Date of Award

Spring 5-18-2012

Degree Type


Degree Name


Degree Program

Financial Economics


Economics and Finance

Major Professor

M. Kabir Hassan

Second Advisor

Arja Turunen-Red

Third Advisor

Peihwang Wei

Fourth Advisor

Sudha Krishnaswami

Fifth Advisor

Tarun Mukherjee


In Chapter 1, I hypothesize that there is a differential response by agents to changes in sovereign credit or default risk in both quiet (low default risk) and turbulent markets (high default risk). These market conditions create two different states of the market (world) or regimes. Investors and policy makers respond differently in the two regimes but the response in the turbulent market condition is amplified as policy makers attempt to smoothen the fluctuations and uncertainty while investors rebalance their portfolios in an attempt to hedge against downside risk of wealth loss. In the two regimes, the short run and long run dynamic relationships between any two cointegrated assets may change. To capture this phenomenon, this study tests for nonlinearities that may characterize the regimes, how cointegration relationships, short term dynamic interaction and price discovery (speed of adjustment to new information between two assets) may change in alternative regimes. To this end, I employ threshold cointegration, threshold vector error correction model (TVECM) asymmetrical return spillover modeling for sovereign credit default swaps (CDS), bonds and equity markets of seventeen emerging markets from four geographical regions. I find that there is non-linear cointegration and momentum in long-run adjustment process in 43/51 spreads analyzed. All countries analyzed have at least 2/6 possible regime specific asymmetric price discovery process. The study also finds evidence in support of asset substitution hypothesis and news-based hypothesis of financial contagions in sovereign CDS, bond and equity markets. The findings have important implications for asset allocation and portfolio rebalancing decisions by investors, policy intervention in financial markets, risk management and regime specific short and/or long term dynamic interactions among assets held in a portfolio as well as nonlinear speed of adjustment to new information.

In chapter 2, I hypothesize that financial intermediaries can be categorized into bank-based institutions (BBIs) and market-based institutions (MBIs). MBIs and BBIs are under different regulatory agencies. Traditionally, only BBIs, regulated by the Fed, are used as conduits of transmitting liquidity and monetary policy into real economy and financial markets yet MBIs also play important role in providing liquidity and stability in financial markets. I use two tools of monetary policy (Federal fund rate and monetary aggregate) under two monetary policy regimes to investigate the impact of monetary policy under each regime on the liquidity of MBIs and BBIs. I investigate whether MBIs be used as conduits of transmitting monetary policy and liquidity in the market and if they should, under what economic and financial conditions (Regimes) should they be used. Moreover, what monetary policy tool is more effective for MBIs relative to BBIs under different regimes? Using Threshold vector auto-regressions and regime specific impulse response functions, I find that liquidity of BBIs and MBIs respond differently to different monetary policy tools under different regimes. Moreover, monetary policies are uncertain and vary over time. The Fed cannot continue to ignore MBIs in formulating and implementing monetary policy. Moreover, monetary aggregate policy is more effective when used on MBIs during contractionary monetary policy intervention (economic downturn) while Federal fund rate is more effective when used on BBIs under expansionary monetary policy.


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