The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities E...The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.展开更多
Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a pr...Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.展开更多
The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market.This volatility leads to greater risk to ...The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market.This volatility leads to greater risk to P2P investors and has become the focus of the regulatory authorities in China.Based on the background data of the P2P platform,Honglingchuangtou,we use the factor analysis method to construct a platform volatility(PV)index and we construct an HAR model to study the heterogeneous traders and leverage effect in the Chinese P2P market.The empirical results show that there are both short-term and long-term heterogeneous traders in the Chinese P2P market and that long-term traders have the greatest impact on market volatility.Similar to traditional financial markets,the volatility of the P2P market also shows a leverage effect,which means that the negative volatility of trader actions should have a negative impact on market fluctuations.With regard to the leverage effect,the LHAR-PV model is superior because of a higher goodness of fit and a lower prediction error.展开更多
文摘The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.
文摘Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.
基金This work is partially supported by the grants from the Key Programs of the National Natural Science Foundation of China(NSFC No.71631005)the National Natural Science Foundation of China(NSFC No.71471161)the Key Programs of the National Social Science Foundation of China(No.17ZDA074).
文摘The new financial industry represented by peer-to-peer lending has gradually become a new source of volatility due to the increasing complexity of the Chinese financial market.This volatility leads to greater risk to P2P investors and has become the focus of the regulatory authorities in China.Based on the background data of the P2P platform,Honglingchuangtou,we use the factor analysis method to construct a platform volatility(PV)index and we construct an HAR model to study the heterogeneous traders and leverage effect in the Chinese P2P market.The empirical results show that there are both short-term and long-term heterogeneous traders in the Chinese P2P market and that long-term traders have the greatest impact on market volatility.Similar to traditional financial markets,the volatility of the P2P market also shows a leverage effect,which means that the negative volatility of trader actions should have a negative impact on market fluctuations.With regard to the leverage effect,the LHAR-PV model is superior because of a higher goodness of fit and a lower prediction error.
