By using our newly defined measure, we detect and quantify asymmetries in the volatility spillovers of petroleum commodities: crude oil, gasoline, and heating oil. The increase in volatility ...spillovers after 2001 correlates with the progressive financialization of the commodities. Further, increasing spillovers from volatility among petroleum commodities substantially change their pattern after 2008 (the financial crisis and advent of tight oil production). After 2008, asymmetries in spillovers markedly declined in terms of total as well as directional spillovers. In terms of asymmetries we also show that overall volatility spillovers due to negative (price) returns materialize to a greater degree than volatility spillovers due to positive returns. An analysis of directional spillovers reveals that no petroleum commodity dominates other commodities in terms of general spillover transmission.
Summary
In this paper, we introduce quantile coherency to measure general dependence structures emerging in the joint distribution in the frequency domain and argue that this type of dependence is ...natural for economic time series but remains invisible when only the traditional analysis is employed. We define estimators that capture the general dependence structure, provide a detailed analysis of their asymptotic properties, and discuss how to conduct inference for a general class of possibly nonlinear processes. In an empirical illustration we examine the dependence of bivariate stock market returns and shed new light on measurement of tail risk in financial markets. We also provide a modelling exercise to illustrate how applied researchers can benefit from using quantile coherency when assessing time series models.
•The forex market exhibits asymmetric volatility connectedness.•We use high-frequency data of the most actively traded currencies over 2007–2015.•We document that the negative spillovers dominate ...positive spillovers.•Positive spillovers are correlated with the subprime crisis.•Negative spillovers are chiefly tied to the dragging sovereign debt crisis in Europe.
We show how bad and good volatility propagate through the forex market, i.e., we provide evidence for asymmetric volatility connectedness on the forex market. Using high-frequency, intra-day data of the most actively traded currencies over 2007–2015 we document the dominating asymmetries in spillovers that are due to bad, rather than good, volatility. We also show that negative spillovers are chiefly tied to the dragging sovereign debt crisis in Europe while positive spillovers are correlated with the subprime crisis, different monetary policies among key world central banks, and developments on commodities markets. It seems that a combination of monetary and real-economy events is behind the positive asymmetries in volatility spillovers, while fiscal factors are linked with negative spillovers.
In this paper, we examine how to quantify asymmetries in volatility spillovers that emerge due to bad and good volatility. Using data covering most liquid U.S. stocks in seven sectors, we provide ...ample evidence of the asymmetric connectedness of stocks at the disaggregate level. Moreover, the spillovers of bad and good volatility are transmitted at different magnitudes that sizably change over time in different sectors. While negative spillovers are often of substantial magnitudes, they do not strictly dominate positive spillovers. We find that the overall intra-market connectedness of U.S. stocks increased substantially during the recent financial crisis.
•We suggest how to quantify asymmetries in volatility spillovers.•Asymmetries emerge due to bad and good volatility.•Asymmetric connectedness is evidenced for most liquid U.S. stocks in seven sectors.•Spillovers are transmitted at magnitudes that change over time in different sectors.•Negative spillovers do not strictly dominate positive spillovers.
Asymmetric Network Connectedness of Fears Baruník, Jozef; Bevilacqua, Mattia; Tunaru, Radu
The review of economics and statistics,
11/2022, Volume:
104, Issue:
6
Journal Article
Peer reviewed
Open access
This paper introduces forward-looking measures of the network connectedness of fears in the financial system arising due to the good and bad beliefs of market participants about uncertainty that ...spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put traded option prices of the main U.S. banks contains valuable information for predicting macroeconomic conditions and economic uncertainty, and it can serve as a tool for forward-looking systemic risk monitoring.
Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission ...mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as demand-side benchmarks, we document new stylized facts about cyclical properties of the transmission mechanism generated by volatility shocks with heterogeneous frequency responses. Our first key finding is that shocks to volatility with response shorter than one week are increasingly important to the transmission mechanism over the studied period. Second, demand-side shocks to volatility are becoming increasingly important in creating short-run connectedness. Third, the supply-side shocks to volatility resonating in both the long run and short run are important sources of connectedness.
•We suggest how to quantify cyclical properties supply-side and demand-side shocks.•New stylized facts about cyclical properties of the transmission mechanism are documented.•Shocks to volatility have heterogeneous frequency responses.•Shocks to volatility with response shorter than one week are increasingly important.•Demand-side shocks to volatility are becoming increasingly important in creating short-run connectedness.
•We analyse term structure of crude oil markets.•New model for forecasting based on neural networks is proposed.•We show that even basic architecture of neural models performs very well against ...benchmarking models.
The paper contributes to the limited literature modelling the term structure of crude oil markets. We explain the term structure of crude oil prices using the dynamic Nelson–Siegel model and propose to forecast oil prices using a generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month-, 3-month-, 6-month- and 12-month-ahead forecasts obtained from a focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.
We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007–2017. By employing variance decompositions and their ...spectral representation in combination with realized semivariances to account for asymmetric and frequency connectedness, we obtain interesting results. We show that divergence in monetary policy regimes affects forex volatility spillovers but that adding oil to a forex portfolio decreases the total connectedness of the mixed portfolio. Asymmetries in connectedness are relatively small. While negative shocks dominate forex volatility connectedness, positive shocks prevail when oil and forex markets are assessed jointly. Frequency connectedness is largely driven by uncertainty shocks and to a lesser extent by liquidity shocks, which impact long-term connectedness the most and lead to its dramatic increase during periods of distress.
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