Common liquidity factor was identified to which drives the 5 commodity prices to move along a common trend

Market more (less) liquid, all CP tend to move up (down) in the same direction

Policy makers draw valuable lessons from monitoring daily commodity liquidity dynamics as timely bellwhether for incipent inflation and to more efficiently control inflation risk.

Cody and Mills 1991: A close relationship between CP and CPI

Clarida et al. 1991: CP strongly inform movements in inflation, interest rates and output (GDP)

Bhar and Hamori 2008: CP can be informative for formulating monetary policy, due to the role played as input factors to industrial production

Stock and Watson 2003: Argued that CP are useful as predictors for inflation and output growth

Aim: To identify a high-frequency market liquidity bellwhether and investigate its potential in signaling changes in CP trends

Evidence demonstrating how daily measures of liquidity observed across different commodity markets exhibit a tendency to co-move in accordance with the existence of a common liquidity factor

Shown with such a common liquidity bellwether may be underpinning much of observed co-movement of commodity prices, by presenting evidence from a series of co-integrating regressions and Granger causality test

Statistically establish the existence of significant daily price co-movement among different commodity classes

Liquidity can drive aggregate price shock, which amplify inflation risk

Additional tests further suggest that the impact of liquidity on CP co-movement is stronger in a post-2008 financial crisis sub-sample of our complete data set

Dynamic linear regression carried out on lagged liquidity variables & commodity market CRB index to control for changes in overall macroeconomic conditions

Co-integrating regression analysis similar from Leybourne et al. 1994

Establishing the existence of a co-integrating & long-run equilibrium relationship between CP co-movements and common liquidity factors

Liquidity effect on prices is not only found to spill over across different commodity sector boundaries, but also to propagate across different maturities of commodity futures securities

Establish existence and usefulness of common commodity liquidity bellwether as an important market-derived

Presence of a series of long-run co-integrating relationships between common liquidity bellwether and array of CP while establishing predictive utility

Hegerty, S. W. (2016). Commodity-price volatility and macroeconomic spillovers: Evidence from nine emerging markets. The North American Journal of Economics and Finance, 35, 23-37.

Ferraro, D., Rogoff, K., & Rossi, B. (2015). Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates. Journal of International Money and Finance, 54, 116-141.

Limitation: The existence of out-of-sample correlation is not informative regarding economic causality in the data

Monetary fundamentals do not help forecast EXR out-of-sample not even in terms of out-of-sample fit, at the monthly or quarterly frequencies

Investigates the predictive content of CPs for exchange rates (EXR)

Average commodity returns can successfully predict the level and excess returns to EXR of currencies in Australia, Canada, New Zealand and South Africa for in-sample and out-sample perspectives

Mean-variance allocating wealth between domestic and foreign bonds can improve the portfolio performance by using commodity forecasts of currency return instead of benchmark of historical average forecasts.

Shown CPs can successfully predict short-term EXR of commodity currencies, providing a potential way to partly resolve the "Meese-Rogoff" puzzle

Out-sample indicate the univariate predictive regression with commodity factors produces significantly more accurate exchange rate forecasts than benchmark of random walk model for all 4 currencies

Detected predictive content of CPs for currency excess return and significant predictability in-sample and out-sample

Dynamic portfolio formed by commodity forecasts of currency return has a higher annualized return 1.6% - 3.1% than the portfolio formed by benchmark of historical average forecasts

1. World CP fluctuations potentially explain a major component of their terms-of-trade fluctuations in these countries

2. World commodity price is exogenous to EXR to a large extent

Chen & Rogoff 2003: Pointed out that changes in WCPs are exogenous to small economies

Kilian 2009: Price of some commodities like crude oil are even exogenous to the US economy in short-term

Basher et al. 2012, 2016: Price of some commodities have important influences on EXR

3. Commodity factor addresses the model uncertainty which causes worse forecasting performance of economic models

Dangl & Halling 2012, Rapach et al. 2010: Forecast combination approaches have been widely employed in asset price forecasting studies (FAVG)

Currency predictability can be explained that CP provide useful info regrading future business changes

Kilian 2009, Wen et al. 2012, Zhang 2017, Salisu et al. 2018, Wang et al 2013, 2019: Oil price shocks have essential impacts on the variables reflecting economic activities

Verdelhan 2010: Proposes a habit model which advocates that domestic investor is more risk-averse than foreign counterparts when the economy is a recession so the author expects a positive currency excess return during bad times at home, creating the predictability of excess return

Lustig et al 2014: Found countercyclical behavior of expected return on a short position in the dollar and long position in a basket of foreign currencies

Reason: 4 countries primmary commodities account for significant fraction of their exports

Before applying Multivariate GARCH to examine spillovers among the variables

Global CPs has large increase during Global Financial Crisis & steep decline after the crash

Chile is most closely tied to copper price, Indonesia to oil and tin

Found that volatility of CPs is a significant predictor of banking crises in a sample of 60 low-income countries

In contrast to findings on advanced, emerging economies, credit booms and capital inflows do not play a significant role in predicting banking crises

Historical data 40 peripheral economies between 1848 and 1938

Effect of CPs volatility on banking crises is concentrated in LICs with fixed EXR regime and high share of primary goods in production

CPs volatility is likely to trigger financial instability through a reduction in government revenues and shortening of sovereign debt maturity, which weaken the banks' balance sheets

Motivation: zoom in experience of LICs and focus on the role CPs in driving financial sector distress

Logit model enables to maintain full sample of 60 countries including non-experienced crises episode hat estimation can be interpreted as within-country estimates

Developed a LIC-specific early warning system for systemic banking crises by Laeven & Valencia 2013

Hardy & Pazarbasioglu 1999: Banking system distress are differ across economies with different structural characteristics

Fernandez et al 2017: Since 1960s global shocks to CPs account for about one quarter of output fluctuations in LICs, they are comparable with richer countries and has significantly increased over past 15 years with financialization of commodity markets

Random effects logit model approach by Mundlak 1978 & Chamberlain 1982

Koren & Tenreyro 2007, Fernandez-Villaverde at al 2011: Shown the importance of volaitlity shocks for economic growth and development

Bloom 2009: Found uncertainty shocks including CPs have real effects on firms' hiring and investment decisions

Bleaney and Greenaway 2001, Blattman et al 2007, Williamson 2012, Cavalcanti et al 2015: Shown the volatility of CPs more than growth are matters for output fluctuations

Caprio and Klingebiel 1996a, 1996b: Documented a number of banking crises in 1980s and early 1990s and show that volatile terms of trade as associated with systemic crises, specifically on countries with a concentrated export base

2. Sharp drop in prices translate into revenues for exporting firms which is more difficult to service their debt obligations, with potential negative effects on banks asset quality

a. Emphasized key role that CPs play in triggering financial sector stress. So, this paper help whether fluctuations in CPs can help predict banking crises

a.i. Agarwal et al 2020: Shown declining CPs are associated with worsening bank health and lead to a contraction of bank lending in LICs

b. Most literature looking at advanced and emerging markets so focused in this paper towards LICs.

CPs, oil prices connectedness: post Asian Financial crisis

Past values of CPs were not good forecasts of future values of CPs cause ended up rejecting the predictive ability CPs

CP volatility spillovers to country's EXR or output but also policymakers can react and impacting interest rates and inflation

Van der Ploeg and Poelhekke 2009: Both oil and non-oil price volatility reduce economic growth and contributed to 'resource curse'

Aim: Studying the effect of CP volatility on 9 emerging markets

Monthly log changes in output, interest rates, CPs, EXR & price of each country's important commodity

Cui, J., Goh, M., & Zou, H. (2021). Coherence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets. Energy, 225, 120190.

Each country behaves idiosyncratically. EXR & inflation volatility are linked to CP fluactuations in more cases rather than output and interest rates

Examined individual results being Chile the 'commodity currency'

Chilean economy influences copper prices indicating traders recognize the country's strong ties to metal

Not all central banks must be as sensitive to CP swings as others

Defining countries by categories: Commodity economies, Diversified exporters, insulated oil exporters (less affected by oil price shocks)

In the long run, diversification into manufacturing may help protect countries from wild swings in CPs

In short run, countries must manage risk either through capital controls or inflation targeting monetary policy to avoid transmission to EXR and prices

Included Global Financial Crisis 2008-2009, European Sovereign Debt Crisis 2010-2012, Oil prices collapse 2014-2016, China's stock market crisis 2015 and ongoing Covid-19 pandemic

Teterin, P., Brooks, R., & Enders, W. (2016). Smooth volatility shifts and spillovers in US crude oil and corn futures markets. Journal of Empirical Finance, 38, 22-36.

Shah, A. A., & Dar, A. B. (2021). Exploring diversification opportunities across commodities and financial markets: Evidence from time-frequency based spillovers. Resources Policy, 74, 102317.

Biofuel production is not likely to be highly responsive to short-run oil price changes

Corn and oil future prices often move together and that both volatilities are far larger in the latter third of the sample than during the 1990s.

Wetzstein and Wetzstein 2011: Strong connection between oil and agricultural prices is a myth because of creation of biofuel capacity entails adjustment costs non-reversibilities and uncertainties

Tyner 2010, Hertel & Beckaman 2011, Saghaian 2010, Zhang et al 2010: Found that changes in government policy and non-petroleum input price changes often govern large movements in grain prices

Myers et al 2014: Use common trend-cycle decompositions and find that co-movements between energy and agricultural feedstock prices tend to dissipate in the long run.

Structural change in volatility presents in both short-term & long term futures contracts

Variance impulse response function to assess whether controlling for volatility breaks leads to reduction in volatility persistence

Connectedness between CPs and oil prices among oil-rich countries

Diebold-Yilmaz, DY Approach: Total Spillover, Directional spillover & net pairwise spillovers

To identify the connectedness between the 2 markets

Significant impact between CPs and oil. High oil prices affect commodity prices

Spillover increases at post-crisis period: relatively higher during Global financial crisis, European Debt crisis, covid 19

Higher percentage of directional spillover indicates high connectedness

Currency values has strong connection with world commodity prices and will the linkage help in forecasting inflation