Quantile relationships between standard, diffusion and jump betas across Japanese banks

Abstract

Using high frequency financial data and associated risk decomposition and quantile regression techniques we characterise some stylised facts and relationship(s) between standard betas, diffusion betas and jump betas of individual stocks and portfolios in Japanese market. We then investigate whether the beta in the conventional CAPM is the weighted average of the jump beta and diffusion beta in the jump-diffusion model and how these different betas behave across different banks. Our empirical findings indicate that jump betas are cross-sectionally more dispersed than diffusion and standard betas. We find that the relationship(s) between the three betas are non-linear. We also find that standard betas are influenced more by diffusion betas than the jump betas, although the actual magnitude of the weights differ significantly across the quantile. This relationship holds for both individual stocks and portfolios. Empirical studies have shown that betas vary systematically across large and small firm equities. For large equity portfolios, the jump beta-diffusion beta ratios are lower that the jump betadiffusion beta ratios of the small equity portfolios. Empirically, we further find that the standard CAPM beta is composed of two-components, i.e. it is the weighted average of the diffusion component and the jump component.

Higher‐order moments and asset pricing in the Australian stock market

“…Consequently, some researchers have shown that the continuous and jump betas are priced differently (e.g. , see Bollerslev et al, 2016; Chowdhury et al, 2018; Chowdhury & Jeyasreedharan, 2019; Dungey & Yao, 2013; Todorov & Bollerslev, 2010).…”

“…The predictive power of the jump beta has been extensively documented in the extant high-frequency literature. Consequently, some researchers have shown that the continuous and jump betas are priced differently (e.g., see Bollerslev et al, 2016;Chowdhury et al, 2018;Chowdhury & Jeyasreedharan, 2019;Dungey & Yao, 2013;Todorov & Bollerslev, 2010). Markowitz (1952) recognised that investors should consider higher-order moments, such as skewness and kurtosis, in order to accurately assess the risk of an asset.…”

"Black-Scholes-Artificial Neural Network": A novel option pricing model

“…The jump-diffusion model, introduced in 1976 by Robert Merton, is a model for stock price behavior that incorporates small day-to-day "diffusive" movements together with larger, randomly occurring "jumps" (Chowdhury & Jeyasreedharan, 2019). The inclusion of jumps allows for more realistic "crash" scenarios, rendering the standard dynamic replication hedging approach of the Black-Scholes model ineffective.…”