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MASSIMO GUIDOLIN

Publications

My books are here.

Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach (forthcoming)
MASSIMO GUIDOLIN, Erwin Hansen, and Manuela Pedio
Journal of Financial Markets, forthcoming

Journal of Financial Markets

The recent U.S. subprime crisis provides us with a perfect framework to study cross-asset contagion mechanisms in the U.S. financial markets. Specifically, we look at how and to what extent a negative shock that initially occurred in the asset-backed security (ABS) low-quality market propagated to ABS higher grade, Treasury repos, Treasury note, corporate bond, and stock markets. We rely on dynamic time series models estimated with Bayesian methods to capture the (potentially) time-varying relation among the different financial markets. We provide evidence of structural changes in the cross-asset relationships and therefore of contagion. Moreover, by observing the impulse response functions of the models, we conclude that contagion mainly occurred through the flight-to-liquidity, risk premium, and the correlated information channels.



Last change 11/06/2019

Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson-Siegel Models (forthcoming)
GUIDOLIN MASSIMO and Manuela Pedio
Journal of Economic Dynamics and Control, forthcoming

Journal of Economic Dynamics and Control

We use 1982–2014 data on the US riskless yield curve to show that regime switching dynamics in Nelson-Siegel factor models extended to encompass variables that summarize the state of monetary policy lead to superior predictive accuracy. Such spread in forecasting power turns out to be statistically significant even controlling for parameter uncertainty and sample variation. Exploiting regimes, we obtain evidence that the increase in predictive accuracy is stronger during the Great Financial Crisis, when monetary policy underwent a significant, sudden shift. Although more caution applies in comparisons to a naïve random walk benchmark over a few sub-samples and when transaction costs are accounted for, we report that the increase in predictive power owed to the combination of regimes and of variables that capture the stance of unconventional monetary policies is tradeable. We devise and test butterfly strategies that exploit the forecasts from the models and obtain evidence of risk-adjusted profits both per se and in comparisons to simpler models.



Last change 06/11/2019

Modeling systemic risk with Markov Switching Graphical SUR models (10/06/2019)
MASSIMO GUIDOLIN, Daniele Bianchi, Monica Billio, and Roberto Casarin
Journal of Econometrics, 2019, Vol. 210, pages 58-74

Journal of Econometrics

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on the basis of a novel weighted eigenvector centrality measure. An empirical application to the constituents of the S&P100 index shows that cross-firm connectivity significantly increased over the period 1999–2003 and during the financial crisis in 2008–2009. Finally, we provide evidence that firm-level centrality does not correlate with market values and it is instead positively linked to realized financial losses.



Last change 11/06/2019

Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis (24/01/2018)
MASSIMO GUIDOLIN, G. Dalpra, M. Pedio, and F. Vasile
Journal of Portfolio Management, 2018, vol. 44, issue 3, pages 10-24.

The Journal of Portfolio Management: 44 (3)

The authors analyze the out-of-sample performance of asset allocation decisions based on financial ratio predictability of aggregate stock market returns under linear and regime-switching models. The authors adopt both a statistical perspective to analyze whether models based on valuation ratios can forecast excess equity returns, and an economic approach that turns predictions into portfolio strategies. These consist of a portfolio switching approach, a mean-variance framework, and a long-run dynamic model. The authors find a disconnect between the statistical perspective, whereby the ratios yield a modest forecasting power, and a portfolio approach, by which a moderate predictability is often sufficient to yield significant portfolio outperformance, especially before transaction costs and when regimes are taken into account. However, also when regimes are considered, predictability gives high payoffs only to long horizon, highly risk-averse investors. Moreover, different strategies deliver different performance rankings across predictors.



Last change 11/06/2019

Predictions of short-term rates and the expectations hypothesis (10/2018)
MASSIMO GUIDOLIN and D. Thornton
International Journal of Forecasting, 34(4), pp. 636-664.

International Journal of Forecasting

This paper emphasizes that traditional tests of the EH are based on two assumptions: the expectations hypothesis (EH) per se and an assumption about the expectations generating process (EGP) for the short-term rate. Arguing that conventional tests of the EH need to assume EGPs that may be significantly at odds with the true EGP, we investigate this possibility by analyzing the out-of-sample predictive performances of several models for predicting interest rates, including a few models which assume that the EH holds in its functional form that relates long- to short-term yields. Using US riskless yield data for a 1970–2016 monthly sample and testing methods that take into account the parameter uncertainty, the null hypothesis of an equal predictive accuracy of each model relative to the random walk alternative is hardly ever rejected at intermediate and long horizons. This confirms that, at least at a practical level, the main difficulty with the EH is represented by the effective prediction of short-term rates. We discuss the relevance of these findings for central banks’ use of forward guidance.



Last change 03/02/2019

Can No-Arbitrage SDF Models with Regime Shifts Explain the Correlations Between Commodity, Stock, and Bond Returns? (13/07/2017)
GUIDOLIN MASSIMO, M. Giampietro, and M. Pedio
European Journal of Operational Research, 2018, vol. 265, issue 2, pages 685-702.

European Journal of Operational Research

We develop new likelihood-based methods to estimate factor-based Stochastic Discount Factors (SDF) that may accommodate Hidden Markov dynamics in the factor loadings. We use these methods to investigate whether it is possible to find a SDF that jointly prices the cross-section of eight U.S. portfolios of stocks, Treasuries, corporate bonds, and commodities. In particular, we test a range of possible different specification of the SDF, including single-state and Hidden Markov models and compare their statistical and pricing performances. In addition, we assess whether and to which extent a selection of these models replicates the observed moments of the return series, and especially correlations. We report that regime-switching models clearly outperform single-state ones both in term of statistical and pricing accuracy. However, while a four-state model is selected by the information criteria, a two-state three-factor full Vector Autoregression model outperforms the others as far as the pricing accuracy is concerned.


Last change 30/08/2018

Dissecting the 2007-2009 Real Estate Market Bust: Systematic Pricing Correction or Just a Housing Fad? (23/06/2017)
GUIDOLIN MASSIMO, D. Bianchi, and F. Ravazzolo
Journal of Financial Econometrics, 2018, vol. 16, issue 1, pages 34-62.

Issue Cover

We use a flexible Bayesian model averaging method to estimate a factor pricing model characterized by structural uncertainty and instability in macro-financial factor loadings and idiosyncratic risks. We propose such a framework to investigate key differences in the pricing mechanism that applies to residential versus non-residential real estate investment trusts (REITs). An analysis of cross-sectional mispricings reveals no evidence of pure housing/residential real-estate abnormal returns inflating between 1999 and 2007, to subsequently collapse. In fact, all REITs sectors record increasing alphas during this period, and show important differences in the dynamic evolution of risk factors exposures.



Last change 30/08/2018

Portfolio Performance of Linear SDF Models: an Out-of-Sample Assessment (12/01/2018)
GUIDOLIN MASSIMO, Erwin Hansen e Martin Lozano-Banda
Quantitative Finance, 2018, 18, issue 9, pages 1425-1436.

Publication Cover

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean–variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968–2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean–variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.



Last change 30/08/2018

How Good Can Heuristic-Based Forecasts Be? A Comparative Performance of Econometric and Heuristic Models for UK and US Asset Returns (30/06/2017)
GUIDOLIN MASSIMO, Alex Orlov e Manuela Pedio
Quantitative Finance, 2018, 18, issue 1, pages 139-169.

Publication Cover

This paper systematically investigates the sources of differential out-of-sample predictive accuracy of heuristic frameworks based on internet search frequencies and a large set of econometric models. The volume of internet searches helps gauge the degree of investors’ time-varying interest in specific assets. We use a wide range of state-of-the-art models, both of linear and nonlinear type (regime-switching predictive regressions, threshold autoregressive, smooth transition autoregressive), extended to capture conditional heteroskedasticity through GARCH models. The predictor variables investigated are those typical of the literature featuring a range of macroeconomic and market leading indicators. Our out-of-sample forecasting exercises are conducted with reference to US, UK, French and German data, both stocks and bonds, and for 1- and 12-months-ahead horizons. We employ several forecast performance metrics and predictive accuracy tests. Internet-search-based models are found to perform better than the average of all of the alternative models. For several country-asset-horizon combinations, particularly for UK bond returns, our heuristic models compare favourably with sophisticated econometric methods. The heuristic models are also shown to perform well in forecasting realized volatility. The baseline results are supported by several extensions and robustness checks, such as using alternative search keywords, controlling for Fama–French and Cochrane–Piazzesi factors, and implementing heuristic-based trading strategies.


Last change 29/08/2018

Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the US Cross-Section (17/07/2015)
GUIDOLIN MASSIMO, Daniele Bianchi e Francesco Ravazzolo
Journal of Business and Economic Statistics, 2017, 35, issue 1, pages 110-129.

Publication Cover

This article proposes a Bayesian estimation framework for a typical multi-factor model with time-varying risk exposures to macroeconomic risk factors and corresponding premia to price U.S. publicly traded assets. The model assumes that risk exposures and idiosyncratic volatility follow a break-point latent process, allowing for changes at any point on time but not restricting them to change at all points. The empirical application to 40 years of U.S. data and 23 portfolios shows that the approach yields sensible results compared to previous two-step methods based on naive recursive estimation schemes, as well as a set of alternative model restrictions. A variance decomposition test shows that although most of the predictable variation comes from the market risk premium, a number of additional macroeconomic risks, including real output and inflation shocks, are significantly priced in the cross-section. A Bayes factor analysis massively favors the proposed change-point model. Supplementary materials for this article are available online.


Last change 29/08/2018

The Impact of Monetary Policy on Corporate Bonds under Regime Shifts (18/03/2017)
GUIDOLIN MASSIMO, Alex Orlov e Manuela Pedio
Journal of Banking and Finance, 2017, 80, pages 176-202

Journal of Banking & Finance

 
We study the effects of a conventional monetary expansion, quantitative easing, and the maturity extension program on corporate bond yields using impulse response functions obtained from flexible models with regimes. Using a three-state Markov switching model with time-homogeneous vector autoregressive (VAR) coefficients that emerges from a systematic model specification search, we find that unconventional policies may have been generally expected to decrease both corporate yields and spreads. However, even though the sign of the responses is the one desired by policymakers, the size of the estimated effects depends on the assumptions regarding the decline in long-term Treasury yields caused by unconventional policies, on which considerable uncertainty remains. Further tests based on yield spreads and a variable that measures inflation expectations show that, in the crisis regime, unconventional monetary policies do not produce any perverse effects on expected inflation. These results prove robust to adopting a framework that allows VAR coefficients to break, to imposing coefficient restrictions that increase parsimony, and to a range of different ordering schemes that identify shocks in alternative ways.


Last change 29/08/2018

Ambiguity Aversion and Under-Diversification (01/11/2016)
GUIDOLIN MASSIMO e Hening Liu
Journal of Financial and Quantitative Analysis, 2016, 51, issue 4, pages 1297-1323

Risultati immagini per jfqa

We examine asset allocation decisions under smooth ambiguity aversion when an investor has a prior degree of belief in the domestic capital asset pricing model (CAPM). Different from a Bayesian approach, the investor separately relies on the conditional distribution of returns and on the posterior over parameters to make decisions, rather than on the predictive distribution of returns that integrates priors and likelihood information. We find that in the perspective of U.S. investors, ambiguity aversion generates strong home bias in equity holdings, regardless of beliefs in the CAPM or risk aversion. Results become stronger under regime-switching investment opportunities.



Last change 29/08/2018

Learning How to Smile: Can Rational Learning Explain the Predictable Dynamics in the Implied Volatility Surface? (13/10/2015)
GUIDOLIN MASSIMO e Alejandro Bernales
Journal of Financial Markets, 2015, vol. 26, pages 1-37

Journal of Financial Markets

We develop a general equilibrium asset pricing model under incomplete information and rational learning in order to understand the unexplained predictability of option prices. In our model, the fundamental dividend growth rate is unknown and subject to breaks. Immediately after a break, there is insufficient information to price option contracts accurately. However, as new information arrives, a representative Bayesian agent recursively learns about the parameters of the process followed by fundamentals. We show that learning makes beliefs time-varying and generates predictability patterns across option contracts with different strike prices and maturities; as a result, the implied movements in the implied volatility surface resemble those observed empirically.


Last change 29/08/2018

Pricing S&P 500 Index Options: A Conditional Semi-Nonparametric Approach (27/05/2015)
GUIDOLIN MASSIMO e Erwin Hansen
Journal of Futures Markets, 2015, vol. 3, issue 3, pages 217-239

Publication cover image

We price S&P 500 index options under the assumption that the conditional risk‐neutral density function of the index follows a Semi‐Nonparametric (SNP) process with GARCH variance. The model is estimated combining a set of option contracts written on the index and the daily index return time series in the period 1996–2011. The in‐sample and out‐sample performance of the model is compared with several benchmark models, beating most of them. We conclude that a pricing model dealing simultaneously with non‐normalities and time‐varying volatility helps to mitigate the observed S&P 500 index option biases.



Last change 29/08/2018

How did the financial crisis alter the correlations of U.S. yield spreads? (11/2014)
GUIDOLIN MASSIMO, Silvio Contessi e Pierangelo De Pace
Journal of Empirical Finance, 2014, vol. 28, issue C, pages 362-385

We investigate the pairwise correlations of eleven U.S. fixed income yield spreads over a sample that includes the Great Financial Crisis of 2007–09. Using cross-sectional methods and nonparametric bootstrap breakpoint tests, we characterize the crisis as a period in which pairwise correlations between yield spreads were systematically and significantly altered in the sense that spreads comoved with one another much more than in normal times. We find evidence that, for almost half of the fifty-five pairs under investigation, the crisis has left spreads much more correlated than they were previously. This evidence is particularly strong for liquidity- and default-risk-related spreads, long-term spreads, and the spreads that were most likely directly affected by policy interventions.



Last change 29/08/2018

Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests (09/2014)
GUIDOLIN MASSIMO and Alejandro Bernales
Journal of Banking & Finance, 2014, vol. 46, issue C, pages 326-342

We examine whether the dynamics of the implied volatility surface of individual equity options contains exploitable predictability patterns. Predictability in implied volatilities is expected due to the learning behavior of agents in option markets. In particular, we explore the possibility that the dynamics of the implied volatility surface of individual stocks may be associated with movements in the volatility surface of S&P 500 index options. We present evidence of strong predictable features in the cross-section of equity options and of dynamic linkages between the volatility surfaces of equity and S&P 500 index options. Moreover, time-variation in stock option volatility surfaces is best predicted by incorporating information from the dynamics in the surface of S&P 500 options. We analyze the economic value of such dynamic patterns using strategies that trade straddle and delta-hedged portfolios, and find that before transaction costs such strategies produce abnormal risk-adjusted returns.



Last change 29/08/2018

Can long-run dynamic optimal strategies outperform fixed-mix portfolios? Evidence from multiple data sets (11/2014)
GUIDOLIN MASSIMO and Daniele Bianchi
European Journal of Operational Research, 2014, vol. 236, issue 1, pages 160-176

Using five alternative data sets and a range of specifications concerning the underlying linear predictability models, we study whether long-run dynamic optimizing portfolio strategies may actually outperform simpler benchmarks in out-of-sample tests. The dynamic portfolio problems are solved using a combination of dynamic programming and Monte Carlo methods. The benchmarks are represented by two typical fixed mix strategies: the celebrated equally-weighted portfolio and a myopic, Markowitz-style strategy that fails to account for any predictability in asset returns. Within a framework in which the investor maximizes expected HARA (constant relative risk aversion) utility in a frictionless market, our key finding is that there are enormous difference in optimal long-horizon (in-sample) weights between the mean–variance benchmark and the optimal dynamic weights. In out-of-sample comparisons, there is however no clear-cut, systematic, evidence that long-horizon dynamic strategies outperform naively diversified portfolios.



Last change 29/08/2018

Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate (11/2014)
GUIDOLIN MASSIMO, Francesco Ravazzolo, and Andrea Donato Tortora
The Journal of Real Estate Finance and Economics, 2014, vol. 49, issue 4, pages 477-523

This paper uses a multi-factor pricing model with time-varying risk exposures and premia to examine whether the 2003–2006 period has been characterized, as often claimed by a number of commentators and policymakers, by a substantial mispricing of publicly traded real estate assets (REITs). The estimation approach relies on Bayesian methods to model the latent process followed by risk exposures and idiosynchratic volatility. Our application to monthly, 1979–2009 U.S. data for stock, bond, and REIT returns shows that both market and real consumption growth risks are priced throughout the sample by the cross-section of asset returns. There is weak evidence at best of structural mispricing of REIT valuations during the 2003–2006 sample.



Last change 29/08/2018

Does the Macroeconomy Predict UK Asset Returns in a Nonlinear Fashion? Comprehensive Out-of-Sample Evidence (08/2014)
MASSIMO GUIDOLIN with Stuart Hyde, David McMillan, and Sadayuki Ono
Oxford Bulletin of Economics and Statistics, 2014, 76, (4), 510-535
 
We perform a comprehensive examination of the recursive, comparative predictive performance of linear and nonlinear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR) and smooth transition autoregressive (STR) regime switching models and a range of linear specifications including models with GARCH type specifications. Results demonstrate UK asset returns require nonlinear dynamics to be modelled with strong evidence in favour of Markov switching frameworks. Our results appear robust to the choice of sample period, changes in loss functions and to the methodology employed to test for equal predictive accuracy. The key findings extend to a similar sample of US data.

 



Last change 29/08/2018

Markov Switching Dynamics in REIT Returns: Univariate and Multivariate Evidence on Forecasting Performance (08/2014)
GUIDOLIN MASSIMO, Brad Case, and Yildiray Yildirim
Real Estate Economics, 2014, vol. 42, issue 2, pages 279-342

We document the presence of Markov switching regimes in expected returns, variances and the implied reward-to-risk ratio of real estate investment trust (REIT) returns and compare them to properties of stocks and bonds. Our evidence suggests that regime switching techniques are more successful over the period 1972–2008 than other time-series models are. When the analysis is extended to a multivariate setting in which REIT, stock and bond returns are modeled jointly, we find that the data call for the specification of four separate regimes. These result from the absence of synchronicity among the regimes that characterize univariate REIT, stock and bond returns.



Last change 29/08/2018

Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios (07/2014)
GUIDOLIN MASSIMO and Daniele Bianchi
The Journal of Real Estate Finance and Economics, 2014, vol. 49, issue 1, pages 116-164

A recent literature has shown that REIT returns contain strong evidence of bull and bear dynamic regimes that may be best captured using nonlinear econometric models of the Markov switching type. In fact, REIT returns would display regime shifts that are more abrupt and persistent than in the case of other asset classes. In this paper we ask whether and how simple linear predictability models of the vector autoregressive (VAR) type may be extended to capture the bull and bear patterns typical of many asset classes, including REITs. We find that nonlinearities are so deep that it is impossibile for a large family of VAR models to either produce similar portfolio weights or to yield realized, ex-post out-of-sample long-horizon portfolio performances that may compete with those typical of bull and bear models. A typical investor with intermediate risk aversion and a 5-year horizon ought to be ready to pay an annual fee of up to 5.7 % to have access to forecasts of REIT returns that take their bull and bear dynamics into account instead of simpler, linear forecast.



Last change 29/08/2018

Can VAR Models Capture Regime Shifts in Asset Returns? A Long-Horizon Strategic Asset Allocation Perspective (03/2012)
MASSIMO GUIDOLIN and Stuart Hyde
Journal of Banking and Finance, 36(3), pp. 695–716
 
It is often suggested that through a judicious choice of predictors that track business cycles and market sentiment, simple vector autoregressive (VAR) models could produce optimal strategic portfolio allocations that hedge against the bull and bear dynamics typical of financial markets. However, a distinct literature exists that shows that nonlinear econometric frameworks, such as Markov switching (MS), are also natural tools to compute optimal portfolios in the presence of stochastic good and bad market states. In this paper we examine whether simple VARs can produce portfolio rules similar to those obtained under MS, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included. In a typical stock–bond strategic asset allocation problem, we compute the out-of-sample certainty equivalent returns for a wide range of VARs and compare these measures of performance with those typical of nonlinear models for a long-horizon investor with constant relative risk aversion. We conclude that most VARs cannot produce portfolio rules, hedging demands, or (net of transaction costs) out-of-sample performances that approximate those obtained from equally simple nonlinear frameworks. We also compute the improvement in realized performance that may be achieved adopting more complex MS models and report this may be substantial in the case of regime switching ARCH.
 

 



Last change 29/08/2018

Time and Risk Diversification in Real Estate Investments: Assessing the Ex Post Economic Value (10/2009)
GUIDOLIN MASSIMO with Carolina Fugazza and Giovanna Nicodano
Real Estate Economics, 37(3), pp. 341-81.

Welfare gains to long-horizon investors may derive from time diversification that exploits non-zero intertemporal return correlations associated with predictable returns. Real estate may thus become more desirable if its returns are negatively serially correlated. While it could be important for long horizon investors, time diversification has been mostly investigated in asset menus without real estate and focusing on in-sample experiments. This paper evaluates ex post, out-of-sample gains from diversification when E-REITs belong to the investment opportunity set. We find that diversification into REITs increases both the Sharpe ratio and the certainty equivalent of wealth for all investment horizons and for both Classical and Bayesian (who account for parameter uncertainty) investors. The increases in Sharpe ratios are often statistically significant. However, the out-of sample average Sharpe ratio and realized expected utility of long-horizon portfolios are frequently lower than that of a one-period portfolo, which casts doubts on the value of time diversification.



Last change 29/08/2018

Affiliated Mutual Funds and Analyst Optimism (07/2009)
GUIDOLIN MASSIMO with Simona Mola
Journal of Financial Economics, 93(1), pp. 108-37.

Journal of Financial Economics

Prior studies have shown that investment banking affiliations place pressure on analysts to produce optimistic recommendations on the investment bank’s stock-clients. Our analysis of a large sample of recommendations issued from 1995 through 2003 indicates that a mutual fund affiliation also affects analysts’ research. That is, analysts are likely to look favorably at stocks held by the affiliated mutual funds. Controlling for a variety of factors including the investment banking affiliation, we find that the greater the portfolio weight of a stock for the affiliated mutual funds, the more optimistic the analyst rating becomes when compared to the consensus. Reputation partly restrains the optimism of analyst recommendations. In fact, the presence of other institutional investors as shareholders of the recommended stocks curbs analyst optimism. Nevertheless, from 1999 through 2001, star analysts report the most optimism when they recommend stocks in the portfolios of affiliated mutual funds.



Last change 29/08/2018

Forecasts of US Short-term Interest Rates: A Flexible Forecast Combination Approach (06/2009)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Econometrics, 150(2), pp. 297-311.

Journal of Econometrics

This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that accounting for both regimes in interest rate dynamics and combining forecasts from different models helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expectations hypothesis on the forecasting model are found to help at long forecasting horizons.



Last change 29/08/2018

Non-Linear Predictability in Stock and Bond Returns: When and Where Is It Exploitable? (04/2009)
GUIDOLIN MASSIMO with Stuart Hyde, David McMillan, and Sadayuki Ono
International Journal of Forecasting, pp. 373-99.

International Journal of Forecasting

We systematically examine the comparative predictive performance of a number of alternative linear and non-linear models for stock and bond returns in the G7 countries. Besides Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regime switching (predictive) regression models, we also estimate univariate models in which conditional heteroskedasticity is captured through GARCH, TARCH and EGARCH models and ARCH-in mean effects appear in the conditional mean. Although we fail to find a consistent winner/out-performer across all countries and asset markets, it turns out that capturing non-linear effects is of extreme importance to improve forecasting performance. U.S. and U.K. asset return data are special in the sense that good predictive performance seems to loudly ask for models that capture non linear dynamics, especially of the Markov switching type. Although occasionally also stock and bond return forecasts for other G7 countries appear to benefit from non-linear modeling (especially of TAR and STAR type), data from France, Germany, and Italy express interesting predictive results on the basis of simpler benchmarks. U.S. and U.K. data are also the only two data sets in which we find statistically significant differences between forecasting models. Results appear to be remarkably stable over time, and robust to the specification of the loss function used in statistical evaluations as well as to the methodology employed to perform pairwise comparisons.



Last change 29/08/2018

International Asset Allocation under Regime Switching, Skew and Kurtosis Preferences (02/2008)
GUIDOLIN MASSIMO with Allan Timmermann
Review of Financial Studies, 21(2), pp. 889-935.

Issue Cover

This paper proposes a new tractable approach to solving asset allocation problems in situations with a large number of risky assets which pose problems for standard approaches. Investor preferences are assumed to be defined over moments of the wealth distribution such as its mean, variance, skew and kurtosis. Time-variations in investment opportunities are represented by a flexible regime switching process. In the context of a four-moment international CAPM specification that relates stock returns in five regions to returns on a global market portfolio, we find evidence of distinct bull and bear states. Ignoring regimes, an unhedged US investor’s optimal portfolio is strongly diversified internationally. The presence of regimes in the return distribution leads to a large increase in the investor’s optimal holdings of US stocks as does the introduction of skew and kurtosis preferences. Our paper therefore offers an explanation of the strong home bias observed in US investors’ asset allocation based on regime switching and skew and kurtosis preferences.



Last change 29/08/2018

Size and Value Anomalies under Regime Shifts (01/2008)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Financial Econometrics, 6(1), pp. 1-48.

Issue Cover

This paper finds strong evidence of time-variations in the joint distribution of returns on a stock market portfolio and portfolios tracking size- and value effects. Mean returns, volatilities and correlations between these equity portfolios are found to be driven by underlying regimes that introduce short-run market timing opportunities for investors. The magnitude of the premia on the size and value portfolios and their hedging properties are found to vary across regimes. Regimes are shown to have a large impact on the optimal asset allocation - especially under rebalancing - and on investors’ utility. Regimes also have a considerable impact on hedging demands, which are positive when the investor starts from more favorable regimes and negative when starting from bad states. Recursive out-of-sample forecasting experiments show that portfolio strategies based on models that account for regimes dominate single-state benchmarks.



Last change 29/08/2018

Diamonds Are Forever, Wars Are Not. Is Conflict Bad for Private Firms? (12/2007)
GUIDOLIN MASSIMO with Eliana La Ferrara
American Economic Review,97(5), pp. 1978-93.

Risultati immagini per american economic review

This paper studies the relationship between civil war and the value of firms in a poor, resource abundant country using microeconomic data for Angola. We focus on diamond mining firms and conduct an event study on the sudden end of the conflict, marked by the death of the rebel movement leader in 2002. We find that the stock market perceived this event as bad news rather than good news for companies holding concessions in Angola, as their abnormal returns declined by 4 percentage points. The event had no effect on a control portfolio of otherwise similar diamond mining companies. This finding is corroborated by other events and by the adoption of alternative methodologies. We interpret our findings in the light of conflict-generated entry barriers, government bargaining power and transparency in the licensing process.



Last change 29/08/2018

Asset Allocation under Multivariate Regime Switching (11/2007)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Economic Dynamics and Control, 31(11), pp. 3503-44.

Journal of Economic Dynamics and Control

This paper studies asset allocation decisions in the presence of regime switching in asset returns. We find evidence that four separate regimes - characterized as crash, slow growth, bull and recovery states - are required to capture the joint distribution of stock and bond returns. Optimal asset allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Welfare costs from ignoring regime switching can be substantial even after accounting for parameter uncertainty. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in asset returns.

 



Last change 29/08/2018

Investing for the Long-Run in European Real Estate (01/2007)
GUIDOLIN MASSIMO with Carolina Fugazza and Giovanna Nicodano
Journal of Real Estate Finance and Economics, 34(1), pp. 35-80.

 
We calculate optimal portfolio choices for a long-horizon, risk-averse investor who diversifies among European stocks, bonds, real estate, and cash, when excess asset returns are predictable. Simulations are performed for scenarios involving different risk aversion levels, horizons, and statistical models capturing predictability in risk premia. Importantly, under one of the scenarios, the investor takes into account the parameter uncertainty implied by the use of estimated coefficients to characterize predictability. We find that real estate ought to play a significant role in optimal portfolio choices, with weights between 12 and 44 percent. Under plausible assumptions, the welfare costs of either ignoring predictability or restricting portfolio choices to traditional financial assets only are found to be in the order of 150-300 basis points per year. These results are robust to changes in the benchmarks and in the statistical framework.

 



Last change 29/08/2018

Properties of Equilibrium Asset Prices Under Alternative Learning Schemes (01/2007)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Economic Dynamics and Control,31(1), pp. 161-217.

Journal of Economic Dynamics and Control

This paper characterizes equilibrium asset prices under adaptive, rational and Bayesian learning schemes in a model where dividends evolve on a binomial lattice. The properties of equilibrium stock and bond prices under learning are shown to differ significantly compared with prices under full information rational expectations. Learning causes the discount factor and risk-neutral probability measure to become path-dependent and introduces serial correlation and volatility clustering in stock returns. We also derive conditions under which the expected value and volatility of stock prices will be higher under learning than under full information. Finally, we derive restrictions on prior beliefs under which Bayesian and rational learning lead to identical prices and show how the results can be generalized to more complex settings where dividends follow either multi-state i.i.d. distributions or multi-state Markov chains.



Last change 29/08/2018

High Equity Premia and Crash Fears. Rational Foundations (10/2006)
GUIDOLIN MASSIMO
Economic Theory, 28(3), pp. 693-708.

We show that when in Lucas trees model the process for dividends is described by a lattice tree subject to infrequent but observable structural breaks, in equilibrium recursive rational learning may inflate the equity risk premium and reduce the risk-free interest rate for low levels of risk aversion. The key condition for these results to obtain is the presence of sufficient initial pessimism. The relevance of these findings is magnified by the fact that under full information our artificial economy cannot generate asset returns matching the empirical evidence for any positive relative risk aversion.



Last change 29/08/2018

Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface (05/2006)
GUIDOLIN MASSIMO with Silvia Goncalves
Journal of Business, 79(3), pp. 1591-1635.

The Journal of Business

 
One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of fitting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper we study whether the resulting predictability patterns in the IVS coefficients may be exploited in practice. We propose a two-stage approach to modeling and forecasting the S&P 500 index options IVS. In the first stage we model the surface along the cross-sectional moneyness and time-to-maturity dimensions, similarly to Dumas et al. (1998). In the second-stage we model the dynamics of the cross-sectional first-stage implied volatility surface coefficients by means of vector autoregression models. We find that not only the S&P 500 implied volatility surface can be successfully modeled, but also that its movements over time are highly predictable in a statistical sense. We then examine the economic significance of this statistical predictability with mixed findings. Whereas profitable delta-hedged positions can be set up that exploit the dynamics captured by the model under moderate transaction costs and when trading rules are selective in terms of expected gains from the trades, most of this profitability disappears when we increase the level of transaction costs and trade multiple contracts off wide segments of the IVS. This suggests that predictability of the time-varying S&P 500 implied volatility surface may be not inconsistent with market efficiency.
 

 



Last change 29/08/2018

Term Structure of Risk under Alternative Econometric Specifications (03/2006)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Econometrics,131(1-2), pp. 285-308.

Journal of Econometrics

This paper characterizes the term structure of risk measures such as Value at Risk (VaR) and expected shortfall under different econometric approaches including multivariate regime switching, GARCH-in-mean models with student-t errors, two-component GARCH models and a non-parametric bootstrap. We show how to derive the risk measures for each of these models and document large variations in term structures across econometric specifications. An out-of-sample forecasting experiment applied to stock, bond and cash portfolios suggests that the best model is asset- and horizon specific but that the bootstrap and regime switching model are best overall for VaR levels of 5% and 1%, respectively.



Last change 29/08/2018

An Econometric Model of Nonlinear Dynamics in the Joint Distribution of Stock and Bond Returns (01/2006)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Applied Econometrics, 21(1), pp. 1-22.

Publication cover image

This paper considers a variety of econometric models for the joint distribution of US stock and bond returns in the presence of regime switching dynamics. While simple two‐ or three‐state models capture the univariate dynamics in bond and stock returns, a more complicated four‐state model with regimes characterized as crash, slow growth, bull and recovery states is required to capture their joint distribution. The transition probability matrix of this model has a very particular form. Exits from the crash state are almost always to the recovery state and occur with close to 50% chance, suggesting a bounce‐back effect from the crash to the recovery state.



Last change 29/08/2018

Economic Implications of Bull and Bear Regimes in UK Stock and Bond Returns (01/2005)
GUIDOLIN MASSIMO with Allan Timmermann
Economic Journal, 115(500), pp. 111-43.

Publication cover image

This paper presents evidence of persistent ‘bull’ and ‘bear’ regimes in UK stock and bond returns and considers their economic implications from the perspective of an investor's portfolio allocation. We find that the perceived state probability has a large effect on the optimal asset allocation, particularly at short investment horizons. If ignored, the presence of such regimes gives rise to substantial welfare costs. Parameter estimation uncertainty, while clearly important, does not overturn the conclusion that predictability in the return distribution linked to the presence of bull and bear states has a significant effect on investors’ strategic asset allocation.



Last change 29/08/2018

Option Prices under Bayesian Learning: Implied Volatility, Dynamics, and Predictive Densities (03/2003)
GUIDOLIN MASSIMO with Allan Timmermann
Journal of Economic Dynamics and Control, 27(5), pp. 717-69

Journal of Economic Dynamics and Control

This paper shows that many of the empirical biases of the Black and Scholes option pricing model can be explained by Bayesian learning effects. In the context of an equilibrium model where dividend news evolve on a binomial lattice with unknown but recursively updated probabilities we derive closed-form pricing formulas for European options. Learning is found to generate asymmetric skews in the implied volatility surface and systematic patterns in the term structure of option prices. Data on S&P 500 index option prices is used to back out the parameters of the underlying learning process and to predict the evolution in the cross-section of option prices. The proposed model leads to lower out-of-sample forecast errors and smaller hedging errors than a variety of alternative option pricing models, including Black–Scholes and a GARCH model.



Last change 29/08/2018

Last change 29/08/2018



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