International Journal of Financial Management

Anandadeep Mandal ¹, Ruchi Sharma ¹

Affiliations
1 School of Management, KIIT University, Bhubaneshwar, IN

Abstract

In this paper we formulate an explicit time discretization model for modeling risk by establishing an initial value problem as a function of time. The model is proved stable and the scaled-stability regions can encapsulated the volatile macroeconomic condition pertaining to financial risk. The model is extended to multistage schemes where we test for convergence under higherorder difference equations. Further, for addressing advection problems we have used Runge-Kutta method to propose a multistep model and have shown its stability patterns against general and absolute stability conditions. The paper also provides second-order and forth-order algorithm for computational programming of the models in practice. We conclude by stating that explicit time discretization models are stable and adequate for changing business environment.

Keywords

Explicit Time Discretization, Runge-kutta Method, Algorithms, Computational Programming, Risk Modeling

Full Text

     

Anandadeep Mandal ¹

Affiliations
1 Faculty, Finance & Accounting, KIIT School of Management, Bhubaneswar, Orissa

Abstract

This paper studies hedging effectiveness in Indian stock index futures market. The main focus is on various procedures to estimate time-varying and static optimal hedge ratios. For the S&P CNX Nifty futures contract 5 different econometric models that are employed. The data set used is from 2001-2008. Traditional OLS regressions, modified OLS viz. LTS , error correction model (ECM), vector error correction model (VECM) and multivariate generalized autoregressive heteroscedastic (M-GARCH) models are used to estimate hedge ratios, not only for mirror index underlying the futures contract but also for mutual funds. It is the first exhaustive study of its kind on the Indian stock index futures market and reveals that mutual funds tend to be a good proxy for market portfolios. Simple OLS seems to provide the best hedging effectiveness in terms of risk reduction for the Indian futures market. However, the use of more complex models like VECM cannot be sublimed as they provide more or less same hedging effectiveness.

Keywords

Hedge Ratios, OLS, VECM, M-GARCH, Nifty Futures

Full Text

     

Anandadeep Mandal ¹

Affiliations
1 Faculty of Finance & Accounting, KIIT School of Management, KIIT University, Bhubaneswar – 751024, IN

Abstract

The paper examines the presence of herd behavior in the S&P CNX Nifty 50 index of the National Stock Exchange of India, which arises out of the informational asymmetries found in the emerging markets around the globe. A price-based model with logarithmic crosssectional deviation employing Kalman fi lter is used to measure the presence of herding. This study exposes the severe effects of herd behavior on the Nifty index. We have found highly signifi cant herding in the Nifty index on a market-wide level during the period of 1997-2008. We also state that this type of behavior is decidedly exhibited by the market participants of the Nifty index, during the bull runs in the market and correspondingly less exhibited during the bear runs. Our work also examines the various events that took place during our sample period (May 1997-December 2008) and relates it to the course of herding in the Nifty index.

Keywords

Herd Behavior, Informational Asymmetries, Logarithmic Cross-sectional Deviation, Kalman fi Lter

Full Text

     

Anandadeep Mandal ¹, Sailabala Debi ², Smruti R. Tripathy ³

Affiliations
1 Assistant Professor, Finance and Accounting, KIIT School of Management, Campus – 7, KIIT University, Bhubaneswar – 751024, Orissa, IN
2 Economics Area, KIIT School of Management, Campus – 7, KIIT University, Bhubaneswar – 751024, Orissa
3 Final Year Student, KIIT School of Management, Campus – 7, KIIT University, Bhubaneswar – 751024, Orissa

Abstract

This paper examines the stock market volatility and FIIs for the period (2004 - 2010). The volatility of NIFTY index influenced by the investment inflows of FIIs is modeled using a time series ARIMA approach. The empirical results calibrated through these models are analytic in several fronts. The paper further models and forecasts the index using the data set, where Foreign Institutional Investment (FII), Nominal Effective Exchange Rate (NEER) and Call money rate are considered as the exogenous variables. Finally, the empirical findings show that if the net FII flow is auto regressed with FII flow of various lag periods then it does not have significant influence on the monthly-volatility of the index. The paper also proposes the possible reasons supporting the empirical observations.

Keywords

Foreign Institutional Investor, Stock Market Volatility, Arima, Nominal Effective Exchange Rate, Call Money Rate

Full Text

     

Anandadeep Mandal ¹

Affiliations
1 Doctoral Scholar, Cranfield School of Management, Cranfield University, England, United Kingdom

Abstract

In this paper we propose iterative solvers for portfolio optimization in a two dimensional domain. To put the modeled equations into practice we provide Jacobi and Gauss-Seidal algorithms. In order to improve the efficiency of portfolio optimization iterative solvers we study the convergence rate and introduce successive over relaxation scheme to the developed algorithms. Further to overcome the domain bias of this relaxation scheme we propose a symmetric successive relaxation model. This is demonstrated through a Chebyshev acceleration technique. We conclude by stating that iterative solvers are more superior and consistent techniques for portfolio optimization.

Keywords

Iterative Solvers, Portfolio Optimization, Successive Over Relaxation, Chebyshev Accelaration, Jacobi And Gauss-seidel Algorithms

Full Text