Cross-asset Correlations Research Articles

Economic policy uncertainty and dynamic correlations in energy markets: Assessment and solutions

The objective of this study is twofold. On the one hand, we investigate the dynamic correlations among the energy, clean energy, crude oil, and carbon markets using a DCC-MIDAS specification. On the other hand, we check whether the Economic Policy Uncertainty (EPU) has had an impact on the cross-asset correlations under different market conditions using the Quantile-on-Quantile (QQ) technique. Further, we comparatively compare the hedging potential and diversification performance of these energy sources in a context of uncertainty. Accordingly, we, first, show that the connections among the four markets have time-varying characteristics with positive and negative alternating changes in most periods. Random events might cause ephemeral structural changes in correlations. Second, we identify different ways of hedging specific to the energy assets under consideration that provides an efficient risk management framework. Third, we find that the EPU has an asymmetrical and often a positive impact on energy cross-market linkages. Particularly, when macroeconomy facing high uncertainty, then markets become more inseparable.

Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond

Vigorously developing the clean energy industry, improving the carbon allowance trading scheme, and issuing green bonds can effectively reduce emissions. To this end, this study aims to investigate the time-varying connections among clean energy, carbon, and green bonds through the DCC-MIDAS model, thus providing a bird's-eye view of their dynamic nexus. A non-parametric causality-in-quantile method is also employed to adequately capture the asymmetric causation of economic policy uncertainty (EPU) and the oil volatility index (OVX) on cross-asset correlations under different market conditions. The primary results imply complicated links among these three assets, with alternating positive and negative trends throughout the sample period. Notably, turbulence in financial markets can exacerbate network connectivity, particularly during the COVID-19 pandemic. Moreover, EPU and OVX can serve as strong predictors across various distributions of cross-market connections, which indicates that co-movement between assets is vulnerable to exogenous risks, especially under normal market conditions. Our findings have broader implications for market participants and policymakers.

On the Economic fundamentals behind the Dynamic Equicorrelations among Asset classes: Global evidence from Equities, Real estate, and Commodities

We reveal the macroeconomic determinants of the dynamic correlations between three global asset markets: equities, real estate, and commodities. Conditional equicorrelations, computed by the GJR-GARCH-DECO model, are explained by the macro-financial proxies of economic policy and financial uncertainty, credit conditions, economic activity, business and consumer confidence, and geopolitical risk. Our results suggest that elevated cross-asset correlations are associated with higher uncertainty, tighter credit conditions, and lower geopolitical risk, while lower correlations are related to stronger economic activity, business, and consumer confidence. We further focus on economic policy uncertainty (EPU) as a potent catalyst of the asset markets integration process and conclude that EPU magnifies all macro-effects across all correlations. Lastly, we investigate the global financial crisis effect on the time-varying impact of the correlations’ macro-drivers. The crisis structural break amplifies the influence that all determinants exert on the evolution of correlations apart from the geopolitical risk upshot, which is alleviated after the crisis advent.

Diversification Benefits from Leveraged Loans? Trends in Cross-Asset Correlations

The concept of diversification is well understood by both academics and investment professionals. In forming a mean-variance efficient portfolio, investment managers look for low-correlated assets in order to maximize diversification benefits. This article investigates the potential of European and US leveraged loans to serve as diversifiers in a multi-asset portfolio. By calculating correlations between leveraged loans and other asset classes over the sample period from 2002 to 2020, the authors are not only able to demonstrate increasing correlations in highly volatile markets (i.e., financial crises), but also long-term, crisis-independent positive trends in asset correlations. In particular, leveraged loans and their correlations with stocks and high-yield bonds present significantly positive long-term trends and hence diminishing diversification benefits. The authors state that instead of short-term market volatility, increasing market integration could be the driving force behind this effect. The results also indicate that diversification benefits from leveraged loans are lower in the US than in the European market. TOPICS:Fixed income and structured finance, developed markets, portfolio construction, performance measurement Key Findings ▪ The empirical analysis shows generally stable and low correlations of leveraged loans with most asset classes in European and US markets. Stocks and high-yield bonds are an exception, showing a pattern with rising long-term correlation coefficients. ▪ The correlation of leveraged loans with high-yield bonds is higher than those with other asset classes due to their comparable characteristics. Correlations between those asset classes have risen, especially since the global financial crisis in 2008. ▪ Due to higher market volumes and integration advantages in the US, the correlations of leveraged loans with other assets are higher in the US market, leading to lower diversification benefits compared to Europe.

Decoding Systematic Relative Investing: A Pairs Approach

We propose a novel theory that brings to light three fundamental performance drivers of zero-cost systematic investment strategies: (1) high (positive) own-asset signal-return predictability; (2) low (or negative) cross-asset signal correlation; and (3) low (or negative) cross-asset signal-return predictability. We develop these insights in the context of long-short pair strategies used as portfolio building blocks. We test our approach empirically using momentum signals for major asset classes, though our method can generalize to any signal. Our investable pairs-based portfolio harvests over double the average returns of a conventional rank-based portfolio over the last 20 years.

Asset prices in segmented and integrated markets

This paper evaluates the effect of market integration on prices and welfare, in a model where two Lucas trees grow in separate regions with similar investors. We find equilibrium asset price dynamics and welfare both in segmentation, when each region holds its own asset and consumes its dividend, and in integration, when both regions trade both assets and consume both dividends. Integration always increases welfare. Asset prices may increase or decrease, depending on the time of integration, but decrease on average. Cross-asset correlation is zero or negative before integration, but significantly positive afterwards, explaining some effects commonly associated with financialisation.

Strategic Currency Hedging in Multi-Asset Portfolios

The authors provide a strategic currency hedging framework for multi-asset portfolios, helping global investors better manage foreign currency exposures by balancing hedging costs and risk. The authors focus primarily on G4 currencies—US dollar (USD), euro (EUR), pound sterling (GBP), and Japanese yen (JPY)—but also provide some insights for other G7 currencies. Starting from a single-asset-class perspective, the framework shows that developed market fixed-income instruments should generally be fully hedged back to the investor currency. US investors should fully hedge foreign exchange risk when buying European equities. Japanese equities should be left unhedged for all investors because of the Japanese yen’s value as a safe haven during market panics. Conversely, Japanese investors buying international equities should fully hedge currency risk but may find it optimal to leave some of their US bond exposures unhedged to minimize hedging costs while retaining some diversification benefits. The picture is also nuanced for European investors where the cost-benefit trade-off points to partial hedging as the optimal approach. When it comes to multi-asset investing, hedge ratio optimality also becomes a function of cross-asset correlations, which leads to interesting nuances of approach across domiciles. The authors believe their research provides global investors with useful information to calibrate strategic hedge ratios. <b>TOPICS:</b>Currency, portfolio management/multi-asset allocation <b>Key Findings</b> • We find the optimal hedge ratio dynamically derived from exploring the trade-off between hedging costs and risk minimization generally leads to superior risk adjusted returns than a static approach to currency hedging. • Our mean-variance optimization framework allows practitioners to set a flexible currency hedging policy based on their own risk preference and is applicable to single asset and multi-asset portfolios. The hedge ratio decomposition we propose sheds light on the drivers of the optimal hedge ratio and gives investors the ability to further calibrate their currency hedging policy by putting more emphasis on the driver they deem the most important. • We find developed markets fixed-income instruments should generally be fully foreign exchange (FX) hedged. US investors should fully hedge FX risk when buying European equities. Japanese equities should be left unhedged for all investors due to the Japanese yen’s risk-off characteristics. Conversely, Japanese investors buying international equities should fully hedge currency risk but may find it optimal to leave some of their US bond exposures unhedged to minimize hedging costs while retaining some diversification benefits. The picture is also nuanced for European investors where the cost-benefit trade-off points to partial hedging as the optimal approach. When it comes to multi-asset investing, hedge ratio optimality also becomes a function of cross-asset correlations, which leads to interesting trade-offs.

Risk-Parity Optimality Even with Negative Sharpe Ratio Assets

A stopped clock is right twice a day. Similarly, any portfolio allocation is likely to be optimal at least at some point. Risk parity is no stopped clock. The authors derive a general result regarding when and why risk parity is Sharpe ratio optimal, even with negative-Sharpe-ratio assets. This derivation goes beyond the simple observation that risk parity is Sharpe ratio optimal when asset correlations and Sharpe ratios are identical. Based on the analytical result, the authors develop an indicator to describe when risk-parity strategies are likely to be more or less optimal. They also explain how negative-Sharpe-ratio assets can still be an important part of a portfolio—whether it is a risk-parity portfolio or not. Although risk parity and risk balancing in general do not require assumptions about returns, the authors provide guidance regarding how to infer returns that are consistent with the portfolios built from targeting risk. TOPICS:Statistical methods, portfolio theory, portfolio construction Key Findings • Conditions that are generally sufficient for risk-parity portfolios to be Sharpe ratio optimal are provided, beyond the special case of identical cross-asset correlations and Sharpe ratios. From this, an indicator of how likely risk parity is to be Sharpe ratio optimal can be derived. • Based on diversification properties of assets, the generally sufficient conditions for risk-parity portfolios to be Sharpe ratio optimal explain why even negative-return assets can be included in a diversified portfolio. • A method is provided to translate risk targets into a rank ordering of asset returns. With an additional set of assumptions, these rank orderings can be translated into returns for a coherent set of risk targets, risks, and returns.

Explaining the stock-stock, bond-bond and stock-bond correlation across countries

This paper examines the behaviour of the same asset-cross country and cross-asset same country correlations for stocks and bond for four (Germany, Japan, UK, USA) major economies. Using the realised volatility methodology to construct time-varying correlations, the results reveal that rising same asset correlations occur when cross-asset correlations fall. While there is evidence of segmentation of Japanese assets within international markets. We seek to explain the movement in correlations and note that the variables that exhibit a positive predictive relation for the stock-bond correlation, exhibit a negative predictive relation for the stock-stock and bond-bond correlations and that this is linked to economic conditions. Within this, four variables (inflation, stock returns, consumer sentiment and purchasing managers index) exhibit consistent significance across the regressions. Using these variables, we construct a correlation indicator variable that is used to construct a switching portfolio. This constructed portfolio constructed outperforms buy-and-hold alternatives.

Managing Capital Market Risk for Retirement

We offer an overview of solutions available to pension plans to manage capital market risk in order to meet their obligations. We outline the main drivers behind the evolution of asset-liability management (ALM) for pension plans and the emergence of liability-driven investment (LDI) in the last decade. We look at some of the most popular pension de-risking tools and at recent innovations prompted by the Global Financial Crisis. We offer examples based on the rise of cross-asset correlation, the use of hybrid products to mitigate tail risk, and the increasing relevance of counterparty risk mitigation tools such as collateralization. We conclude by outlining some of the main challenges ahead, including developments in pension regulation, centralized clearing of over-the-counter (OTC) instruments, and risk taking incentives in delegated asset management for long term retirement obligations.

Determinants of stock-bond market comovement in the Eurozone under model uncertainty

This paper examines the dynamic relationship between stock and bond returns in eleven Eurozone countries during the last seventeen years. The literature so far reports heterogeneous results with respect to the important determinants of the stock-bond relationship. To deal with model uncertainty we employ a Bayesian model averaging technique and examine various macroeconomic and financial variables which are likely to influence stock-bond comovement. Bond and stock market uncertainty, interest rate, inflation and state of the economy are important determinants of cross-asset correlations. Divergence in the dynamic patterns of stock-bond comovement as well as on the effect of economic variables on this comovement is reported during crisis periods and between different European regions. Our results are of high relevance for investment strategies as well as for policy decisions in the European context.

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.

An Intertemporal Study of ETF Liquidityand Underlying Factor Transition, 2009–2014

This article seeks to determine the migration of exchange-traded fund (ETF) liquidity and its factor constituents in the U.S. market over time, with the ultimate goal of making the ETF market more efficient and transparent. Using a set of factors commonly thought to impact liquidity, the authors develop a four-factor liquidity scoring algorithm (A-C liquidity score), extending the 2009 study by Agrrawal and Clark. The most liquid ETFs typically have lower bid–ask spreads, higher market capitalizations, lower expense ratios, and higher average trading volumes. The transition of ETF liquidity over the 2009–2014 period indicates that there is liquidity persistence and factor strengthening across all variables. Bid–ask spreads and expense ratios have compressed, which is a good trend for investors. Asset size and trading volume have gone up, which can be indicative of greater market participation and possibly high-frequency trading in the ETF environment. From 2009 to 2014, the ETF asset class has witnessed a phenomenal 34.9% annual growth rate, compared with a 19.8% growth rate in the Russell 3000 (the aggregate market capitalization for the ETF dataset increased from $438 billion in 2009 to $2,112 billion in 2014). The ETF ecosystem seems to be thriving as the new asset class of choice among both institutional and informed retail investors. However, there are two findings of concern. First, the overall beta of the ETFs in the study seems to have transitioned over time and moved up more than 9%. This may be due to increased cross-asset correlations, sector intertwining, global market cointegration, overlapping core constituents in ETFs, or simply a systematic upward drift in overall market risk premium; this limits portfolio diversification benefits. Second, over the five-year period, 25.7% of ETFs in the initial 622 ETF dataset (2009) were liquidated. This is an additional risk that investors and regulators have to keep in mind when evaluating ETFs. The cost of poor liquidity, overlooked by some retail investors, is often in the form of shallow market depth, high expense ratios, and a range of trade execution difficulties, besides potentially high tracking errors with the associated benchmark index. In conclusion, although ETF liquidity has grown significantly over time for most assets, there is some natural pruning, with pockets of concern. Overall, ETFs have rapidly become a viable and efficient instrument in the asset management landscape. <b>TOPICS:</b>Exchange-traded funds and applications, factor-based models, portfolio construction

Indonesia's Rupiah and Capital Market Correlation Risk Study for Macroprudential Policy to Prevent Systemic Risk

On the last 3 months, there have been some concerns over the weakening of Indonesian Rupiah currency that mostly driven by bearish trend in Indonesia’s equity market. Though the explanation of this correlation is known mostly due to foreign capital flow in the equity market (hot money), it appears that correlation risk between currency rates, with mostly impacted USD/IDR rate, and capital market is not well-studied and modeled in Indonesian market. The difficulty of the investigation would be to build model that would fit to the observed dynamics of both time-series and computational difficulties. This study will show how the cross-asset correlation between Indonesia’s Rupiah strength and capital market behaves along time. In this paper, auto-correlation was filtered out from USD/IDR rate and JCI (Jakarta Composite Index) daily returns from 2003-2013 data using ARMA-GARCH type modeling and the filtered “white noise” of the fitted data will be used to check indication of the correlated “innovation” existence that will be used in the proposed model. In addition, an empirical study will evidence an indication of global crisis spillover effect to Indonesian Rupiah currency due to JCI correlations with major world market indices, to express indirect effect of the cross-asset correlation risk. The cross-asset correlation is proposed to be modeled using a multi-factor Dupire model with correlated two Brownian Motions of the equity (JCI) and currency (USD/IDR) components for derivative pricing activity. Lastly, several important points are proposed to note regarding macroprudential measures to be taken in accordance to the shown correlation risk.

Realizing the volatility impacts of sovereign credit ratings information on equity and currency markets: Evidence from the Asian Financial Crisis

We examine the effects of different types of sovereign rating announcements on realized stock and currency market volatilities and cross-asset correlations around periods of financial crises. Using intraday market data and sovereign ratings data for nine sample countries in the Asia-Pacific region over 1997–2001, we find that currency and stock markets react somewhat heterogeneously to various rating announcements and that stock markets are more responsive to rating news than currency markets. We find new evidence that ratings events have significant and asymmetric impacts on intraday market data and that national market attributes influence rating impacts during financial crises.

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

This study models the role of public news arrivals on asset correlation in a trading environment populated by computerized algorithms. The model is empirically tested with the individual stock futures and its underlying spot markets, which are characterized by the mechanical cost-of-carry relation that is typically exploited by algorithmic trading. In normal circumstances, the return correlation between the stock futures and spot quotes is nearly perfect, because futures market makers peg their quotes to those of the underlying by using computerized algorithms. Our simple model predicts that this near-perfect correlation can occasionally break down with two conditions: one, the futures market is less liquid than the corresponding spot market; and two, the uncertainty surrounding the impact of the news on the underlying stocks is sufficiently large. This breakdown occurs because the futures market makers switch from automating the quote-matching process to manually monitor and update their quotes. By employing the comprehensive RavenPack database with firm-level news releases, we test and confirm our model predictions. In particular, the spot-futures return correlation falls as the news uncertainty rises, and this correlation breakdown is more prominent for small-cap stocks. Furthermore, for actively traded stocks, the impact of the news on the breakdown is more intense. If the overall stock market experiences extreme turbulence, however, this impact is weaker. We discuss the implications for the limits of algorithmic trading.

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

A PARSIMONIOUS MULTI-ASSET HESTON MODEL: CALIBRATION AND DERIVATIVE PRICING

We propose a parsimonious multi-asset Heston model and provide an easy-to-implement calibration algorithm. The model is customized to pricing multi-asset options in markets with liquidly traded single-asset options but no liquidly traded cross-asset options. In this situation, single-asset model parameters can be calibrated from option price data, however, cross-asset parameters cannot. We formulate a parsimonious model specification such that all single-asset models are Heston models, which are affine allowing for efficient calibration of the respective parameters. The single-asset models are correlated using cross-asset correlations only. Cross-asset correlations are observable, in contrast to correlations of latent variables such as volatilities, and serve as basis for calibration. A hybrid calibration approach for identifying the model parameters consistent with option price data and asset price data is outlined and illustrated by a case study. In banking practice the approach is referred to as correlation adjustment.

A Parsimonious Multi-Asset Heston Model: Calibration and Derivative Pricing

We present a parsimonious multi-asset Heston model. All single-asset sub-models follow the well-known Heston dynamics and their parameters are typically calibrated on implied market volatilities. We focus on the calibration of the correlation structure between the single-asset marginals in the absence of sufficient liquid cross-asset option price data. The presented model is parsimonious in the sense that d(d - 1)/2 asset-asset cross-correlations are required for a d-asset Heston model. In order to calibrate the model, we present two general setups corresponding to relevant practical situations: (1) when the empirical cross-asset correlations in the risk neutral world are given by the user and we need to calibrate the correlations between the driving Brownian motions or (2) when they have to be estimated from the historical time series. The theoretical background, including the ergodicity of the multidimensional CIR process, for the proposed estimators is also studied.