Sunirand, Pojanart
(2004)
Financial markets, monetary policy, and the real economy.
PhD thesis, London School of Economics and Political Science.
Abstract
This thesis studies the interactions between financial markets, monetary policy, and the real economy. It analyses the role of financial markets in business cycle fluctuations and explores issues concerning systemic financial stability. Chapter One develops a dynamic general equilibrium model in which firms and banks face financial frictions in obtaining external funds. The model exhibits an unconventional bank capital channel as monetary policy affects the economy partly via its effect on bank capital. We show that the dynamic interactions between bank capital, firm net worth and asset price amplify and propagate the effect of a monetary shock in the macroeconomy. Chapter Two empirically investigates the importance of financial markets in the monetary transmission. The analysis is based on the argument that the real money stock serves as a proxy for the relative yields of various non-money assets that matter for aggregate demand. Using Thailand data, we find that the two-asset assumption is biased and that this problem can be ameliorated by introducing an explicit role for money into standard macroeconomic models. Chapter Three develops a numerically-solvable version of our general model [Goodhart, Suni-rand, and Tsomocos (2003)] to analyse financial fragility. The model incorporates heterogeneous agents and therefore leads to different simulation results from those obtained when using standard representative agent models; the effect of a shock depends on the part of the economy on which it falls and can generally shift the distribution of income and welfare between agents. Chapter Four proposes a general equilibrium model incorporating three heterogeneous banks. This allows us to study not only the interactions between any two individual banks, but also their inter-relationship with the rest of the banking sector. The model is calibrated against real UK banking data and therefore can be implemented as a risk assessment tool for financial regulators and central banks.
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