TY - JOUR
T1 - Portfolio management under sudden changes in volatility and heterogeneous investment horizons
AU - Fernandez, Viviana
AU - Lucey, Brian M.
N1 - Funding Information:
This manuscript was written at the Institute for International Integration Studies (IIIS), Trinity College, Dublin, while the corresponding author held a Visiting Research Fellowship during January–March 2006. Financial support from FONDECYT Grant No. 1050486 and from the IIIS is greatly acknowledged.
PY - 2007/3/1
Y1 - 2007/3/1
N2 - We analyze the implications for portfolio management of accounting for conditional heteroskedasticity and sudden changes in volatility, based on a sample of weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005. To that end, we first proceed to utilize the ICSS algorithm to detect long-term volatility shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor's time horizon. We repeat the same procedure for artificial data generated from semi-parametric estimates of the distribution functions of returns, which account for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead to an overestimation of financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock and bond indices contribute to portfolio diversification.
AB - We analyze the implications for portfolio management of accounting for conditional heteroskedasticity and sudden changes in volatility, based on a sample of weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005. To that end, we first proceed to utilize the ICSS algorithm to detect long-term volatility shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor's time horizon. We repeat the same procedure for artificial data generated from semi-parametric estimates of the distribution functions of returns, which account for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead to an overestimation of financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock and bond indices contribute to portfolio diversification.
KW - Heterogeneous investors
KW - Structural volatility shifts
KW - Value at risk
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=33845443396&partnerID=8YFLogxK
U2 - 10.1016/j.physa.2006.10.004
DO - 10.1016/j.physa.2006.10.004
M3 - Article
AN - SCOPUS:33845443396
SN - 0378-4371
VL - 375
SP - 612
EP - 624
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
IS - 2
ER -