nep-rmg New Economics Papers
on Risk Management
Issue of 2010‒07‒17
seven papers chosen by
Stan Miles
Thompson Rivers University

  1. Predictive ability of Value-at-Risk methods: evidence from the Karachi Stock Exchange-100 Index By Iqbal, Javed; Azher, Sara; Ijza, Ayesha
  2. Systemic risk in the financial sector; A review and synthesis By Michiel Bijlsma; Jeroen Klomp; Sijmen Duineveld
  3. International Transmission of Bank and Corporate Distress By Hiroko Oura; Papa M'B. P. N'Diaye; Qianying Chen; Dale F. Gray; Natalia T. Tamirisa
  4. Downside risk optimization in securitized real estate markets By Kroencke, Tim-Alexander; Schindler, Felix
  5. QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles By Nyberg, Henri
  6. Can You Map Global Financial Stability? By Ken Miyajima; Rebecca McCaughrin; Jaume Puig; Peter Dattels
  7. Stock volatility in the periods of booms and stagnations By Kaizoji, Taisei

  1. By: Iqbal, Javed; Azher, Sara; Ijza, Ayesha
    Abstract: Value-at-risk (VaR) is a useful risk measure broadly used by financial institutions all over the world. VaR is popular among researchers, practitioners and regulators of financial institutions. VaR has been extensively used for to measure systematic risk exposure in developed markets like of the US, Europe and Asia. In this paper we analyze the accuracy of VaR measure for Pakistan’s emerging stock market using daily data from the Karachi Stock Exchange-100 index January 1992 to June 2008. We computed VaR by employing data on annual basis as well as for the whole 17 year period. Overall we found that VaR measures are more accurate when KSE index return volatility is estimated by GARCH (1,1) model especially at 95% confidence level. In this case the actual loss of KSE-100 index exceeds VaR in only two years 1998 and 2006. At 99% confidence level no method generally gives accurate VaR estimates. In this case ‘equally weighted moving average’, ‘exponentially weighted moving average’ and ‘GARCH’ based methods yield accurate VaR estimates in nearly half of the number of years. On average for the whole period 95% VaR is estimated to be about 2.5% of the value of KSE-100 index. That is on average in one out of 20 days KSE-100 index loses at least 2.5% of its value. We also investigate the asset pricing implication of downside risk measured by VaR and expected returns for docile portfolios sorted according to VaR of each stock. We found that portfolios with higher VaR have higher average returns. Therefore VaR as a measure of downside risk is associated with higher returns.
    Keywords: Downside risk; Emerging Markets; Value-at-Risk
    JEL: C52 G10
    Date: 2010–01–28
  2. By: Michiel Bijlsma; Jeroen Klomp; Sijmen Duineveld
    Abstract: The financial crisis has put systemic risk firmly on the policy agenda. In such a crisis, an initial shock gets amplified while it propagates to other financial intermediaries, ultimately disrupting the financial sector. We review the literature on such amplification mechanisms which create externalities from risk taking. We distinguish between two classes of mechanisms: contagion within the financial sector and pro-cyclical connection between the financial sector and the real economy. Regulation can diminish systemic risk by reducing these externalities. However, regulation of systemic risk faces several problems. First, systemic risk and its costs are difficult to quantify. Second, banks have strong incentives to evade regulation meant to reduce systemic risk. Third, regulators are prone to forbearance. Finally, the inability of governments to commit not to bail out systemic institutions creates moral hazard and reduces the market’s incentive to price systemic risk. Strengthening market discipline can play an important role in addressing these problems, because it reduces the scope for regulatory forbearance, does not rely on complex information requirements, and is difficult to manipulate.
    Keywords: Financial markets; Contagion; Systemic risk
    JEL: G28
    Date: 2010–07
  3. By: Hiroko Oura; Papa M'B. P. N'Diaye; Qianying Chen; Dale F. Gray; Natalia T. Tamirisa
    Abstract: The paper evaluates how increases in banks’ and nonfinancial corporates’ default risk are transmitted in the global economy, using in a vector autoregression model for 30 advanced and emerging economies for the period from January 1996 to December 2008. The results point to two-way causality between bank and corporate distress and to significant global macroeconomic and financial spillovers from either type of distress when it originates in a systemic economy. Corporate distress in advanced economies has a larger impact on economic growth in emerging economies than bank distress in advanced economies has. In contrast, activity in advanced economies is more vulnerable to bank distress than to corporate distress.
    Keywords: Banking sector , Corporate sector , Credit risk , Developed countries , Economic models , Emerging markets , Financial risk , Spillovers ,
    Date: 2010–05–20
  4. By: Kroencke, Tim-Alexander; Schindler, Felix
    Abstract: Optimization of international securitized real estate portfolios has been a key topic for several decades. However, most previous analysis has focused on regional diversification by applying the traditional mean-variance (MV) framework suggested by Markowitz (1952) even if the limitations of this approach are well-known. Thus, we focus on a more suitable and appealing downside risk (DR) framework suggested by Estrada (2008), which applies a similar optimization algorithm as the MV framework. The analysis covers the eight largest securitized real estate markets from January 1990 to December 2009 and thus captures a more global perspective. The main findings are as follows: first, the return distributions are non-normally distributed and negatively skewed. Second, optimal portfolio weights differ substantially between the MV and DR approach. Third, portfolio weights are shifted from the U.S. and Australian market to the Dutch and the French market when applying the DR framework instead of the MV framework. Fourth, the dominance of the DR framework is well-documented by comparing out-of-sample performance. The empirical results are remarkable and emphasize the practical merit of the presented DR framework for investors and portfolio managers. --
    Keywords: Downside Risk Analysis,International Real Estate Markets,Portfolio Management,Portfolio Optimization,Out-of-Sample Analysis
    JEL: C61 G11 G15
    Date: 2010
  5. By: Nyberg, Henri
    Abstract: In the empirical finance literature findings on the risk return tradeoff in excess stock market returns are ambiguous. In this study, we develop a new QR-GARCH-M model combining a probit model for a binary business cycle indicator and a regime switching GARCH-in-mean model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional ICAPM, there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.
    Keywords: Regime switching GARCH model; GARCH-in-mean model; probit model; stock return; risk-return tradeoff; business cycle
    JEL: C32 E32 G12 E44
    Date: 2010–04
  6. By: Ken Miyajima; Rebecca McCaughrin; Jaume Puig; Peter Dattels
    Abstract: The Global Financial Stability Map was developed as a tool to interpret the risks and conditions that impact financial stability in a graphical manner. It complements other existing tools for assessing financial stability, and seeks to overcome some of the drawbacks of earlier approaches. This paper provides the motivation for the tool, a detailed discussion of its construction, including the choice of risk factors and conditions, a description of the underlying indicators, and a discussion on how the final assessment is determined. When applied to past events of financial instability, the Global Financial Stability Map performs reasonably well in signaling risks to stability, as well as in characterizing the depth of crisis episodes.
    Date: 2010–06–15
  7. By: Kaizoji, Taisei
    Abstract: The aim of this paper is to compare statistical properties of stock price indices in periods of booms with those in periods of stagnations. We use the daily data of the four stock price indices in the major stock markets in the world: (i) the Nikkei 225 index (Nikkei 225) from January 4, 1975 to August 18, 2004, of (ii) the Dow Jones Industrial Average (DJIA) from January 2, 1946 to August 18, 2004, of (iii) Standard and Poor’s 500 index (SP500) from November 22, 1982 to August 18, 2004, and of (iii) the Financial Times Stock Exchange 100 index (FT 100) from April 2, 1984 to August 18, 2004. We divide the time series of each of these indices in the two periods: booms and stagnations, and investigate the statistical properties of absolute log return, which is a typical measure of volatility, for each period. We find that (i) the tail of the distribution of the absolute log-returns is approximated by a power-law function with the exponent close to 3 in the periods of booms while the distribution is described by an exponential function with the scale parameter close to unity in the periods of stagnations.
    Keywords: volatility; boom; and stagnation; stock price indices
    JEL: C16 D30 G19
    Date: 2010–06–25

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