اثرات نامتقارن شوکهای پولی
Abstract
JEL classification
۱٫ Introduction
۲٫ Methodology and data
۳٫ Empirical results
۴٫ Conclusions
Declaration of Competing Interest
Appendix.
References
Abstract
This study investigates the asymmetric effects of unanticipated monetary shocks on stock prices in India over the period 1994M4–۲۰۱۸M11. We find that the evolution of stock prices is state-dependent across different monetary policy processes. Unanticipated monetary shocks appear to have significantly asymmetrically lagged effects on stock prices, namely: (i) the positive effect of negative unanticipated shocks in bull markets; and (ii) the negative effect of positive unanticipated shocks in bear markets. Our findings imply that monetary policy-markers should attend to these situations for the future of money-supply policies to diminish the degree of uncertainty about the money supply in adjusting stock prices.
Introduction
The relationship between monetary policy and the real economy has been well developed in the specialized literature. Lucas (1972), Barro (1978), and Frydman and Rappoport (1987) indicated that if anticipated monetary policy is neutral, only unanticipated monetary shocks are probably to entirely impact real output. Interestingly, the literature argues that the impacts of unanticipated monetary shocks can be asymmetric on real economic activities. This is attributed to the following factors: (i) the business cycle’s different stages (Galí, ۲۰۱۵; Iacoviello, 2005); (ii) the contraction versus the expansion in the conduct of monetary policy (Jiang, 2018; Shiu-Sheng, 2007); and (iii) the different levels of monetary policy effect on real economic activities. In the same vein, regarding financial markets, several studies found that the relationship between the monetary policy process and stock market prices is also asymmetric (Ravn, 2014; Ülke and Berument, 2016). Most studies on the relationship between monetary policy and stock market employed such estimation techniques as event analysis (Val et al., 2018), VAR models (Fausch and Sigonius, 2018; Singh and Nadkarni, 2018), DSGE models (Ravn, 2014), and Markov Switching models (Ivrendi and Guloglu, 2012).