Essays on jump risk in the Indian financial market

The dissertation consists of four independent but related studies on jump risk and the systemic risk of Indian banking stocks. Jumps are defined as abnormal stock price movements in terms of a local volatility. We provide evidence of the existence of jumps in the Indian market and banking sector and show that jump systematic risks (or jump betas) are priced differently from continuous systematic risks (or continuous betas). These jump occurrences can be explained partly by market liquidity conditions in addition to news arrivals. We finally show that banks with higher jump and continuous betas are also more active in propagating systemic risk. In the first study, we use high-frequency stock returns of 41 Indian banks and find that the beta on jump movements of these stocks substantially exceeds that on the continuous components, and that the majority of the information content for returns lies with the jump beta. The predictability of stock returns from the traditional CAPM beta mainly comes from the jump beta. In this study, we contribute to the debate on strategies to decrease systematic risk, showing that increased bank capital and reduced leverage reduce both jump and continuous beta ‚Äö- with slightly stronger effects for capital on continuous beta and stronger effects for leverage on jump beta. However, changes in these firm characteristics need to be large to create an economically meaningful change in beta. The second study examines the jump risks for banking sector represented by 41 banking stocks and non-banking financial sector comprises 55 financial Institutions (FI) all listed on the National Stock Exchange of India (NSE). Using intra-day high frequency data we apply several widely-used jump tests to the price series of the financial institutions of India. We observe wide variation in jump detection rates across different methods. Our test results show that the banking industry is associated with a higher degree of jump risk compared with the market whereas the result is opposite for the FI industry. Results from a probit regression indicate that jumps in the market increase the likelihood of jump occurrence in the financial sectors in the next period. The intra-day jump test results of Indian financial stocks reveal the existence of intra-day and weekly seasonality in jump patterns in contrast with the general description of jump occurrences in early literature as a Poisson distribution. The third study seeks to understand the relationship of liquidity variables with jump movements based on emerging market stocks. We use 15-minute return data from ten of the largest Indian banking stocks and implement an event study method to examine the behaviour of liquidity variables around the jump times. We find notable variations in the liquidity measures around the jump occurrences in the stocks. Liquidity characterized by market spread, trading quantity and immediacy do improve around jumps. The results indicate that the demand of immediacy of traders may cause jumps in stock returns. The Mann-Whitney test results also confirm the significant changes in the liquidity variable during the jump intervals from the non-jump intervals. Our probit and logit estimations show that the liquidity variables have more explanatory power in determining the probability of negative jump occurrences than that of positive ones. We do not observe substantial changes in the results by dividing our sample into pre-crisis, crisis and post crisis periods, indicating that the effects of liquidity variables are general on jump occurrences. Finally, several liquidity variables are shown to contribute to price discovery although the post jump price discovery process does not seem strongly related with these variables. In the fourth paper we measure the Indian banks' systemic risk which can be defined as the likelihood of propagating financial adversity such as illiquidity. We use stock returns of 40 listed commercial banks of India for the period of 2011 to 2015. First we apply a multivariate Granger causality test to establish statistically significant connections between each pair of banks. To measure the strength of each of the links we measure the weight of the link by applying a variance decomposition method. Finally, we derive the network of banks where only the statistically significant links with their respective weights are retained and from this network matrix we identify the most systemically active banks in India. An analysis on the daily rolling window of the network measures shows that the overall network strength increases during our sample period. Our analysis shows that market liquidity and volatility are related to the network connectivities. Decrease in liquidity and increase in volatility heighten the connectivity of networks. In addition, the systematically risky banks in terms of both jump and continuous beta are found also to be the more active banks in strengthening network connectivities.