephard and Chib (Rev. Econom. Stud. 65 (1998) 361), we develop efficient Markov chain Monte Carlo algorithms for estimating these models. The paper also discusses how the likelihood function of these models can be computed by appropriate particle filter methods. Computation of the marginal likelihood by the method of Chib (J. Amer. Statist. Assoc. 90 (1995) 1313) is also considered. The methodology is extensively tested and validated on simulated data and then applied in detail to daily returns data on the S&P 500 index where several stochastic volatility models are formally compared under different priors on the parameters.

Daily Cross-border Equity Flows: Pushed or Pulled?

In a model that is consistent with the existence of a home bias and with foreign investors that are less informed than domestic investors, we show that unexpectedly high worldwide returns lead to net equity inflows into small countries. In addition, a small country experiences net equity inflows when its stocks earn unexpectedly high returns. We investigate these predictions using daily data on net equity flows for nine emerging market countries and find that equity flows are positively related to host country stock returns as well as market performance abroad. Both our theoretical model and our empirical analysis show that global stock return performance is an important factor in understanding equity flows.

Investor Behavior in the Mutual Fund Industry: Evidence from Gross Flows

Using a large sample of monthly gross flows from 1997 to 2003, we uncover several previously undocumented regularities in investor behavior. First, investor purchases and sales produce fund-level gross flows that are highly persistent. Persistence in fund flows dominates performance as a predictor of future fund flows. More importantly, failing to account for flow persistence leads to incorrect inferences with respect to the relation between performance and flows. Second, we document that investors react differently to performance depending on the type of fund, and that investor trading activity produces meaningful differences in the persistence of fund flows across mutual fund types. Third, at least some investors appear to evaluate and respond to mutual fund performance over much shorter time spans than previously assessed. Additionally, we document differences in the speed and magnitude of investors’ purchase and sales responses to performance.

Investors Do Respond to Poor Mutual Fund Performance: Evidence from Inflows and Outflows

We examine the relation between mutual fund performance and gross flows for a large sample of actively managed U.S. mutual funds. Unlike previous studies that have only examined periods of generally increasing net flows, our sample includes periods of both increasing and decreasing net flows. We find that outflows are related to performance, with investors withdrawing money from poor performers. We also find that outflows and inflows respond asymmetrically to performance, outflows increase more aggressively following poor performance, and inflows increase more aggressively following good performance. Additionally, we find a symmetric performance net flow relation.

Bayesian Analysis of Linear Factor Models with Latent Factors, Multivariate Stochastic Volatility, and Apt Pricing Restrictions

We analyze a new class of linear factor models in which the factors are latent and the covariance matrix of excess returns follows a multivariate stochastic volatility process. We evaluate cross-sectional restrictions suggested by the arbitrage pricing theory (APT), compare competing stochastic volatility specifications for the covariance matrix, and test for the number of factors. We also examine whether return predictability can be attributed to time-varying factor risk premia. Analysis of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) methods. We find that three latent factors with multivariate stochastic volatility best explain excess returns for a sample of 10 size decile portfolios. The data strongly favor models constrained by APT pricing restrictions over otherwise identical unconstrained models.

Are Emerging Markets More Profitable? Implications for Comparing Weak and Semi-Strong Form Efficiency

Using data from 56 markets, we find that short-term reversal, post-earnings drift, and momentum strategies earn similar profits in emerging and developed markets. Portfolio-level variance ratios and market delay measures show greater deviations from efficiency in developed markets and firm-level variance ratios are similar across emerging and developed markets. Conceptually, we show that efficiency tests can yield misleading inferences because they do not control for the information environment. Our evidence corrects misperceptions that emerging markets feature larger trading profits and higher return autocorrelation, highlights crucial limitations of weak and semi-strong form efficiency measures, and points to the importance of measuring informational aspects of efficiency.

Does the Choice of Model or Benchmark Affect Inference in Measuring Mutual Fund Performance

We address the practical question of whether investors and researchers are likely to make invalid inferences about fund manager performance when using the wrong model and/or benchmark. We consider three well-known models, those of Jensen (1968), Treynor and Mazuy (1966), and Henriksson and Merton (1981), and two commonly used timing benchmarks, the SP (2) but biases in measures of overall performance are economically insignificant; (3) benchmark misspecification results in qualitatively similar difficulties, with the addition that overall performance as well can be biased; and (4) model and benchmark misspecification do not appreciably alter the power to detect ability and distinguish a good fund from a bad fund. These results are robust to alternative asset pricing specifications, alternative simulation schemes, varying length of the return series, and periodicity of the simulated series. The use of daily fund returns amplifies our conclusions about the biases induced by model misspecifications. Moreover, the biases we identify appear to be difficult to correct by using standard model selection criteria and misspecification tests. If the benchmark is known but the timing model is not, investors should use measures of overall performance to evaluate funds and managers.

Do Model and Benchmark Specification Error Affect Inference in Measuring Mutual Fund Performance

This paper examines the implications for mutual fund performance measurement of two likely sources of specification error. We compare three well-known models, those of Jensen (1968), Treynor and Mazuy (1966), and Henriksson and Merton (1981), and two commonly-used timing benchmarks, the SP (2) benchmark misspecification results in qualitatively similar inferences, although statistical significance is not as strong; and (3) the power of detecting ability for an individual fund or for distinguishing between a good fund from a bad fund is typically quite low and such power is not appreciably altered by model and benchmark misspecification. These results are robust to alternative asset pricing specifications (CAPM versus Carhart 4-factor) and the periodicity of the simulation (calibrated to daily versus monthly data).

Do Commodities Add Economic Value in Asset Allocation? New Evidence from Time-Varying Moments

We conduct a comprehensive out-of-sample assessment of the economic value adding of commodities in multiasset investment strategies that exploit the predictability of asset return moments. We find that predictability makes the inclusion of commodities profitable even when short selling and high leverage are not permitted. For instance, a mean-variance (non-mean-variance) investor with moderate risk aversion and leverage, rebalancing quarterly, would be willing to pay up to 108 (155) basis points per year after transaction cost for adding commodities to her stock, bond, and cash portfolio. Previous research had reached mixed or even opposite conclusions, especially in an out-of-sample context.