Performance of Gold as a Financial Asset During Different Phases of Financial Cycles
Aniket Ranjan and Naveen Kumar (Reserve Bank of India)
January 2022
The paper examines the fundamental relationship between gold and financial markets within the framework of unobserved components model. It measures the performance of gold as a financial asset during different phases of financial cycles (credit, equity and property). The paper explores discrete series of peaks and troughs to determine the financial cycles across markets using a combination of Baxter-King filter and Harding and Pagan’s methodology. The paper estimates the time varying coefficients by regressing gold returns on other assets like US dollar (DXY) and stocks (MSCI) to evaluate the diversifying attribute of gold. It further explores the time-dependent relationship between gold returns and financial market characteristics like liquidity, volatility and yield spread to better understand its role as a safe haven. The results show that gold does behave like a diversifying asset and can be used as a risk hedge during turbulent times. The results illustrate that this attribute is strengthened during periods of volatility which places gold as a safe haven asset. This behaviour is also reflected in the rise of gold prices after the global financial crisis as well as the most recent Covid-19 related market upheavals.

What Do the Data Tell Us About Inflation Expectations?
Francesco D’Acunto (Boston College), et al.
March 2022
Inflation expectations are central to economics because they affect the effectiveness of fiscal and monetary policy as well as realized inflation. We survey the recent literature with a focus on the inflation expectations of households. We first review standard data sources and discuss their advantages and disadvantages. We then document that household inflation expectations are biased upwards, dispersed across individuals, and volatile in the time series. We also provide evidence of systematic differences by gender, income, education, and race. Turning to the underlying expectations formation process, we highlight the role of individuals’ exposure to price signals in their daily lives, such as price changes in groceries, the role of lifetime experiences, and the role of cognition. We then discuss the literature that links inflation expectations to economic decisions at the individual level, including consumption-savings and financial decisions. We conclude with an outlook for future research.

The Predictive Power of the Oil Variance Risk Premium
David G. McMillan (U. of Stirling) and Salem Adel Ziadat (Al-Ahliyya Amman U.)
February 2022
This paper examines the ability of the oil market variance risk premium (VRP) to predict both financial and key macroeconomic series. Interest in understanding movement in such variables increasingly considers measures of investor risk and the variance risk premium, which incorporates both implied and realised volatility, has recently come for the fore. It is well established that oil price movement impacts both the stock market and wider economy and thus, we examined whether this is also true of the oil VRP. Using monthly US data over the period from 2009 to 2021, we demonstrate the nature of oil VRP predictive power for oil and stock returns, as well as output growth, unemployment, and inflation. Of notable interest, while predictability from the oil VRP series dominates at the one-month horizon and (largely) wanes at over longer time periods, the reverse is found for the stock VRP. These results are robust to the inclusion of additional, established, predictor variables. This indicates that the impact of oil market risk has a more immediate effect on both the stock market and economy, with stock market risk reflecting longer term considerations.

Commodity price uncertainty comovement: Does it matter for global economic growth?
Laurent Ferrara (SKEMA Business School), et al.
December 2021
Global economic activity is surrounded by increasing uncertainties from various sources. In this paper, we focus on commodity prices and estimate a global commodity uncer- tainty factor by capturing comovement in volatilities of major agricultural, metals and energy commodity markets through a group-specific Dynamic Factor Model. Then, by computing impulse response functions estimated using a Structural VAR model, we find that an increase in the common commodity price uncertainty results in a substantial and persistent drop in investment and trade for a set of emerging and advanced economies. We show that a global commodity uncertainty shock is more detrimental for economic growth than usual financial and economic policy uncertainty shocks. Last, our method- ology turns out to be a way to disentangle the macroeconomic effects of “good” and “bad” oil uncertainty: when an oil uncertainty shock is common to all commodities, then the macroeconomic effect is likely to be negative, but when this shock is specific to the oil market, the effect tends to be positive in the short run.

The Pricing of Geopolitical Risk in Cross-Sectional Commodity Returns
Daxuan Cheng (Macquarie University), et al.
January 2022
In this study, we investigate whether geopolitical risk is a pricing factor in cross-sectional commodity futures returns. By estimating the exposure of commodity futures returns on a geopolitical risk index, we find that commodities with high-risk beta generate 7.92% higher annual returns than those with low-risk beta. The results indicate that high geopolitical risk-related commodity futures contracts require extra compensation. A moving average procedure shows that the geopolitical risk beta has a regular changing pattern that cycles every 10 years, and the relative risk premium tends to be higher than average before economic recessions and to further increase during the recession periods. Finally, we find that geopolitical threats better explain the variation of commodity futures return than do geopolitical actions.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno