The Effects of Interest Rate Movements on Assets’ Conditional Second Moments

Palandri, Alessandro

doi:10.2139/ssrn.1447342

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…This model attracted the attention of many researchers. The model was examined in several studies (Kumar et al, 2008) New York Stock Exchange; (Palandri, 2009) Copenhagen Stock Exchange; (Lee, Tsai, and Lee, 2009), (Adrian and Franzoni, 2009) and (Guermat and Freeman, 2010) New York Stock Exchange. Another important development took place in CAPM when researchers were paying attention to the impact of financial, operational, combined and economic leverages in efficiency of portfolio management.…”

Section: Introductionmentioning

confidence: 99%

A Study on Developing of Asset Pricing Models

Raei¹,

Ahmadinia²,

Hasbaei³

2011

IBR

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This study introduces the development and modifications of the widely used standard capital asset pricing model (CAPM). Many modifications are applied to the model’s challenging financial variables such as: financial risk factors, liquidity risks, downside risks, risk of non expected events, and economic and operational risk factors. Efficiency of the model is increased when applying various challenging financial variables. As a result of the gradual CAPM developments, various new models will present better interpretations of market conditions in economic units and portfolio structure. Furthermore, this study will show the importance of applying the new models advantages and disadvantages for financial managers, financial analysts and investors.

po? c.?x ?w vant factors identified in this study showed that lenders took particular interest in risk when dealing with SMEs. This is not out of place, as every business seeks to make profit and thus they need to be sure of recouping their monies when they lend them out to small businesses. It is thus very necessary for SME borrowers to develop an understanding of the decision criteria used by financial institutions in order to increase the probability of getting their loan request approved by fulfilling the required criteria adequately.

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Section: Introductionmentioning

confidence: 99%

A Study on Developing of Asset Pricing Models

Raei¹,

Ahmadinia²,

Hasbaei³

2011

IBR

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“…A number of studies have examined the predictive power of several economic variables for equity returns and second moment of distribution (e.g. Harvey and Whaley, 1992 for the S&P 100 market; Schwert, 1989, Glosten, Jagannathan and Runkle, 1993, Perez-Quiros and Timmermann, 2001 for the volatility; Bakshi and Madan, 2006, Konstantinidi and Skiadopoulos, 2013and Feunou et al, 2014 for the volatility risk premium; Sheppard, 2008 andPalandri, 2009 for correlations). Based on these findings, the ability of factors related to general macroeconomic as well as stock market specific conditions to explain the time variation and predict the correlation risk premium is explored.…”

Section: Economic Determinants and Time Variation Of Crpmentioning

confidence: 99%

“…A number of studies have examined the role of economic variables in determining asset return correlations (see Erb et al, 1994, Moskowitz, 2003, Sheppard, 2008, Palandri, 2009, amongst others). Based on their findings, financial and macroeconomic variables, such as the short-term interest rates or the slope of the term structure, are expected to determine the systematic risk of equity portfolios and, consequently affect the time variation of equity correlations.…”

Section: Economic Determinantsmentioning

confidence: 99%

Correlation modelling with application to risk management

Markopoulou¹,

Μαρκοπούλου²

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Accurate estimation and prediction of correlation is of paramount importance in asset allocation, risk management and hedging applications, particularly in light of recent studies that provide evidence of increased correlation during periods of high volatility, leading to diminishing diversification benefits in states of nature that are most needed. The time-variability of the correlation process has fuelled extensive literature on dynamic correlation modelling. In an attempt to depart from correlation estimation based on projections from historical data, two alternative measures of correlation, namely the implied and the realized correlation, have been proposed in the recent literature. Remarkably, in contrast to volatility estimates, existing studies on the informational efficiency and forecasting performance of respective correlation measures are rather limited.This thesis focuses on exploring the dynamics that govern the evolution of correlation risk premium and its components, namely implied and realized correlation, and assessing the impact of predictability to portfolio allocation, hedging and trading decisions. First, the time-variation and certain distribution characteristics of the correlation risk premium, defined as the difference of realized and implied correlation, are examined. The information content of market –specific and macroeconomic variables, which have been previously reported as proxies of the business cycles, in predicting future premium is also evaluated. Secondly, a model-free measure of implied correlation is proposed and the question of predictable dynamics in the evolution of the series is investigated both in statistical and economic terms. A trading strategy designed to exploit daily changes of the series sets the foundation for addressing the efficient market hypothesis. Finally, based on the distributional properties of realized volatility, correlation and hedge ratio, an alternative forecasting methodology is applied to predictthe realized hedge ratio and to explore the additive value of intraday data in a dynamichedging context while the hedging performance is compared in terms of portfoliooptimization and risk management.The thesis has reached a number of conclusions. First, correlation and correlationrisk premium vary substantially over time and increase sharply during turbulent periods,while culminated during the Asian and Russian financial crisis in 1997-1998 and thesubprime mortgage crisis of 2007-2009. The previously documented correlation riskpremium is no longer significant during the recent 2007 – 2009 crisis, suggesting thedisappearance of arbitrage opportunities. Secondly, the predictability of model-freeimplied correlation series suggested by statistical measures cannot be exploited in termsof economic gains, suggesting that the S&P 100 options market is efficient. Finally,forecasting the dynamics of the realized hedge ratio directly reveals predictable patternsin the evolution of the hedge ratio, resulting in improved hedging performance, in termsof both economics gains and risk measures.

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