...predictability CHAPTER ONE INTRODUCTION 1.1 BACKGROUND TO THE STUDY The performance of an economy is dependent largely on the efficient performance of its financial markets, since they facilitate the financing of productive activity and hence national output and economic growth. In this research report, the key roles and function of the financial markets are highlighted with the thrust of the discussion on the core issue of how the market works; directly and indirectly. One of the most important factors for rapid economic development is the effective mobilization and allocation of scarce resources within an economy. These resources can be real or financial, but they are scarce and command a price. The establishment of effective and efficient channel for the mobilization and allocation of scarce financial resources is therefore essential. The financial market, comprising of the money and capital markets, occupies an important place in most economies of the world. The primary function of a financial market is to enable funds to be sufficiently allocated from the surplus units of the economy to the deficit units for productive investment. The greater the transmission efficiency is, the higher the rate of growth of the economy (Olowe, 1997). The money market trades only in securities or debt instruments maturing in less than twelve months, while in the capital market, longer term debts as well as equity instruments are traded. The complementarity between money market...
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... Part A 1. Dividend Growth Rates and Positive Correlation – Dimson and Marsh (‘DM’) affirm the previous periods of small cap outperformance (particularly in the United States and the UK) concurred with superior real dividend growth from small caps. Conversely the subsequent under performance coincided with inferior dividend growth[1]. Thus DM implies the reason for the reversal related to underlying business performance of small caps relative to large companies rather than investor’ sentiment. Specific evidence to support this view was that from 1988 to 1997 small cap dividends grew at a rate 2% p.a. less than large cap stocks, which was in direct contrast to the period 1955 to 1988. Furthermore, over the same period, the small cap price/dividend multiple fell by 4.2%. When these factors are added together the approximate 6% small cap reversal is clear[2]. 2. Differences in sector composition – portfolio concentration measures the extent to which a portfolios...
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...analysis for Brazil under inflation targeting Gabriel Caldas Montes1* Alexandre Curi ** Abstract This paper raises the following hypotheses: to the extent that the monetary authority successively reaches the inflation target and credibility increases, expectations will have more influence on inflation and, thus, the efforts of the monetary authority to reach the inflation target will decrease. Hence, the goal of this work is twofold: 1) a theoretical model is developed to show that when the monetary authority is committed to the goal of price stability, the gain of credibility not only acts by producing a better result in terms of inflation, but also it reduces the volatility of the basic interest rate, and; 2) based on the Brazilian economy, the article provides empirical evidence that the gain of credibility is crucial to reduce the volatility of the basic interest as well as the inflation rate. The findings suggest that credibility plays a key role for the conduct of monetary policy and inflation control. Keywords: inflation targeting, credibility, interest rate, inflation JEL classification: E43, E52, E58 * Fluminense Federal University, Department of Economics, National Council for Scientific and Technological Development (CNPq), Brazil. Rua Tiradentes, 17, Ingá, Niterói, Rio de Janeiro, CEP: 24210-510. gabrielmontesuff@yahoo.com.br ** Fluminense Federal University, Department of Economics, Rua Tiradentes, 17, Ingá, Niterói, Rio de Janeiro, CEP: 24210-510. alexandre...
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...How to benefit from stock futures You are bullish on a stock say Satyam, which is currently quoting at Rs 280 per share. You believe that in one month it will touch Rs 330. Question: What do you do? Answer: You buy Satyam. Effect: It touches Rs 330 as you predicted – you made a profit of Rs 50 on an investment of Rs 280 i.e. a Return of 18% in one month – Fantastic!! Wait: Can it get any better? Yes!! Question: What should you do? Answer: Buy Satyam Futures instead. Effect: On buying Satyam Futures, you get the same position as Satyam in the cash market, but you pay a margin and not the entire amount. For example, if the margin is 20%, you would pay only Rs 56. If Satyam goes upto Rs 330, you will still earn Rs 50 as profit. Now that translates into a fabulous return of 89% in one month. Unbelievable!! But True nevertheless!! This is the advantage of ‘leverage’ which Stock Futures provide. By investing a small margin (ranging from 10 to 25%), you can get into the same positions as you would be able to in the cash market. The returns therefore get accordingly multiplied. Question: What are the risks? Answer: The risks are that losses will be get leveraged or multiplied in the same manner as profits do. For example, if Satyam drops from Rs 280 to Rs 250, you would make a loss of Rs 30. The Rs 30 loss would translate to an 11% loss in the cash market and a 54% loss in the Futures market. Question: How can I reduce such losses? Answer: It is very easy...
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...Asset Value and Volatility Estimation for Corporate Credit Rating 1009611462 LUFEI Xiaoxin 1009611301 HE Yao Abstract The market-based credit models make use of market information such as equity values to estimate a firm’s credit risk. The Merton model and the Black-Cox model are two popular models that link asset value with equity value, based on the option pricing theories. Under these models, the distance to default can be derived and thus the default probability can be mapped to as long as a large database of companies is provided. The difficulty, however, is that some parameters, including asset values and asset volatilities, which are required in calculating the distance to default, are unobservable in market. Therefore, statistical methods need to be developed in order to estimate the unobservable parameters. In this project, our focus is on using KMV method, which has been widely used in the industry, to estimate corporates’ asset values and asset volatilities. We implemented two models and did numeric study by simulation, which shows that the KMV method gives generally accurate estimates under both models. We also analyzed the model risk under different circumstances. The barrier sensitivity analysis gives the result of how sensitive the Black-Cox model is in choosing different barriers, in the relation with asset volatility and debt level. Furthermore, the models are applied to real companies with different leverage ratios, which shows that structural models are...
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...MEASURING TIME VARYING VOLATILITY OF USDINR CURRENCY FUTURES IN INDIA *Suhashini.J ** Dr.Chandrasekar.K *Suhashini.J, Faculty Research Scholar, PSNA College of Engineering and Technology, Dindigul, Tamilnadu.Suhashinij@gmail.com **Dr.K.Chandrasekar, Assistant Professor, Alagappa Institute of Management, Alagappa University, Kariakudi. MEASURING TIME VARYING VOLATILITY OF USDINR CURRENCY FUTURES IN INDIA Abstract This paper examines the volatility of USDINR currency pair. USDINR currency pair was introduced in regulated stock exchange of National Stock Exchange in the year 2008. USDINR currency stated to trade as a future instrument on 29.08.2008. Though it’s a delayed decision undertaken in India to introduce currency futures in regulated exchange within the three years of its introduction 10 times of volume traded has increased. The pricing of currencies is supposed to be dependent on volatility of the markets. Therefore it’s important to know the volatility implications of currency market to trade in futures market. To understand volatility implications it is examined using ARCH, GARCH, and GARCH (1, 1) model in this paper. The study finds the evidence of time varying volatility of futures. The study finds an evidence of time varying volatility, which exhibits clustering, high persistence and predictability of currency futures in Indian Market. Key words: Time Varying Volatility, currency futures, USDINR and GARCH Introduction Currency Futures has been...
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...Nobel memorial prize[1] for the theory, in recent years the basic assumptions of MPT have been widely challenged by fields such as behavioral economics. MPT is a mathematical formulation of the concept of diversification in investing, with the aim of selecting a collection of investment assets that has collectively lower risk than any individual asset. That this is possible can be seen intuitively because different types of assets often change in value in opposite ways. For example, to the extent prices in the stock market move differently from prices in the bond market, a collection of both types of assets can in theory face lower overall risk than either individually. But diversification lowers risk even if assets' returns are not negatively correlated—indeed, even if they are positively correlated More technically, MPT models an asset's return as a normally distributed function (or more generally as an elliptically distributed random variable), defines risk as the standard deviation of return, and models a portfolio as a weighted combination of assets, so that the return of a portfolio is the weighted combination of the assets' returns. By combining different assets whose returns are not perfectly positively correlated, MPT seeks to reduce the total variance of the portfolio return. MPT also assumes that investors are...
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...NBER WORKING PAPER SERIES FINANCIAL RISK MEASUREMENT FOR FINANCIAL RISK MANAGEMENT Torben G. Andersen Tim Bollerslev Peter F. Christoffersen Francis X. Diebold Working Paper 18084 http://www.nber.org/papers/w18084 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 May 2012 Forthcoming in Handbook of the Economics of Finance, Volume 2, North Holland, an imprint of Elsevier. For helpful comments we thank Hal Cole and Dongho Song. For research support, Andersen, Bollerslev and Diebold thank the National Science Foundation (U.S.), and Christoffersen thanks the Social Sciences and Humanities Research Council (Canada). We appreciate support from CREATES funded by the Danish National Science Foundation. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2012 by Torben G. Andersen, Tim Bollerslev, Peter F. Christoffersen, and Francis X. Diebold. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Financial Risk Measurement for Financial Risk Management Torben G. Andersen, Tim Bollerslev, Peter F. Christoffersen, and...
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...AN ANALYTICAL STUDY ON EFFICACY OF ALGORITHM FOR BOTH TRADING AND INVESTING AN ANALYTICAL STUDY ON EFFICACY OF ALGORITHM FOR BOTH TRADING AND INVESTING ABSTRACT INDEX AIM OF STUDY PURPOSE * The main agenda of this study is to test the basic oscillators like RSI and OBV is to identify the behavior of these early indicators in various types of market. The agenda of using moving average lag indicators like Bollinger band is to check how well these bands work in giving out trade signals. * The study aims to find out using Bloomberg terminal that whether combination of studies and Risk management help to enhance the performance of the indicators and do they really help to make a more profitable decision. * This study also intends to use some basic fundamental indicators to identify whether they can be used as tool to invest in securities and how well they are able to perform as compared to a benchmark index. The aim is to use a matrix of indicators, so that it can be also assessed whether combination of basic indicators are good enough to make portfolio creation judgment that can lead to market beating portfolio or not. * All the testing has been done using the Bloomberg terminal. LIMITATIONS * There are many lead, hybrid and lag indicators available in the market however not every single one can be tested. * The testing only targets the NSE that is typically Indian market, hence the results may be non-inferential for international markets...
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...for their invaluable direction, patience, and guidance throughout this entire process. Abstract The goal of this paper is to investigate the forecasting ability of the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH). We estimate the DCC’s forecasting ability relative to unconditional volatility in three equity-based crashes: the S&L Crisis, the Dot-Com Boom/Crash, and the recent Credit Crisis. The assets we use are the S&P 500 index, 10-Year US Treasury bonds, Moody’s A Industrial bonds, and the Dollar/Yen exchange rate. Our results suggest that the choice of asset pair may be a determining factor in the forecasting ability of the DCC-GARCH model. I. Introduction Many of today’s key financial applications, including asset pricing, capital allocation, risk management, and portfolio hedging, are heavily dependent on accurate estimates and well-founded forecasts of asset return volatility and correlation between assets. Although volatility and correlation forecasting are both important, however, existing literature has dealt more closely with the performance of volatility models – only very recently has the issue of correlation estimation and forecasting begun to receive extensive investigation and analysis. The goal of this paper is to extend research that has been undertaken regarding the forecasting ability of one specific correlation model, the Dynamic...
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...Empirical Finance Value, Momentum, and Volatility ABSTRACT In this paper we approach two major topics on the central debate of asset pricing theory: the returns to value and momentum strategies and also, the comparison of volatility models. Our analysis is divided in two parts: in the first, we provide a monthly view on 115 stocks from the S&P 500 index for the past twenty four years and the respective return premia resulting from value and momentum strategies. In the latter part, the main goal is to test different volatility models by analyzing historical data from Microsoft stocks. Therefore, we follow the structure of Asness et al. (2013) while analyzing value and momentum, and used the methodology of several authors to define and calibrate the data. Our results are in line with the literature since we detected return premium for value and also for momentum. Nevertheless, not all of the conclusions of the literature are confirmed in our analysis, as we will demonstrate. On the second section, ARCH (5), GARCH (1,1) and Taylor/Schwert GARCH(1,1) models are tested revealing the supremacy of the latter. Key words: Market efficiency, Value, Momentum, ARCH, GARCH, Taylor/Schwert, Volatility Models. 1. Introduction Our research is mainly related with the recent literature published on global asset pricing. We have followed Asness et al. (2013) where the authors present evidence of value and momentum return premia across eight different asset classes and markets. Moreover...
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...Fixed Income Securities Tools for Today’s Markets Second Edition BRUCE TUCKMAN John Wiley & Sons, Inc. Copyright © 2002 by Bruce Tuckman. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax 978-750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, 201-748-6011, fax 201-748-6008, e-mail: permcoordinator@wiley.com. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies...
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...Beyond Technical Analysis Beyond Technical Analysis: How to Develop and Implement a Winning Trading System Tushar S. Chande, PhD John Wiley 61 Sons, Inc. New York • Chichester • Brisbane • Toronto • Singapore • Weinheim This text is printed on acid-free paper. Copyright © 1997 by Tushar S. Chande. Published by John Wiley & Sons, Inc. Data Scrambling is a trademark of Tushar S. Chande. TradeStadon, System Writer Plus, and Power Editor are trademarks of Omega Research, Inc. Excel is a registered trademark of Microsoft Corporation. Continuous Contractor is a trademark of TechTools, Inc. Portfolio Analyzer is a trademark of Tom Berry. All rights reserved. Printed simultaneously in Canada. Reproduction or translation of any part of this work beyond that permitted by Section 107 or 108 of the 1976 United States Copyright Act without the permission of the copyright holder is unlawful. Requests for permission or further information should be addressed to the Permissions Department of John Wiley & Sons. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If legal advice or other expert assistance is required, the services of a competent professional person should be sought. Library of Congress Cataloging in Publicaton Data: Chande, Tushar S., 1958Beyond technical analysis : how...
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... 6-5-2012 Modelling and forecasting volatility in the gold market Stefan Trück Macquarie University, stefan.trueck@mq.edu.au Kevin Liang Macquarie University Follow this and additional works at: http://epublications.bond.edu.au/ijbf Recommended Citation Trück, Stefan and Liang, Kevin (2012) "Modelling and forecasting volatility in the gold market," International Journal of Banking and Finance: Vol. 9: Iss. 1, Article 3. Available at: http://epublications.bond.edu.au/ijbf/vol9/iss1/3 This Journal Article is brought to you by the Faculty of Business at ePublications@bond. It has been accepted for inclusion in International Journal of Banking and Finance by an authorized administrator of ePublications@bond. For more information, please contact Bond University's Repository Coordinator. Trück and Liang: Forecasting volatility in the gold market International Journal of Banking and Finance, Volume 9 (Number 1), 2012: pages 48-80 MODELLING AND FORECASTING VOLATILITY IN THE GOLD MARKET Stefan Trück and Kevin Liang Macquarie University, Australia _____________________________________________ Abstract We investigate the volatility dynamics of gold markets. While there are a number of recent studies examining volatility and Value-at-Risk (VaR) measures in financial and commodity markets, none of them focuses on the gold market. We use a large number of statistical models to model and then forecast daily volatility and VaR. Both insample and out-of-sample...
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...cumulative density function 1 t 2 1 F x dt exp 2 2 Standard Normal Density X ~ N 0,1 probability density function n x cumulative density function x N x 1 1 exp x 2 2 2 x important result: standardization 1 exp t 2 dt 2 2 1 if X~N , 2 and Z= then Z~N 0,1 X- 1 Mathematical Expectation: Given a random variable X and its pdf f x we define the expectation of the function g X to be the integral E g X g x f x dx Note that g X is also a random variable The Moment Generating Function (MGF) The MGF of a random variable X is a function of t denoted by M X t E e xt which is an expectation MGF of normal If X ~ N , 2 1 x 1 Xt xt Then M X t E e e e 2 2 Lognormal Distribution: 2 1 t 2t 2 dx e 2 Y has the lognormal distribution with parameters , 2 if: its logarithm is normally distributed X log e Y ~ N , 2 . This in turn means that Y e X 2 The cumulative density function of Y is log e y FY y Pr Y y N x 12 1 2t where N x e dt the cdf of the standard normal distribution 2 The probability density function of Y is 1 log y 2 1 e fY y ...
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