Pairs Of Assets Research Articles

Detecting Statistical Arbitrage Opportunities Using a Combined Neural Network - GARCH Model

This paper proposes a hybrid computational intelligent system for the detection of statistical arbitrage opportunities in pairs of assets. The proposed methodology combines nonlinear neural network autoregressive models with GARCH parametrizations of volatility for describing the dynamics of the correction of relative mispricings. First results from this approach seem encouraging; further experimentation on the optimal sampling frequency, the forecasting horizon and the points of entry and exit is conducted, in order to improve the economic value when transaction costs are taken into account.

Probability Judgment Error and Speculation in Laboratory Asset Market Bubbles

AbstractIn 12 sessions conducted in a typical bubble-generating experimental environment, we design a pair of assets that can detect both irrationality and speculative behavior. The specific form of irrationality we investigate is the probability judgment error associated with low-probability, high-payoff outcomes. Independently, we test for speculation by comparing prices of identically paying assets in multiperiod versus single-period markets. We establish that aggregate irrationality measured in one dimension (probability judgment error) is associated with aggregate irrationality measured in another (bubble formation).

Performance of Time-Varying Correlation Estimation Methods

This study evaluates and compares alternative time series correlation modeling techniques, using a broad database of 33 variables and 467 asset pairs in nine different asset classes. For each pair of assets a time-varying moving window correlation (MWC) is computed from different moving itional correlation (DCC) time series model, first documenting the closeness of various MWC estimates to DCC, and next evaluating the effectiveness of the models in a portfolio context. We consider four statistical measures of closeness to DCC. According to the concordance correlation coefficient, the Kolmogorov-Smirnov statistic, and the sign agreement test, across all asset pairs under consideration, the shorter to intermediate moving windows (252 days and below) tend to lie closest to DCC; whereas for the mean square error measure, longer windows tend to best match DCC. However, there are some patterns distinct to certain asset classes such as equity and credit, in which both mean square error and concordance correlation coefficient measures of closeness suggest that MWC match DCC estimates at shorter moving windows. In the portfolio management context, the economic closeness test based on 2,802 monthly rebalanced two-asset portfolios shows that generally MWCs are closer to DCC at the longer window lengths.

Equity-style timing: A multi-style rotation model for the Russell large-cap and small-cap growth and value style indexes

Researchers and practitioners have devoted considerable attention to devising market-timing strategies as potential value-enhancement tools. The success of such active or tactical asset allocation strategies is dependent on their ability to capture either inefficiencies, to the extent that they exist, or disequilibria associated with changes in the investor opportunity set. Much of the equity-style timing literature focuses on the shifting between pairs of risky assets or between one risky and one riskless asset class, using a binomial approach. This paper develops a multinomial timing model based on macroeconomic and fundamental public information using Frank Russell large-cap and small-cap style indexes. We model four different market segments simultaneously. Out-of-sample tests demonstrate that active multi-style rotation strategies can be devised that outperform the best performing buy-and-hold portfolio. The profitability of such strategies is robust to reasonable levels of transaction costs.

The Origins of Bubbles in Laboratory Asset Markets

In twelve sessions conducted in a typical bubble-generating experimental environment, we design a pair of assets that can detect both irrationality and speculative behavior. The specific form of irrationality we investigate is probability judgment error associated with low-probability, high-payoff outcomes. Independently, we test for speculation by comparing prices of identically paying assets in multiperiod versus single-period markets. When these tests indicate the presence of probability judgment error and speculation, bubbles are more likely to occur. This finding suggests that both factors are important bubble drivers.

Preservation of the location independent risk order under convolution

The location independent risk order has been used to compare different random assets in risk analysis without the requirement of equal means. Let ( X i , Y i ) , i = 1 , 2 , … , n , be independent pairs of random assets. It is shown that if X i is less than Y i in the location independent risk order for each i and the X i and Y i have log-concave density or probability functions, or if X i is less than Y i in the dispersive order and the X i and Y i have log-concave distribution functions, then ∑ i = 1 n X i is less than ∑ i = 1 n Y i in the location independent risk order. Similar results also hold for the excess wealth order.

Testing the Gaussian copula hypothesis for financial assets dependences

Using one of the key properties of copulas that they remain invariant under an arbitrary monotonic change of variable, we investigate the null hypothesis that the dependence between financial assets can be modelled by the Gaussian copula. We find that most pairs of currencies and pairs of major stocks are compatible with the Gaussian copula hypothesis, while this hypothesis can be rejected for the dependence between pairs of commodities (metals). Notwithstanding the apparent qualification of the Gaussian copula hypothesis for most of the currencies and the stocks, a non-Gaussian copula, such as the Student copula, cannot be rejected if it has sufficiently many ‘degrees of freedom’. As a consequence, it may be very dangerous to embrace blindly the Gaussian copula hypothesis, especially when the coefficient of correlation between the pairs of assets is too high, such that the tail dependence neglected by the Gaussian copula can became large, leading to the ignoring of extreme events which may occur in unison.

Liquid markets and market liquids

We characterize the collective phenomena of a liquid market. By interpreting the behavior of a no-arbitrage N asset market in terms of a particle system scenario, (thermo)dynamical-like properties can be extracted from the asset kinetics. In this scheme the mechanisms of the particle interaction can be widely investigated. We test the verisimilitude of our construction on two-decade stock market daily data (DAX30) and show the result obtained for the interaction potential among asset pairs.

Pareto improving financial innovation in incomplete markets

In this paper we develop a differential technique for investigating the welfare effects of financial innovation in incomplete markets. Utilizing this technique, and after parametrizing the standard competitive, pure-exchange economy by both endowments and utility functions, we establish the following (weakly) generic property: Let S be the number of states, I be the number of assets and H be the number of households, and consider a particular financial equilibrium. Then, provided that the degree of market incompleteness is sufficiently larger than the extent of household heterogeneity, S−I≥2H−1 [resp. S−I≥H+1], there is an open set of single assets [resp. pairs of assets] whose introduction can make every household better off (and, symmetrically, an open set of single assets [resp. pairs of assets] whose introduction can make them all worse off ). We also devise a very simple nonparametric procedure for reducing extensive household heterogeneity to manageable size, a procedure which not only makes our restrictions on market incompleteness more palatable, but could also prove to be quite useful in other applications involving smooth analysis.

Return Properties of Equity REITs, Common Stocks, and Commercial Real Estate: A Comparison

Most previous studies of REIT-securitized real estate examine the performance of REITs versus only common stocks. In addition, previous studies have focused on the mean and standard deviation of returns, while this study also examines skewness, kurtosis, and conducts several tests of normality for the returns. The time series properties of the returns are also examined by calculating the autocorrelation function for each of the series. Finally, this study extends the results of Giliberto concerning the intertemporal relationship between REIT returns and real estate returns by examining a vector autoregressive model in which returns on pairs of assets are modeled as a linear function of lags of their own returns and lags of the returns on the other asset in the pair. Granger causality tests are also performed to determine if an asset's returns Granger cause the returns on the other asset. In a distributional and time series sense, equity REIT returns appear to be much more like those on common stocks and closed-end funds than those on unsecuritized commercial real estate. Intertemporally, REIT returns are much more strongly related to unsecuritized real estate than stocks or closed-end funds. The equity REIT index returns were found to Granger cause unsecuritized real estate returns for most of the real estate indices. Results were somewhat mixed for the individual REITs.

ASSET DEMANDS AND SLUTSKY EQUATIONS WHEN ALL ASSETS ARE RISKY

ABSTRACTThis paper exploits the properties of the indirect expected utility function to explore the testable implications of a portfolio model in which all assets are risky. An uncertainty analogue of Roy's Identity and extremely simple derivations of Slutsky equations for risk and return are used in the analysis. Substitutability/complementarity relations between asset pairs are also explored. The one‐riskless‐asset case emerges as a special case of the present model, and it is shown that (since inflation uncertainty makes all real returns uncertain) the inclusion of a riskless asset would lead to misleading over‐simplifications.

The Arbitrage Pricing Model and Returns on Assets Under Uncertain Inflation*

THE IMPACT OF INFLATION on equilibrium returns in capital markets has been the subject of several articles in recent years. Chen and Boness [1], Friend, Landskroner and Losq [4], Long [7] and Roll [13] have all constructed general equilibrium models which incorporate the impact of uncertain inflation on returns.' All of these authors have derived models in a mean-variance world utilizing methodologies similar to that first utilized by Mossin [9] or Merton [8]. The basic underlying tenet of mean-variance analysis is that for any given level of expected return investors wish to minimize the variance of return. In mean-variance equilibrium models the only risk that cannot be diversified away is the covariance of an asset's return with the market return. Assets which have the same covariance with the market have the same equilibrium required return. Thus in a mean-variance world if each member of the following pairs of assets had the same covariance with the market each member would have the same expected return: (a) a stock which was a perfect hedge against inflation versus one which was not, (b) a stock which was a perfect hedge against oil price changes versus one which was not, (c) a security that hedged against interest rate changes versus one which did not. In this article we break out of the limitations of the mean variance assumptions by utilizing the Arbitrage Pricing Theory (APT) of Ross [15], [16], [17]. This

Interest rate parity tests: Switzerland and some major western countries

This article reviews briefly the interest rate parity ( IP) theory and possible divergences between covered interest rates on two assets denominated in different currencies. From direct IP tests involving pairs of assets denominated in Sfr., $, £ and DM, using monthly data on 3-month Treasury Bills and on 3-month Euro-deposits, it appears that the theory holds better for Euro-currency pairs of assets than for domestic pairs. Further investigation uncovers significant occurence of autocorrelation for domestic pairs, possibly indicating left-out explanatory factors in the standard IP formulation, and points to the non-normality of the distrbution of arbitrage margins, thus casting doubts on most statistical results in such tests.

Shoe-fitting by X-rays

That investors should diversify their portfolios is a core principle of modern finance. Yet there are some periods in which diversification is undesirable. When the portfolio’s main growth engine performs well, investors prefer the opposite of diversification. An ideal complement to the growth engine would provide diversification when it performs poorly and unification when it performs well. Numerous studies have presented evidence of asymmetric correlations between assets. Unfortunately, this asymmetry is often of the undesirable variety: It is characterized by downside unification and upside diversification. In other words, diversification often disappears when it is most needed. In this article, the authors highlight a fundamental flaw in the way some prior studies have measured correlation asymmetry. Because they estimate downside correlations from subsamples in which both assets perform poorly, they ignore instances of successful diversification (i.e., periods in which one asset’s gains offset the other’s losses). The authors propose instead that investors measure what matters: the degree to which a given asset diversifies the main growth engine when it underperforms. This approach yields starkly different conclusions, particularly for asset pairs with low full-sample correlation. The authors review correlation mathematics, highlight the flaw in prior studies, motivate the correct approach, and present an empirical analysis of correlation asymmetry across major asset classes. <b>TOPICS:</b>Portfolio theory, portfolio construction, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ There is strong empirical evidence that asset class correlations are asymmetric, which poses complications in portfolio construction. ▪ Investors prefer diversification when a portfolio’s main growth engine performs poorly and unification when it performs well. ▪ To measure correlation asymmetry caused by nonnormality, investors must adjust for changes in correlation that arise mathematically when part of a sample is excluded. ▪ Unlike prior research, investors should condition correlations on the performance of a single asset, not two assets.