Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component

In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the realized volatility. We also tried to investigate whether dividing volatility into simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The results provide the evidence that inclusion of realized bipower variance in the HAR models helps in predicting future volatility.Peer Reviewe

The properties of realized volatility and realized correlation: evidence from the Indian stock market

This paper investigates the properties of realized volatility and correlation series in the Indian stock market by employing daily data converting to monthly frequency of five different stock indices from January 2, 2006 to November 30, 2014. Using non-parametric estimation technique the properties examined include normality, long-memory, asymmetries, jumps, and heterogeneity. The realized volatility is a useful technique which provides a relatively accurate measure of volatility based on the actual variance which is beneficial for asset management in particular for non-speculative funds. The results show that realized volatility and correlation series are not normally distributed, with some evidence of persistence. Asymmetries are also evident in both volatilities and correlations. Both jumps and heterogeneity properties are significant; whereas, the former is more significant than the latter. The findings show that properties of volatilities and correlations in Indian stock market have similarities as that show in the stock markets in developed countries such as the stock market in the United States which is more prevalent for speculative business traders

Volatility Forecasting in Emerging Markets

This thesis examines the forecasting accuracy of implied volatility and GARCH(1,1) model volatility in the context of emerging equity markets. As a measure of risk volatility is a key factor in risk management and investing. Financial markets have become more global and the importance of volatility forecasting in emerging markets has increased. Emerging equity markets have more different risks than developed stock markets. As risk affects the potential return it is important to test and study how volatility models are able to forecast future volatility in emerging markets. The purpose of this thesis is to study the forecasting abilities and limitations of option implied volatility and GARCH(1,1) in the riskier emerging market environment. The majority of previous studies on volatility forecasting are focused on developed markets. Previous results suggest that in developed equity markets implied volatility provides an accurate short-term future volatility forecast whereas GARCH models offer a better long-term volatility forecast. The previous results in emerging market context have been in rather inconclusive. However, there is more evidence of GARCH(1,1) volatility being the most accurate future volatility forecaster. The main motivation behind this thesis is to examine which models is best suited for volatility forecasting in emerging equity markets. The forecasting accuracy of option implied volatility and GARCH(1,1) volatility is tested with an OLS regression model. The data consist of MSCI Emerging Market Price index data and corresponding option data from 1.1.2015 to 31.12.2019. In this thesis the daily closing prices of the index and option are used to compute daily and monthly implied volatility and GARCH(1,1) model volatility forecasts. Loss functions are applied to test the fit of the models. The results suggest that both models contain information about one-day future volatility as the explanatory power of both models is statistically significant for daily and monthly forecasts. The GARCH(1,1) volatility is a more accurate future volatility estimate than implied volatility for both daily and monthly volatilities. The monthly volatility forecast is more accurate for both models than the daily forecast. The results indicate that in both daily and monthly values GARCH(1,1) volatility is a more accurate estimate for future volatility than implied volatility. The GARCH(1,1) monthly volatility offers the best fit for future volatility with the highest predictive power and lowest error measures, suggesting that it is the most appropriate fit for future volatility forecasting in emerging equity markets

Essays on Indian Futures Markets

Exchange derivatives trading for national level commodity futures and equity index futures in India started in 2003 and 2001 respectively. Despite of the institutional ecosystem being put in place for nearly two decades, the participation of farmers and Farmer Producer Organizations' (FPOs) in agricultural futures trade is extremely low in India; and unlike most global Exchanges, the decision for contract sizes, margins and open position limits on the equity or equity index derivatives still remains a part of the regulator's mandate. This doctoral thesis by identifying two regulatory reforms from the Indian market concerning outright suspension of wheat futures contracts and increase in the market lot size of the index futures contracts, focuses on three essays relating to relative market efficiency, price discovery performance and market quality measures. The first essay examines the impact of wheat trading suspension on the degree of market efficiency in the pre-ban and post-ban phases. The second essay using data from the analogous wheat contract analyses if the trading ban have consequences on the market leadership and information flow between the spot and futures prices, under different trading periods. Lastly, the third essay examines whether the increase in the minimum lot sizes of index futures contracts can affect the trading activity variables, market liquidity and price volatility. Analysis from the first essay show that interventions like abrupt trading bans had negative effects on both the long-run market efficiency and short-run efficiency measure. Further results from the second essay reveal that trading suspensions have negative consequences on the short-run price discovery dynamics. Therefore from a viewpoint of attracting more hedgers (farmers' or FPOs) into the market for increasing liquidity and depth, regulators must provide stable policy environment for future trade to flourish. Finally, findings from the third essay suggest that increase in the contract size had positive impact only for the open interest, but the trading volume and liquidity variables were negatively affected. Therefore, the Securities market regulator's interventions in the Exchange's commercial decisions may discourage interest in the equity futures products at large in the long-term

Functional Asset Returns

Discovering the price of a financial asset is a dynamic and complex process. Based on available literature and empirical evidence there is not singular approach of achieving such a task easily. Despite cur- rent advances in technology and in access to data, a general argument in favor of approaching this problem is centered on the information available at each moment of time for an individual financial asset. Accordingly, it seems coherent to use density functions as a reference for studying relevant aspects of asset prices and subsequently equity returns. Forecasts of density functions is an active approach in deci- sion theory and economics. The direction of my dissertation is related to the employment of density forecasting apply to asset prices. Density forecasting may also may also of the interest for the manage- ment research areas since it provides more information than predic- tions produced considering only point and interval forecasts as these last frameworks yield limited sets of information. Finding the correct true price density of a financial asset is as crucial as the characterization of it. This has implications for investors and managers in terms of both, risk management and value creation. Despite, the acute of the underlying assumption made regarding the properties of the statistical distribution of the future asset fluctuation over time, finding the correct (true) price of a financial asset is as crucial as it is the characterization of it. Regarding the literature that addresses alternative methods to fore- casts asset prices there are some papers that consider that one direct solution for modelling purposes will to assume that the time evolution of the asset price can be described by a random event over time. In this case, the method of choice to produce forecasts will be centered on the idea that the expected asset price will be a discrete process. An alternative discussion may be, to consider that the process that describes the path of the asset price is related to continuous fluctuations in space in which a diffusion process will have a central role. In general, my approach will be is centered on some equities traded on the S&P500. My approach is to forecast the density function of these values using functional time series relying on is principal component analysis (PCA)

The impact of intraday periodicity and news announcements on high-frequency stock volatility

High-frequency intraday financial data are commonly used in stock market volatility estimation and forecasting because they produce accurate results. However, little work to date has focused on the stylised facts of high-frequency returns, such as their tail properties, autocorrelations and leverage effects. One of the most discussed features of high-frequency returns is intraday periodicity, yet it is not well known how this feature operates in returns from data with different sampling schemes and frequencies. In addition, macroeconomic news announcements have been shown to have a large impact on first-moment and second-moment responses in financial markets. However, few existing models consider the effect of news on volatility estimation and forecasting, and those that do tend to treat it as a dummy variable, limiting its analytical power.  This thesis addresses these issues by reporting a study of the stylised facts of returns from S&P 500 stocks and the SPY index, and standardised returns from the latter, using various volatility measures in different financial regimes (i.e. before, during and after the 2008 financial crisis). It presents a comparison of the intraday patterns, jump frequencies, jump components and volatility forecasting of stock returns from calendar-time and business-time sampling schemes, as well as how these features are affected by intraday periodicity. It assesses the direct impact of macroeconomic news announcements on volatility estimation and forecasting for stock returns by incorporating significant news announcements as an index to identify the jumps caused by news in heterogeneous autoregressive (HAR) class models.  The results suggest that absolute intraday returns for high-frequency data exhibit autocorrelations and that aggregated returns display heavy tails. Standardising the returns of the SPY index using eleven different volatility measures produces distributions that are closer to a normal distribution. We find that various volatility measures are significantly correlated with trading volume, and hence that HAR-class models that include trading volume yield better volatility forecasting results than existing models. However, this effect may be limited to data from the relatively non-volatile pre-crisis and post-crisis periods. High-frequency returns based on business-time sampling have smaller jump frequencies, jump components and intraday periodicity patterns, than calendar-time data, which may be useful for volatility analysis. Intraday periodicity has a notable impact on jumps for both sampling schemes, however, and adjusting for intraday periodicity produces fewer jumps for all returns and smaller jump components for the majority. We also find that the forecasting results for less volatile data, such as healthcare stocks and data from the post-crisis period, improved after filtering for intraday periodicity. Finally, macroeconomic news announcements can affect jump components, and considering news outlets in HAR models can improve the forecasting results. The thesis thus contributes to our understanding of the factors affecting stock market volatility by providing evidence in support of including trading volume, efficient intraday periodicity estimators and news surprise in volatility estimation and forecasting models