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on Financial Development and Growth |
| By: | Carbonez, Katelijne; Nguyen, Thi Tuong Van; Sercu, Piet |
| Abstract: | This paper revisits the asymmetric effect of the basis on commodity spot and futures price volatilities documented by Kogan, Livdan and Yaron (2008) and Lien and Yang (2008). Kogan et al. (2008) show both theoretically and empirically that, for a non-storable consumption good, the relationship between commodity price volatility and the basis exhibits a V-shape. Lien and Yang (2008) illustrate the existence of an asymmetric effect of the basis on commodity price volatilities for storable commodities. Their results seem to imply that both scarcity and abundance increase spot and futures price volatility, a counter-intuitive result. The aim of this article is twofold: (i) test the presence and the robustness of the asymmetric effect for storable agricultural commodities by analyzing different sample periods, longer horizons and alternative utility functions; and - given that this asymmetric effect turns out not to be robust - (ii) explore new variables besides the basis to proxy for scarcity, analyze whether they exhibit an asymmetric effect and test their performance in modeling storable commodity price volatility and in hedging futures positions. Our results provide little support for a V-shaped relationship between the basis and storable agricultural commodity price volatilities. Though an asymmetric effect is present in that the size of the coe±cient for a positive basis is much larger than for a negative basis, a negative basis does not lead to higher volatilities. Moreover, we find that the strong hedging performance documented by Lien and Yang (2008) when including the asymmetric basis in the volatility specification is not robust across sample periods, for longer hedging horizons and for alternative utility functions. More positively, though, our results indicate that alternative scarcity specifications do have the expected positive link with volatility and often outperform more simple models in terms of hedging performance. Unfortunately, no single variable consistently leads to better results out-of-sample and there is often no correspondence between the best performing model in- and out-of-sample. |
| Date: | 2010–02–23 |
| URL: | http://d.repec.org/n?u=RePEc:ner:leuven:urn:hdl:123456789/262181&r=fdg |
| By: | Massimo Guidolin (IGIER, Bocconi University and CAIR, Manchester Business School); Francesco Ravazzolo (Norges Bank (Central Bank of Norway)); Andrea Donato Tortora (Bocconi University, Milan) |
| Abstract: | 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 missprcing 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 misspricing of REIT valuations during the 2003-2006 sample. |
| Keywords: | REIT returns, Bayesian estimation, Structural instability, Stochastic volatility, Linear factor models |
| JEL: | G11 C53 |
| Date: | 2011–12–27 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2011_19&r=fdg |
| By: | J. Shen; B. Zheng |
| Abstract: | With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the probability distribution of returns and long-range time-correlation of volatilities etc., remain essentially unchanged. This suggests that the leverage effect or anti-leverage effect in financial markets arises from a kind of feedback return-volatility interactions, rather than the long-range time-correlation of volatilities and asymmetric probability distribution of returns. Further, we show that large volatilities dominate the return-volatility correlation in financial dynamics. |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1202.0342&r=fdg |
| By: | Dario Caldara; Jesús Fernández-Villaverde; Juan F. Rubio-Ramírez; Yao Wen |
| Abstract: | This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991) and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2012-04&r=fdg |
| By: | Masaaki Fujii; Akihiko Takahashi |
| Abstract: | In this work, we apply our newly proposed perturbative expansion technique to a quadratic growth FBSDE appearing in an incomplete market with stochastic volatility that is not perfectly hedgeable. By combining standard asymptotic expansion technique for the underlying volatility process, we derive explicit expression for the solution of the FBSDE up to the third order of volatility-of-volatility, which can be directly translated into the optimal investment strategy. We compare our approximation with the exact solution, which is known to be derived by the Cole-Hopf transformation in this popular setup. The result is very encouraging and shows good accuracy of the approximation up to quite long maturities. Since our new methodology can be extended straightforwardly to multi-dimensional setups, we expect it will open real possibilities to obtain explicit optimal portfolios or hedging strategies under realistic assumptions. |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1202.0608&r=fdg |
| By: | Sunil Kumar; Nivedita Deo |
| Abstract: | We apply RMT, Network and MF-DFA methods to investigate correlation, network and multifractal properties of 20 global financial indices. We compare results before and during the financial crisis of 2008 respectively. We find that the network method gives more useful information about the formation of clusters as compared to results obtained from eigenvectors corresponding to second largest eigenvalue and these sectors are formed on the basis of geographical location of indices. At threshold 0.6, indices corresponding to Americas, Europe and Asia/Pacific disconnect and form different clusters before the crisis but during the crisis, indices corresponding to Americas and Europe are combined together to form a cluster while the Asia/Pacific indices forms another cluster. By further increasing the value of threshold to 0.9, European countries France, Germany and UK constitute the most tightly linked markets. We study multifractal properties of global financial indices and find that financial indices corresponding to Americas and Europe almost lie in the same range of degree of multifractality as compared to other indices. India, South Korea, Hong Kong are found to be near the degree of multifractality of indices corresponding to Americas and Europe. A large variation in the degree of multifractality in Egypt, Indonesia, Malaysia, Taiwan and Singapore may be a reason that when we increase the threshold in financial network these countries first start getting disconnected at low threshold from the correlation network of financial indices. We fit Binomial Multifractal Model (BMFM) to these financial markets. |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1202.0409&r=fdg |