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Short-term
Interest Rates and Macroeconomic Variables: An OLS Model
-- M Thenmozhi and Radha S
The
liberalization of interest rates has triggered the interest
of researchers in India to examine if interest rates are market
determined and develop robust models to account for the relationship
of macroeconomic variables with interest rates. This paper
attempts to examine the relationship between macroeconomic
variables and short-term interest rates using Hierarchical
RegressionOrdinary least squares method. The results show
that yield spread, monetary policy change and forward premia
play an important role in the determination of domestic interest
rates.
©
2006 IUP . All Rights Reserved.
Market
Efficiency and Volatility in Indian FX Market
--
Golaka C Nath
Foreign
exchange market in India has gone through many structural
reforms since last decade. The study tests the market efficiency
in forex market using data for the period, March 1993 to May
2004. The weak form of efficiency cannot be rejected. The
mean reversion theory can be well accepted. The Augmented
Dickey-Fuller (ADF) test testing for stationarity also supports
the weak form of efficiency of the market. The `day effect'
was not found in the study though all the "days' mean"
returns were significantly non-zero. It was found that AR(2)
and AR(3) models tracks the market volatility better in comparison
to other models.
©
2006 IUP . All Rights Reserved.
CAPM
Assisted Fuzzy Binomial Lattice Method for Option Pricing
-- S S Appadoo, R K Thulasiram,
and C R Bector
A
rapid development of mathematical models and methods addressing
uncertainty are reported in the literature in the recent past.
These theories either extend and complement probability theory
by introducing more general structures or provide an alternative
framework. These works enable addressing of subjective risk
assessment, vague data information and sensitivity analysis
in a more flexible way. Recently, there has been growing interest
in using fuzzy supported finance modelling. The systematic
risk Beta of an asset is important in a variety of contexts,
ranging from asset pricing theory, to hedging using index
derivatives. The stability of Beta has been a matter of intense
debate among researchers for the last three decades. In the
current paper, fuzzy algebra is used to price financial options.
Due to fluctuation of the financial market, some parameters
in the the classical Cox-Ross-Rubinstein (CRR) binomial risk
neutral option pricing model may not always be evaluated precisely.
The authors propose to consider a crisp risk free rate assisted
by CAPM return in the fuzzy option pricing model. The model
is geared towards a more natural and intuitive way to deal
with fuzziness, uncertainty and arbitrariness. The classical
option pricing of CRR becomes a special case of the proposed
model and some other special cases are also highlighted. The
superiority and validity of the proposed fuzzy supported option
pricing model is illustrated through a numerical example.
©
2006 IUP . All Rights Reserved.
Intertemporal
Causality between S&P 500 Spot and Futures Prices: Evidence
from Cointegration and Error Correction Models
--
Rafiqul Bhuyan, David Williams,
Syed Ahamed and Mohammad
G Robbani
This
paper explores the application of cointegration and error
correction techniques to study the daily futures and cash
prices on the S&P 500 index. Cointegration analysis is
used to examine the temporal causal linkage between the S&P
500 stock index and futures daily closing prices for the year
1998. If the S&P 500 index and futures markets are efficient,
the best forecast of next period's price is the price of this
period. However, based on root mean square error comparisons,
results from this paper indicate that the proposed error correction
specification outperforms naive univariate forecasts. Thus,
excluding transaction costs, it appears that this modeling
strategy has the potential to profitably predict price changes.
©
2006 IUP . All Rights Reserved.
Short-term
Forecasting of NIFTY Index Using Support Vector Regression
-- V Prasanna Shrinivas,
Sandeep
Dulluri and N R Srinivasa Raghavan
Financial
index prediction is the key for advanced financial information
services. The movement of financial indices depends on various
factors. Though time series analysis has been used for addressing
the problem of predicting the movement of indices, the performance
has been far from satisfactory. Of late, machine-learning
techniques are employed for addressing this problem. One such
technique is Support Vector Regression (SVR). SVR is based
on Support Vector Machines, the state-of-the-art machine-learning
algorithm. SVMs' are strongly based on duality theory in optimization.
The basic idea of the algorithm is to perform a regression
in the kernel induced space using mapped data points or features.
It turns out that the optimal solution is always a linear
combination of all features. The feasible solutions lie within
a subset of this feature space and the area of interest of
this research is limited by this constraint. In the current
research SVR for short-term prediction of NSE S&P CNX
NIFTY index from 1-12-1995 to 30-10-2004 is applied and the
performance is compared with conventional time-series prediction
method. SVR also helps in capturing and modeling the long-term
behavior of the system. The research strengthens that the
radial basis function kernel suits time-series prediction
better than many other kernels. It reveals that the performance
of SVR is much better than that of conventional techniques
in reducing the relative mean errors and root mean square
error of predicted financial index.
©
2006 IUP . All Right Reserved.
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