Are natural resources a “curse” or a “blessing”? The empirical evidence suggests either outcome is possible. The paper surveys a variety of hypotheses and supporting evidence for why some countries benefit and others lose from the presence of natural resources. These include that a resource bonanza induces appreciation of the real exchange rate, deindustrialization and bad growth prospects, and that these adverse effects are more severe in volatile countries with bad institutions and lack of rule of law, corruption, presidential democracies, and underdeveloped financial systems. Another hypothesis is that a resource boom reinforces rent grabbing and civil conflict especially if institutions are bad, induces corruption especially in non-democratic countries, and keeps in place bad policies. Finally, resource rich developing economies seem unable to successfully convert their depleting exhaustible resources into other productive assets. The survey also offers some welfare-based fiscal rules for harnessing resource windfalls in developed and developing economies.

Natural Resources: Curse or Blessing?

This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.

Market Liquidity: Asset Pricing with Liquidity Risk

This paper deals with estimation of high-dimensional covariance with a conditional sparsity structure, which is the composition of a low-rank matrix plus a sparse matrix. By assuming sparse error covariance matrix in a multi-factor model, we allow the presence of the cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specic examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms, including the spectral norm. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also veried by extensive simulation studies.

https://mpra.ub.uni-muenchen.de/38697/1/MPRA_paper_38697.pdf

Large covariance estimation by thresholding principal orthogonal complements

This programming manual accompanies the PcGive Professional 9.0 software, an interactive modelling package for modelling statistical and economic data for forecasting or observing trends.

Object-orientd matrix programming using OX

We provide a model-free test for asymmetric correlations in which stocks move more often with the market when the market goes down than when it goes up, and also provide such tests for asymmetric betas and covariances. When stocks are sorted by size, book-to-market, and momentum, we find strong evidence of asymmetries for both size and momentum portfolios, but no evidence for book-to-market portfolios. Moreover, we evaluate the economic significance of incorporating asymmetries into investment decisions, and find that they can be of substantial economic importance for an investor with a disappointment aversion (DA) preference as described by Ang, Bekaert, and Liu (2005). , Oxford University Press. (This abstract was borrowed from another version of this item.)

Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation

This paper studies the dynamics of volatility transmission between Central European (CE) currencies and the EUR/USD foreign exchange using model-free estimates of daily exchange rate volatility based on intraday data. We formulate a flexible yet parsimonious parametric model in which the daily realized volatility of a given exchange rate depends both on its own lags as well as on the lagged realized volatilities of the other exchange rates. We find evidence of statistically significant intra-regional volatility spillovers among the CE foreign exchange markets. With the exception of the Czech and, prior to the recent turbulent economic events, Polish currencies, we find no significant spillovers running from the EUR/USD to the CE foreign exchange markets. To measure the overall magnitude and evolution of volatility transmission over time, we construct a dynamic version of the Diebold Yilmaz volatility spillover index and show that volatility spillovers tend to increase in periods characterized by market uncertainty.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1621485_code459177.pdf?abstractid=1621485

Volatility Transmission in Emerging European Foreign Exchange Markets

OTC derivatives reform and collateral demand impact

This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of integrated variance, upside and downside semivariance, and jump variation. Our results for the S&P 500 and WTI Crude Oil futures contracts show that simple linear quantile regressions for returns and heterogenous quantile autoregressions for realized volatility perform very well in capturing the dynamics of the respective conditional distributions, both in absolute terms as well as relative to a couple of well-established benchmark models. The models can therefore serve as useful risk management tools for investors trading the futures contracts themselves or various derivative contracts written on realized volatility.

Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility

This paper introduces a new method for estimating variance matrices. Starting from the orthogonal decomposition of the sample variance matrix, we exploit the fact that orthogonal matrices are never ill-conditioned and therefore focus on improving the estimation of the eigenvalues. We estimate the eigenvectors from just a fraction of the data, then use them to transform the data into approximately orthogonal series that deliver a well-conditioned estimator (by construction), even when there are fewer observations than dimensions. We also show that our estimator has lower error norms than the traditional one. Our estimator is design-free: we make no assumptions on the distribution of the random sample or on any parametric structure the variance matrix may have. Simulations confirm our theoretical results and they also show that our simple estimator does very well in comparison with other existing methods, especially when the data are generated from fat-tailed densities.

Design-Free Estimation of Variance Matrices

This paper presents a comprehensive comparison of the existing tests for the presence of jumps in prices of financial assets. The relative performance of the tests is examined in a Monte Carlo simulation, covering scenarios of both finite and infinite activity jumps, stochastic volatility models with continuous and discontinuous volatility sample paths, microstructure noise and infrequent trading. The simulation results reveal important differences in terms of size and power across the different data generating processes and sensitivity to the presence of zero returns and microstructure frictions in the data. An empirical application to different asset classes complements the analysis.

Filip Zikes

Papers

Volatility Transmission in Emerging European Foreign Exchange Markets

OTC derivatives reform and collateral demand impact

Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility

Design-Free Estimation of Variance Matrices

A Comprehensive Comparison of Alternative Tests for Jumps in Asset Prices