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We investigate the question of dynamic allocation across a diversified range of alternative risk premia. By using a set of point-in-time indicators across macro, sentiment and valuation dimensions, we find that a majority of indicators deliver a positive information ratio for a majority of alternative risk premia over the period 2005-2020. In our empirical simulations, the macro dimension seems to have worked well, notably during recession periods. Sentiment (based on market stress and momentum) struggled during recovery periods, but added value elsewhere. Valuation has worked well from 2005 to 2013 and lost part of its appeal since then. The combination of indicators allows to deliver a higher information ratio thanks to the low correlation among them. Our research also finds that point-in-time macroeconomic variables (“nowcasters”) can add value over traditional indicators, while this improvement is not significant in the case of the market stress indicator. Forthcoming in Journal of Systematic Investing.

International audience; Using an affine model to compute the price of equities based on a dataset of macroeconomic factors, we propose a measure of equity bubbles. We use a dynamic affine term structure framework to price equity and bonds jointly, and investigate how prices are related to a set of macrofactors extracted from a large dataset of economic time series. We analyze the discrepancies between market and model implied equity prices and use them as a measure for bubbles. A bubble is diagnosed over a given period whenever the discrepancies are not stationary and impact the underlying economy consistently with the literature’s findings, increasing over the shorter term economic activity before leading to a net loss in it. We perform the analysis over 3 major US and 3 major European equity indices over the 1990–2017 period and find bubbles only for two of the US equity indices, the S&P500 and the Dow Jones. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Cross-sector volatility spillovers can both threaten the financial stability of credit markets and the diversification of a credit bond portfolio. In this article, we measure cross-sector volatility spillovers, casting light on their intensity in the US-denominated investment grade bond universe. We find that volatility spillovers are high in the US credit market and that the insurance, goods and energy sectors have been net contributors to these shocks over the 1996–2017 period. A structural analysis of the spillover history based on a three-regime multivariate VAR Markov Switching model is then proposed. It highlights that with different volatility regimes come different volatility spillover structures: the insurance and goods sectors are volatility spillover sources during crisis periods. However, according to our estimates a large portion of spillovers are non-recurring and therefore difficult to anticipate.

This paper investigates the presence of the leverage effect in commodities, in comparison with financial markets. The EGARCH model with a Mixture of Normals distribution (EGARCH-MN) is used to capture (i) heavy tails and skewness in the conditional returns, and (ii) leverage effects and time-varying long-term component in the volatility specification. Besides, the estimation strategy relies on an innovative recursive (REC) method, which allows disentangling the leverage effect from the unconditional skewness as an empirical result. When applied to a broadly diversified dataset of assets during 1995–2012, the EGARCH-MN models offers state-of-the-art specifications with leverage and fat-tailed skewed densities, that allow to contrast the specific characteristics of commodities with traditional assets (equities, bonds, FX).

Long-term investors are attuned to the thought that risk is rewarded. By making an investment with more potential variation in returns, investors demand a risk premium – the expected return in excess of a comparatively risk-less investment. This is particularly espoused in the long-term investments of pension funds, yet is a reductive view of financial markets. We investigate the realized risk premiums in a global, multi-asset portfolio of a typical pension fund between 1999–2015, and relate the variation of the realized risk premiums to macroeconomic fluctuations. Owing to the coincident relation between the realized risk premiums and the economic cycle, under the prevailing economic condition, the Sharpe ratios of portfolios constructed ex post to capitalize on risk premiums are appallingly low (between −1 and 0.2). Therefore, despite the heartening corroboration of risk premiums’ existence, investors are susceptible to their time variation.

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to one month. We extend the methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns. Considering both intra-week periodicity and signed jumps, we estimate two variants of a bivariate model of returns and volatilities where the jump component is independently modeled. Our empirical results for four futures series (S&P 500, U.S. 10-year Treasury Note, USD/CAD exchange rate and WTI crude oil) highlight the importance of considering the continuous/jump decomposition of volatility for the purpose of density forecasting. Specifically, we show that models considering jumps apart from the continuous component consistently deliver better density forecasts for horizons up to one month and a half and, in two cases out of four, for horizons up to three months.

The authors thank the participants to the following conferences: CFE 2013 in London, FEBS 2014 hosted by the Surrey University, the IAAE 2014 in London, the EEA-ESEM 2016 in Geneva Switzerland and the Paris Financial Management Conference 2016 in Paris.; International audience; This article questions the empirical usefulness of leverage effects to forecast the dynamics of equity returns. In sample, we consistently find a significant but limited contribution of leverage effects over the past 25 years of S&P 500 returns. From an out-of-sample forecasting perspective and using a variety of different models, we find no statistical or economical value in using leverage effects, provided that an asymmetric and fat-tailed conditional distribution is used. This conclusion holds both at the index level and for 70% of the individual stocks constituents of the equity index.

The short end of the yield curve incorporates essential information to forecast central banks' decisions, but in a biased manner. This article proposes a new method to forecast the Fed and the European Central Bank's decision rate by correcting the swap rates for their cyclical economic premium, using an affine term structure model. The corrected yields offer a higher out-of-sample forecasting power than the yields themselves. They also deliver forecasts that are either comparable or better than those obtained with a factor-augmented vector autoregressive model, underlining the fact that yields are likely to contain at least as much information regarding monetary policy as a dataset composed of economic data series. Copyright © 2015 John Wiley & Sons, Ltd.

This chapter covers the economic fundamentals of commodity markets (i.e., what shapes the evolution of the price of raw materials) in three steps. First, it covers the theories explaining why the futures curve can be upward or downward sloping, an essential element for commodity producing companies. The evolution of inventories and hedging pressures are the two dominant sources of explanation. Second, the chapter reviews the fundamentals of commodity spot prices: technologies, supply, demand, and speculation. Production costs draw the long-term evolution of prices, but demand and supply shocks can trigger substantial variations in commodity prices. Third, the chapter presents how commodity prices interact with the business cycle. Commodities are influenced by the world activity but can also have a material impact on it.

We investigate the question of dynamic allocation across a diversified range of alternative risk premia. By using a set of point-in-time indicators across macro, sentiment and valuation dimensions, we find that a majority of indicators deliver a positive information ratio for a majority of alternative risk premia over the period 2005-2020. In our empirical simulations, the macro dimension seems to have worked well, notably during recession periods. Sentiment (based on market stress and momentum) struggled during recovery periods, but added value elsewhere. Valuation has worked well from 2005 to 2013 and lost part of its appeal since then. The combination of indicators allows to deliver a higher information ratio thanks to the low correlation among them. Our research also finds that point-in-time macroeconomic variables (“nowcasters”) can add value over traditional indicators, while this improvement is not significant in the case of the market stress indicator. Forthcoming in Journal of Systematic Investing.

Purpose– The purpose of this paper is to contain an empirical application of the concept of “time series momentum” – as developed by Moskowitzet al.(2012) – to commodity markets with daily data during 1995-2012.Design/methodology/approach– The paper applies the new concept of “time series momentum” to the sphere of commodity markets.Findings– The paper extends the results previously obtained by Moskowitzet al.(2012) to a second category labeled “breakout strategy.”Research limitations/implications– Further management strategies can be elaborated for investment management purposes, based on the suggested inclusion of the “time series momentum” in commodities.Practical implications– The empirical evidence gathered in this paper bears practical significance for portfolio managers and commodity tradings advisors relying on trend following strategies.Originality/value– Commodity markets are quickly developing to an alternative asset class for investors. Discovering their properties and characteristics has a broad appeal in finance.

From 2008 to 2011, commodity markets experienced growing attention from the banking industry for various reasons: the summer 2008 oil price swing, the price surge in an ounce of gold, or sharp variations in agricultural prices. As a consequence, can we hypothesize the existence of a global connection between commodities and economic cycles? If these recent events suggest that commodity markets are strongly related to the business cycle, this evidence goes nevertheless against the widespread intuition that commodity markets are a strong source of diversification in a standard cash–bond–equity portfolio. Based on a data-set from 1990 to present, this paper investigates this issue by (i) looking at the reaction of commodity markets to economic news, and (ii) using a Markov regime-switching model to analyse economic regimes and commodity markets as an asset class.

This article investigates momentum strategies in commodity markets. Using a Markov-switching model and formal tests for the number of regimes in the data, we identify momentum trends for a variety of commodities, exchange rates, interest rates and equities. The data cover the period 1995–2012 at a daily frequency. The results shed light on the key differences between commodities and standard assets with regard to the presence of trends, mean-reverting behaviour and number of regimes that would need to be accurately taken into account to build profitable trend-following strategies. The results are also of economic significance for researchers interested in the modelling of commodity time series.

This article investigates volatility spillovers in commodity markets by following the methodology pioneered in Diebold and Yilmaz (2012). By using a broad data set during 1995–2012, we address three key research questions: are there volatility spillovers within commodities? between standard assets and commodities? between commodities and commodity currencies? The main results indicate first that commodities exhibit weaker than other asset classes volatility spillovers. These spillovers have, however, been increasing over the period. Second, agricultural commodities are the commodities exhibiting the lowest spillovers, whereas precious metals and energy are the biggest net contributors. In a diversified portfolio, including commodities – and especially agricultural products – helps decreasing the total spillover index. This stylized fact has, however, been less and less valid over the years. Third, some currencies are more responsive than others to commodity volatility spillovers.

In this article, we provide statistical evidence around jumps affecting commodity returns. Using nearly 20 years of daily data, we use Laurent, Lecourt, and Palm's (2011) methodology to jump extraction, and discuss various aspects of the estimated jump activity. On average across various commodity markets, we find a high number of days for which returns exhibit the presence of jumps, consistently with the intuition that commodities are affected by large price fluctuations. We emphasize that the post-jump average return depends on the commodity sector considered (e.g. agriculture, energy, or metals). We also show evidence of a jump-to-volatility channel for commodities (similar to the effect usually found for equities). Finally, we diagnose around 40 dates during which commodity indices, stocks, bonds and currencies `co-jump’, revealing a tail dependence between standard and alternative assets.

This article assesses the cross-market linkages between commodities, stocks and bonds in a cointegration framework during 1993–2011.

In investment-decision-making, it is customary to define economic regimes of high or low realized risk premiums with economic indicators such as inflation and growth. We generalize this discrete categorization to a continuous one by estimating a linear relation between economic fundamentals and realized standard and alternative risk premiums. Using a bootstrap method, we estimate that changes in macroeconomic indicators explain about 50% of the variation in realized risk premiums. We apply our estimates to comment on the implication of a prolonged low growth scenario on the prospective level of risk premiums.

How do we transform investment ideas into a real portfolio? How do we turn views and predictions into effective positions? This essential part of an investment process is called the allocation procedure. A investment process needs a cautious, well-designed allocation scheme whose engineering is at least as important as any other part. A badly implemented idea is doomed to lose its potential profitability. We can summarize our view on allocation in simple terms: allocating is finding degrees of freedom in the markets, those degrees of freedom being materialized by portfolios of assets. Allocating is choosing between these portfolios while searching for potential expositions and correlations between them in a statistically uncertain environment. In fact, allocation may be purely an estimation challenge.

An investment process, as sharp and smart as it may be, requires proper ex-post analysis in order to assess its performance in a backtest exercise. A backtest is a simulated strategy on historical data applied to fictional money. Such an approach is quite universal and used from individual traders to the biggest funds. It may be used either by small funds without sufficient seeding or by larger ones testing for candidates strategies for production. In each case, an in-depth analysis of the strategy is carried out. The problem is of utmost importance for young funds. Inception and seeding process of funds is sometimes a closed loop: investors seek to invest in sufficiently large funds, postponing investment in some of them until they become sufficiently large! There is a real need for managers to adopt a critical approach towards their simulations.

Alternative risk premia investing has grown rapidly in popularity in the investment community in recent years. They encompass solutions mimicking investment strategies formerly available through investment in hedge fund vehicles but proposed with terms more favorable to investors, notably in terms of liquidity or management fees.

The ex ante future distribution of the portfolio’s P&L is the relevant objective function that a portfolio manager looks at before making an investment decision. Tracking Error Volatility (TEV) or Value at Risk (VaR) are synthetic risk statistics commonly used to measure the risk of the P&L distribution yet to come. Portfolio optimization, including mean-variance portfolios, heavily relies on the relevance of the ex ante P&L distribution. This chapter introduces key concepts regarding a sound projection of the ex ante P&L distribution and discusses the relevant modeling approaches. It brings forward the building blocks of a comprehensive framework while pointing out the pitfalls of each step.

Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to one month. We extend the methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns. Considering both intra-week periodicity and signed jumps, we estimate two variants of a bivariate model of returns and volatilities where the jump component is independently modeled. Our empirical results for four futures series (S&P 500, U.S. 10-year Treasury, USD/CAD exchange rate and WTI crude oil) highlight the importance of considering the continuous/jump decomposition of volatility for the purpose of density forecasting. Specifically, we show that models considering jumps apart from the continuous component consistently deliver better density forecasts for horizons up to one month and a half and, in two cases out of four, for horizons up to three months.

One important step of any active strategy – quantitative or not – is to understand how different types of environments can have an impact on the strategy’s profits and losses. Voluntarily dismissed the study of the nature of the strategy to consider strategy performance as a statistical object. Yet, if a strategy is statistically a performing one, what are its links towards the real economy? What is its sensitivity to economic conditions? One of the least explored topics in the academic research literature is the connection between financial assets’ cycles and macroeconomic ones. One of the main explanations for that is probably to be found in the disappointing empirical results obtained across various research articles, in spite of the very intuitive idea that both cycles should be connected. When only focusing on equities, the intuition is that, during periods of growth, earnings should be getting more and more positive, and equity prices should consequently trend upward. On the contrary, during periods of recession, the very same earnings should deteriorate, leading equity prices into a slump. What is more, depending on how deep the recession is, the scale of the impact of the economic downturn should be different. From this perspective, the 2001 versus 2008 US recession episodes reinforce the impression that economic and market cycles should be connected.

With a variety of asset classes with different liquidities, constraints and characteristics, and a large number of investors with different investment horizons, speaking in general of financial returns is rather difficult. Finance is not a hard science and no statistical pattern appears with certainty and perfect regularity in the data. However, it has been known for a long time that financial returns show some stylized facts . Stylized facts are statistical patterns that tend to repeat in the data, for different financial instruments (stocks, indices, etc.) and markets, frequently but without certainty as they may be unobserved in some periods or under some extreme market conditions. Were the first to empirically question the Gaussian random walk hypothesis for prices, bringing to light various statistical properties of asset returns. Their studies paved the way to intensive empirical works trying to exhibit statistical regularities common across a wide range of financial data sets, as presented in clearly stating that there is a trade-off between the potential universality of the qualitative qualification and the quantitative precision when characterizing stylized facts).

This article aims at establishing an understanding of the common risk factors in commodity markets, as well as their interactions with equities, currencies and interest rates. Since commodity markets often exhibit cross-sectional dependency, common risk factors exist and can be identified. By using daily data from 1995 to 2012, the econometric methodology resorts to factor modeling combined with a criterion to determine the number of factors presented in Alessi et al.(2010). The operational significance of the results is to evaluate risk-adjusted performance of portfolios allocated to commodities, and to help building cross-asset strategies. Investors can then pinpoint the correlation between any two-position taken within commodity markets, and attempt to profitably exploit the common sources of risk. In turn, it should provide the researcher with an increased understanding of the risks at work in the commodity world.

Intraday volatility measures have recently become the norm in risk measurement and forecasting. This article empirically investigates the unbiasedness of three of these measures over four different datasets. We find that the three measures are significantly biased and that the bias can have either sign.

This article proposes a simple rule to implement dynamic stop-loss strategies in the case of a long-only S&P 500 portfolio, that is a problem market participants such as pension funds have to ponder when investing in US equity. It is based on a Monte Carlo analysis, as advised in Phoa [1999].

Both domestic economies and financial markets are affected by cycles that are often represented through multi-state models such as Markov Switching (MS) models. This article discusses the performances associated to the government bond, the equity and the credit cases along the business cycle, using both an European and a US dataset over the 1987 to 2010 period. Periods of noninflationary growth have been strongly supportive to the credit universe, whereas inflationary growth has led to a strong performance of the equity asset class. On the contrary, recession periods are characterized by strong performances from government and investment grade bonds. These statements hold both in the US and in the European cases.

We question the ability of macroeconomic data to predict risk appetite and ‘flight-to-quality’ periods in the European credit market using a model inspired by the Markov switching literature. This model allows for a direct mapping of exogenous variables into state probabilities. We find that various surveys and transformed hard data have a forecasting power. We show that despite its depth, the 2008–2009 crisis should not be regarded as an unusual episode that would have to be modelled by an additional state. Finally, we show that our model outperforms a pure Markov switching model in terms of forecasting accuracy, thus clearly indicating that economic figures are helpful in forecasting the credit cycle. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we quantify the impact on the representative agent's welfare of the presence of derivative products spanning covariance risk. In an asset allocation framework with stochastic (co)variances, we allow the agent to invest not only in the stocks but also in the associated variance swaps. We solve this optimal portfolio allocation program using the Wishart Affine Stochastic Correlation framework, as introduced in Da Fonseca, Grasselli and Tebaldi (2007): it shares the analytical tractability of the single-asset counterpart represented by the [36] model and it seems to be the natural framework for studying multivariate problems when volatilities as well as correlations are stochastic. What is more, this framework shows how variance swaps can implicitly span the covariance risk. We provide the explicit solution to the portfolio optimization problem and we discuss the structure of the portfolio loadings with respect to model parameters. Using real data on major indexes, we find that the impact of covariance risk on the optimal strategy is huge. It first leads to a portfolio that is mostly driven by the market price of volatility-covolatility risks. It is then strongly leveraged through variance swaps, thus leading to a much higher utility, when compared to the case when investing in such derivatives is not possible.

In this paper, we present a new stylized fact for options whose underlying asset is a stock index. Extracting implied volatility time series from call and put options on the Deutscher Aktien index (DAX) and financial times stock exchange index (FTSE), we show that the persistence of these volatilities depends on the moneyness of the options used for its computation. Using a functional autoregressive model, we show that this effect is statistically significant. Surprisingly, we show that the diffusion-based stochastic volatility models are not consistent with this stylized fact. Finally, we argue that adding jumps to a diffusion-based volatility model help recovering this volatility pattern. This suggests that the persistence of implied volatilities can be related to the tails of the underlying volatility process: this corroborates the intuition that the liquidity of the options across moneynesses introduces an additional risk factor to the one usually considered.

This chapter aims to build a connection between commodities’ price movements and the growth level in industrial production in different economic regions. Commodities should only be incorporated in a diversified strategy as long they either have a poor correlation to standard assets such as equities, or offer an attractive risk premium. We demonstrate that the part of commodity return not attributed to the roll yield is related to economic activities in the U.S. and China. 40 to 50% of variation in commodities returns are explained by industrial production growth in those both regions. The slow-down of economic activity implies lower excess returns on commodities. Similar to the risk premium on equities and credit, the commodity risk premium is dependent on the economic environment. This finding casts doubt on both commodities’ diversification potential, and investor’s interest in this asset class in terms of expected risk premium, given the current low world structural growth.

This article explores the commodity–industrial production nexus. More precisely, we assess the cointegration relationships between commodity markets and industrial production during 1993-2011, with an overview for several countries: the USA, the EU, Australia, China, Brazil, Canada, Germany. First, we explore the descriptive statistics and unit root tests of the dataset. Second, we develop two kinds of cointegration analyses (e.g. with/without structural break) between commodities on the one hand, and industrial production indices on the other hand. Third, we conclude on the main results achieved by this econometric procedure. The key contribution of our paper is to revisit the link between industrial production and commodity prices, by using an econometric methodology incorporating structural breaks, and by using a very recently updated dataset. By carrying out a systematic comparison between our results and papers previously published in this literature, we gain a wealth of insights.

The evaluation of financial risks and the pricing of financial derivatives are based on statistical models trying to encompass the main features of underlying asset prices. From the seminal works of Bachelier (Ann Sci Ecole Norm Super 17:21–86, 1900) based on Gaussian distributions, the random walk hypothesis for the returns or the log-returns has frequently been suggested. Its remarkable mathematical tractability, in particular in the multidimensional case, was the keystone of nice financial theories like Markowitz’s (Portfolio selection: efficient diversification of investments. Wiley, New York, 1959) portfolio management or Black and Scholes (J Polit Econ 81:637–659, 1973) option pricing model, among others. Nevertheless, during the last decades, the explosion of computational tools efficiency has allowed researchers to pay more attention to the analysis of financial datasets and the test of models assumptions. It is now well-documented that in spite of their huge heterogeneity concerning the nature of financial assets (stocks, commodities, interest rates, currencies…), the frequency of observations or the multiplication of financial centers, financial time series exhibit common statistical regularities (called stylized facts) that make satisfactory models difficult to obtain. A major attempt in this direction was done during the 1980s by Engle (Econometrica 50:987–1007, 1982) and Bollerslev (J Econ 31:307–327, 1986) through the ARCH/GARCH approach. After a brief reminder of the classical stylized facts observed for the daily log-returns of financial indices, the aim of the chapter is to present the main features of the GARCH modelling approach and its recent extensions.