News Arrival, Time-Varying Jump Intensity, and Realized Volatility: Conditional Testing Approach

Erdemlioglu, Deniz; Yang, Xiye

doi:10.1093/jjfinec/nbac015

Cited by 3 publications

(11 citation statements)

References 81 publications

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“… See Andersen et al (2020), who exploit jump intensity process to measure tail risk and assess its equity premium implications.14 See e.g.,Erdemlioglu and Yang (2022),Boswijk et al (2018) andDungey et al (2018) for implementation details, particularly on the selection of the functional form for C β i (kn) in (7).15 Our tail risk indicator RI is also quite similar to the estimator ofHill (1975). See also Aït-Sahalia and Jacod (2009) for a related discussion on the role of βi in (7).…”

mentioning

confidence: 84%

“…P) over an interval [0, T ]. The Grigelionis decomposition (see e.g., Erdemlioglu and Yang, 2022;Boswijk et al, 2018;Dungey et al, 2018) implies that X t has the following specification:…”

Section: Underlying Continuous-time Modelmentioning

confidence: 99%

“…where the function, f t (x), controls the jump size distribution and λ t denotes the jump intensity as in Erdemlioglu and Yang (2022) and Boswijk et al (2018). We focus on the tail component of this jump 10 AutoGL's TabularPredictor approach.…”

Section: Underlying Continuous-time Modelmentioning

confidence: 99%

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Mind Your Language: Market Responses to Central Bank Speeches

Neely¹,

Yang²,

McMahon³

et al. 2023

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Researchers have carefully studied post-meeting central bank communication and have found that it often moves markets, but they have paid less attention to the more frequent central bankers' speeches. We create a novel dataset of US Federal Reserve speeches and develop supervised multimodal natural language processing methods to identify how monetary policy news affect financial volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that news in central bankers' speeches can help explain volatility and tail risk in both equity and bond markets. Our results challenge the conventional view that central bank communication primarily resolves uncertainty and indicate that markets attend to speech signals more closely during abnormal GDP and inflation regimes. Our analysis also reveals that the views of Fed members (i.e., hawkish versus dovish) tend to play a marginal role in terms of the strength of the speech signals. Looking at the speeches by the Fed Chair, we find that the Chair signals produce a larger tail risk compared to non-Chair signals, and the estimated magnitude of the market responses depends on the position of the officials (i.e., the Fed Chair or other Fed member).

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mentioning

confidence: 84%

“…P) over an interval [0, T ]. The Grigelionis decomposition (see e.g., Erdemlioglu and Yang, 2022;Boswijk et al, 2018;Dungey et al, 2018) implies that X t has the following specification:…”

Section: Underlying Continuous-time Modelmentioning

confidence: 99%

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Mind Your Language: Market Responses to Central Bank Speeches

Neely¹,

Yang²,

McMahon³

et al. 2023

View full text Add to dashboard Cite

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“…Second, we follow Andersen et al (2020), Erdemlioglu and Yang (2022) and use time-varying jump intensities to capture extreme tails, not only the sizes of price jumps, and then measure systemic downside risk.…”

Section: Related Literaturementioning

confidence: 99%

“…Our econometric approach, which nests existing jump tests, builds on and extends the conditional testing approach recently developed by Erdemlioglu and Yang (2022) to a multivariate cross-sectional setting. 4 By conditioning on event times, we first estimate time-varying jump intensities that capture the tails of return distributions.…”

Section: Introductionmentioning

confidence: 99%

Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications

Neely¹,

Yang²,

Erdemlioglu³

2023

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We develop a new framework to measure market-wide (systemic) tail risk in the cross-section of high-frequency stock returns. We estimate the time-varying jump intensities of asset prices and introduce a testing approach that identifies multi-asset tail risk based on the release times of scheduled news announcements. Using high-frequency data on individual U.S. stocks and sector-specific ETF portfolios, we find that most of the FOMC announcements create systemic left tail risk, but there is no evidence that macro announcements do so. The magnitude of the tail risk induced by Fed news varies over the business cycle, peaks during the global financial crisis and remains high over different phases of unconventional monetary policy. We use our approach to construct a Fed-induced systemic tail risk (STR) indicator. STR helps explain the pre-FOMC announcement drift and significantly increases variance risk premia, particularly for the meetings without press conferences.

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Jump Clustering, Information Flows, and Stock Price Efficiency

Chen

2024

Journal of Financial Econometrics

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We study the clustering behavior of stock return jumps modeled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterized by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.

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News Arrival, Time-Varying Jump Intensity, and Realized Volatility: Conditional Testing Approach

Cited by 3 publications

References 81 publications

Mind Your Language: Market Responses to Central Bank Speeches

Mind Your Language: Market Responses to Central Bank Speeches

Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications

Jump Clustering, Information Flows, and Stock Price Efficiency

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