Heterogeneous investment horizons, risk regimes, and realized jumps

Abstract

This paper introduces a new empirical framework to identify the regimes of jump‐type tail risk over multiple trading horizons. Our approach combines the hidden Markov regime‐switching model with realized jumps, which allows us to examine the tail risk exposure of investors at different investment scales. Applying our method to data on bonds, stocks, and currencies, we find evidence that market risk linked to jumps exhibits time‐varying regime shifts and horizon‐dependence. We show that high‐frequency trading does not contribute to market instability, as measured by jump risk. The European bond market appears to be more vulnerable to downside (left‐tail) jump risk, relative to the U.S. Treasury bond market. Market jump fear embedded in the VIX exhibits vertical clustering where both risk regimes at low frequencies remain unchanged at higher frequencies. These results suggest that the premium for jump risk not only depends on stress periods, but also on the frequency at which investors trade financial assets.

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

“…For investors with different investment horizons, the effects of jumps with multi-time scales may be not the same due to the heterogenous investment horizon ( Dai et al, 2020 ; Wang et al, 2020b ; Dai et al, 2021b ; Miao et al, 2022 ). For investors with short investment horizons, the short-term effects of stock price jumps will bring the most intuitive and significant losses ( Erdemlioglu and Gradojevic, 2021 ). Conversely, for investors with long investment horizons, long-term effects may change the fundamentals of the industries and impact the adjustments to their investment strategies ( Yang et al, 2016 ; Jorion, 2000 ; Bakshi et al, 2000 ).…”

Financial investment risk analysis and countermeasures research based on CVaR-GARCH model

“…Literature [20] designed a robust irreversible investment rule applicable to a series of neighboring models and found that investment decisions are driven by fuzzy aversion as long as they are driven by the magnitude of the value jumps, not the frequency. In literature [21], a new empirical framework is introduced to identify jump tail risk mechanisms across multiple trading horizons. Analytical tests have demonstrated that the premium for jump risk is influenced by market risk and the frequency of investments in financial assets by investors.…”

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

“…The failure of several financial institutions may lead to a severe economic crisis [ 8 , 9 , 10 ]. One of the typical examples is the global financial crisis triggered by the collapse of Lehman Brothers in 2008 [ 11 , 12 , 13 ]. Therefore, how to accurately evaluate the systemic importance of financial institutions (SIFIs) so as to provide early warning and deal with the crisis effectively has become an emergent work [ 14 , 15 , 16 ].…”