Pairs Of Assets Research Articles

Asymmetric correlations in gold and other financial markets

ABSTRACTIn this article, we implement a recently developed statistical method to test asymmetries in cross-asset correlations, focusing in particular on the gold market. Our empirical results provide evidence that gold exhibits asymmetric correlations with stocks and the U.S. dollar, but not with bonds. Furthermore, splitting the sample into three characteristic periods, we find that exceedance correlations exhibit substantial time variation even in similar market tensions for same pairs of assets. Our findings imply that investors and fund managers should take into account the asymmetric dependence structure, which depends on the upside or downside of the market.

Local non-stationarity test in mean for Markov switching GARCH models: an approximate Bayesian approach

In order to exploit mean-reverting behavior among the price differential between two markets, one can use unit root tests to determine which pairs of assets appear to exhibit mean-reverting behavior. Since nonlinear mean reversion shares the same meaning as local stationarity, this paper proposes a Bayesian hypothesis testing to detect the presence of a local unit root in the mean equation using Markov switching GARCH models. This model incorporates a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. To implement the test, we propose a numerical approximation of the marginal likelihoods to posterior odds by using an adaptive Markov Chain Monte Carlo scheme. Our simulation study demonstrates that the approximate Bayesian test performs properly. The proposed method utilizes the daily basis between the FTSE 100 Index and Index Futures as an illustration.

Gold, oil, and stocks: Dynamic correlations

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods.

What drives asymmetric dependence structure of asset return comovements?

We examine the determinants of return comovements of three different asset classes and provide critical insights on the key macroeconomic and non-macroeconomic factors which drive the asset return comovements during economic contraction and expansion regimes. We show that amongst the macroeconomic factors, interest rate and inflation have significant effect on the return comovements during the economic contraction regime whilst risk aversion significantly affects the return comovements during the economic expansion regime. The non-macroeconomic factors, output uncertainty, bond illiquidity and depth of recession contribute significantly in explaining the variations of return comovements for all asset pairs during both economic contraction and expansion periods except for real estate-based portfolios. Our results are robust to alternative model specification that uses regime switching MGARCH model.

Investigating Correlation and Volatility Transmission among Equity, Gold, Oil and Foreign Exchange

The paper offers an investigation into the co-movement between the returns of the S&P 500 stock index, the price of gold, West Texas Intermediate crude oil and the exchange rate of the Swiss Franc to the US Dollar. The research finds significant evidence for a correlation between the returns of the time series as well as their volatilities. The input values for these volatilities is a univariate GARCH(1,1) process for each of the time series. Are more detailed insight into the correlations is provided by a dynamic conditional correlation model on each pair of assets. There is enough significant evidence to suggest that the returns are correlated and there is volatility transmission between the assets. Further, the fact that volatility and correlation can be modeled suggests they can also be forecasted. In combination this knowledge is crucial in areas of risk management, portfolio management or even speculation on volatility indices.

A local unit root test in mean for financial time series

A common financial trading strategy involves exploiting mean-reverting behaviour of paired asset prices. Since a unit root test can be used to determine which pairs of assets appear to exhibit mean-reverting behaviour, we propose a new Bayesian unit root to detect the presence of a local unit root vs. mean-reverting nonlinear smooth transition heteroskedastic alternative hypotheses. This test procedure is based on the posterior odds. For simultaneous estimation and inference, we employ an adaptive Bayesian Markov chain Monte Carlo scheme, which utilizes a mixture prior specification to solve the likelihood identification problem of the smoothing parameter and the autoregressive coefficient with a unit root. The size and power properties of the proposed method are examined via a simulation study. An empirical study examines the mean-reverting behaviour of price differential between stock and future.

Eccentricity in asset management

We describe how networks based on information theory can help measure and visualize systemic risk, enhance diversification, and help price assets. To do this, we first define a distance measure based on the mutual information between asset pairs and use this measure in the construction of minimum spanning trees. The dynamics of the shape and the descriptive statistics of these trees are analyzed in various investment domains. The method provides evidence of regime changes in dependency structures prior to market sell-offs, and as such, it is a potential candidate for monitoring systemic risk. We also provide empirical evidence that the assets that are located towards the center of the network tend to have higher returns. Finally, an investment strategy that utilizes network centrality information is shown to add value historically.

Rho Has No Role: Correlation Coefficient Instability and Non-asymptotic Simulation Volatility

This paper calculates the per quarter pairwise correlation coefficients (Rho) of the daily returns from December 5, 2005 to December 8, 2014 of 30 stocks randomly selected from the Russell 3000 index. For the time series correlation coefficients of 435 pairs of assets, we employ the Elliot-Rothenberg-Stock Point Optimal procedure to examine the stability of correlation coefficients. Our results indicate the inappropriateness of using correlation coefficients in portfolio management and Monte Carlo simulation. More than one-third of the correlation coefficient series generate non-asymptotic simulation volatility and using ex post correlation coefficients in Cholesky decomposition performance forecast can lead to severe deviation from the investment policy mandate.

System-wide tail comovements: A bootstrap test for cojump identification on the S&P 500, US bonds and currencies

This paper studies bivariate tail comovements on financial markets that are of crucial importance for the world economy: the S&P 500, US bonds, and currencies. We propose to study that form of dependence under the lens of cojump identification in a bivariate Brownian semimartingale with idiosyncratic jumps, as well as cojumps. Whereas univariate jump identification has been widely studied in the high-frequency data literature, the multivariate literature on cojump identification is more recent and scarcer. Cojump identification is of interest, as it may identify comovements which are not trivially visible in a univariate setting. That is, price changes can be small relative to local variation, but still abnormal relative to local covariation. This paper investigates how simple parametric bootstrapping of the product of assets' intraday returns can help detect cojumps in a multivariate Brownian semi-martingale with both idiosyncratic jumps and cojumps. In particular, we investigate how to disentangle idiosyncratic jumps from common jumps at an intraday level for pairs of assets. The approach is flexible, trivial to implement, and yields good power properties. It allows to shed new light on extreme dependence at the world economy level. We detect cojumps of heterogeneous size which are partly undetected with a univariate approach. We find an increased cojump intensity after the crisis on the S&P 500-US bonds pair before a return to normal.

Volatility returns with vengeance: Financial markets vs. commodities

To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one exogenous variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of assets, and derive several case studies linking commodities to stocks, bonds and currencies from 1983 to 2013. The innovative feature of our model is that these volatility spillovers are modeled consistently within the correlation dynamics of the ADCCX. We find evidence that return and volatility spillovers do exist between commodity and financial markets and that in turn, their relative impact on each other is very substantial.

An international analysis of REITs and stock portfolio management based on dynamic conditional correlation models

This paper analyzes the volatility spillovers and asymmetry between REITs and stock prices for nine countries (Australia, Belgium, Germany, Italy, Japan, The Netherlands, Singapore, the United Kingdom, and the United States) using eight different multivariate GARCH models. We also analyze the optimal weights, hedging effectiveness, and hedge ratios for REIT-stock portfolio holdings with respect to the results. The empirical results indicate that dynamic conditional correlation (DCC) models provide a better fit than the constant conditional correlation models. The DCC with volatility spillovers and asymmetry (DCC-SA) model provides a better fit than the other multivariate GARCH models. The DCC-SA model also provides the best hedging effectiveness for all pairs of REIT-stock assets. More importantly, this result holds for all cases and for all models that we consider, which means that by taking spillover and asymmetry into consideration, hedging effectiveness can be vastly improved.

On the characteristics of dynamic correlations between asset pairs

Recent research provides considerable evidence that correlations between assets change significantly over time and diversification benefits of correlations may vary substantially based on the time-varying measure of correlation used for different asset types. Our study evaluates and compares alternative time-series correlation modeling techniques according to both statistical and economic metrics, focusing specifically on individual asset pairs. We identify the moving correlation structure that best tracks the dynamic conditional correlation estimates using a large set of different financial time series encompassing 467 asset pairs in nine different asset classes. Results from our direct, statistical loss function based, and indirect, portfolio mean-variance based, forecast evaluations provide optimal window-length ranges for 36 asset-class pairs which should help in portfolio construction as well as risk management. Furthermore for robustness tests, we implement the model confidence set approach which, without a benchmark specification, produces a set of models constructed to contain the best models with a given level of confidence among competing forecast evaluations.

The hexanomial lattice for pricing multi-asset options

Multi-asset options are important financial derivatives. Because closed-form solutions do not exist for most of them, numerical alternatives such as lattice are mandatory. But lattices that require the correlation between assets to be confined to a narrow range will have limited uses. Let ρij denote the correlation between assets i and j. This paper defines a (correlation) optimal lattice as one that guarantees validity as long as -1+O(Δt)⩽ρij⩽1-O(Δt) for all pairs of assets i and j, where Δt is the duration of a time period. This paper then proposes the first optimal bivariate lattice (generalizable to higher dimensions), called the hexanomial lattice. This lattice furthermore has the flexibility to handle a barrier on each asset. Experiments confirm its excellent numerical performance compared with alternative lattices.

System-Wide Tail Comovements: A Bootstrap Test for Cojump Identification on the S&P 500, US Bonds and Exchange Rates

This paper studies bivariate tail comovements on financial markets that are of crucial importance for the world economy: The S&P 500, US bonds, and currencies. We propose to study that form of dependence under the lens of cojump identification in a bivariate Brownian semimartingale with idiosyncratic jumps, as well as cojumps. Whereas univariate jump identification has been widely studied in the high-frequency data literature, the multivariate literature on cojump identification is more recent and scarcer. Cojump identification is of interest, as it may identify comovements which are not trivially visible in a univariate setting. That is, price changes can be small relative to local variation, but still abnormal relative to local covariation. This paper investigates how simple parametric bootstrapping of the product of assets' intraday returns can help detect cojumps in a multivariate Brownian semi-martingale with both idiosyncratic jumps and cojumps. In particular, we investigate how to disentangle idiosyncratic jumps from common jumps at an intraday level for pairs of assets. The approach is flexible, trivial to implement, and yields good power properties. It allows to shed new light on extreme dependence at the world economy level. We detect cojumps of heterogeneous size which are partly undetected with a univariate approach. We find an increased cojump intensity after the crisis on the S&P 500-US bonds pair before a return to normal.

Eccentricity in Asset Management

We describe how networks based on information theory can help measure and visualize systemic risk, enhance diversification, and help price assets. To do this, we first define a distance measure based on the mutual information between asset pairs and use this measure in the construction of minimum spanning trees. The dynamics of the shape and the descriptive statistics of these trees are analyzed in various investment domains. The method provides evidence of regime changes in dependency structures prior to market sell-offs, and as such, it is a potential candidate for monitoring systemic risk. We also provide empirical evidence that the assets that are located towards the center of the network tend to have higher returns. Finally, an investment strategy that utilizes network centrality information is shown to add value historically.

Gold, Oil, and Stocks

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods.

Modelling microstructure noise with mutually exciting point processes

We introduce a new stochastic model for the variations of asset prices at the tick-by-tick level in dimension 1 (for a single asset) and 2 (for a pair of assets). The construction is based on marked point processes and relies on mutually exciting stochastic intensities as introduced by Hawkes. We associate a counting process with the positive and negative jumps of an asset price. By suitably coupling the stochastic intensities of upward and downward changes of prices for several assets simultaneously, we can reproduce microstructure noise (i.e. strong microscopic mean reversion at the level of seconds to a few minutes) and the Epps effect (i.e. the decorrelation of the increments in microscopic scales) while preserving standard Brownian diffusion behaviour on large scales. More effectively, we obtain analytical closed-form formulae for the mean signature plot and the correlation of two price increments that enable us to track across scales the effect of the mean-reversion up to the diffusive limit of the model. We show that the theoretical results are consistent with empirical fits on futures Euro–Bund and Euro–Bobl in several situations.

Dependence and Contagion Between Asset Prices in Poland and Abroad. A Copula Approach

We investigate the dependence structure between Polish and foreign financial assets, including stocks, bonds and foreign exchange. Our interest is in the importance of global factors for asset valuation and on the strength of financial contagion. We work in the copula framework, which offers a full description of the dependence structure. Importantly, we assess many copula families and pay special attention to the testing procedure thereof. Polish equities, currency and to some extent long-term sovereign bonds exhibit economically significant tail dependence, while short-term bonds appear relatively unaffected. Symmetric tail behaviour characterises the majority of asset pairs, though we also find significant asymmetries in a number of cases, with assets more likely to post large losses when global conditions significantly deteriorate, rather than to gain when they improve.

Correlating the Returns of Oil-Producing Projects Realistically

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 150909, ’How To Measure the Correlation Between Return of Oil-Production Projects Realistically,’ by G.A. Costa Lima, SPE, A.T. Gaspar Ravagnani, SPE, and D.J. Schiozer, SPE, Unicamp, prepared for the 2012 North Africa Technical Conference and Exhibition, Cairo, 20-22 February. The paper has not been peer reviewed. Portfolio theory has been an important tool for economic-risk management. There are only three input parameters: mean return, standard deviation of return, and correlation between pairs of such things as assets, projects, and prospects. Although the model is simple, a problem with it is estimating the correlation because there are no historical data. Alternatively, correlation on return of projects is assumed subjective (typically 70%), but with this model it can be estimated in a much more sound way. Results indicate that the main determinants of correlation are fixed cost, variable cost, and oil quality. With this information, managers can select best portfolios (e.g., by means of farm in and farm out) to create value for stakeholders considering means, risk, and possible benefits from diversification. Introduction Often, decision makers must manage portfolios containing many projects, including exploration, development, production, and decommissioning. The life span of these portfolios is similar to that of the company, possibly several decades. Most company portfolios are updated yearly. In this process, the return to stakeholders depends on such factors as the skill of decision makers to add and remove projects, performance of individual projects (and the synergistic interaction in terms of geologic features), operating infrastructure, and marketing conditions. In portfolio management, decisions are made on the basis of the mean and standard deviation (risk) of the rate of return of investments. The risk depends on the standard deviation of each individual asset, correlation between pairs of assets, proportion of investment allocated in each asset, and the number of assets in the portfolio. Of these variables, only the risk of the individual asset is beyond the manager’s control because it depends on variables such as oil production, operating cost, fiscal regime, reservoir conditions, and geologic aspects; therefore, it is called systematic risk. The use of portfolio theory to solve real-world problems depends strongly on calculations/estimations of correlations. Therefore, the focus in this paper is on the methodology to estimate correlations between pairs of net present value (NPV) of oil projects. Correlation vs. Diversification Consider an oil company with an opportunity to construct a portfolio having two oil-production projects. Initially, professionals complete a traditional economic analysis at the project level to estimate mean and standard deviation of the NPV of each project. Results of this initial analysis are shown in Table 1.

Algorithmic Trading Using Phase Synchronization

A novel trading algorithm which performs trading decisions by making use of phase synchronization between oscillatory components of asset pairs is proposed. This way the phase information ascertains a leading asset, which is then used to predict the lagging asset. The oscillatory components of asset pairs are identified using the Synchrosqueezed Transform (SST), which facilitates stable and online implementation. The performance of the proposed approach is compared with existing algorithms used extensively by traders, such as the moving average cross-over, and an extrapolation algorithm based upon the multichannel-least mean square (MLMS).