Search results

Analyses the structural behaviour of money and capital market interest rates 1999‐2001 in the European Monetary Union. Finds that the positive correlations between interest rates of different time periods get stronger as the time periods get closer, derives the principal components which explain most of the variability and applies time series analysis to the model to produce forecasts which come very close to actual values. Develops a model of the Down Jones EURO STOXX financial sector index which also shows reliable forecasting power except for the Feb 2001 period when stock markets were “in turmoil”. Compares the returns for one‐month holdings of zero‐coupon European government bonds with different maturities for various return, risk and risk‐return measures; and finds that a one‐month holding of a two‐year bond is the best investment.

In estimating the inputs into the modern portfolio theory (MPT) portfolio optimisation problem, it is usual to use equal weighted historic data. Equal weighting of the data, however, does not take account of the current state of the market. Consequently this approach is unlikely to perform well in any subsequent period as the data is still reflecting market conditions that are no longer valid. The need for some return weighting scheme that gives greater weight to the most recent data would seem desirable. Therefore, this study uses returns data which are weighted to give greater weight to the most recent observations to see if such a weighting scheme can offer improved ex ante performance over that based on unweighted data.

The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods.

This study has used an advanced set of volatility models such as autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH (1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple exponentially weighted moving average (EWMA) model.

The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly returns making only the EWMA model usable to measure the volatility level in the monthly series. The P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns, while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of volatility, a mean reverting process and an absence of a risk premium in the KSE market with an insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low volatility and the global economic crisis did not cause a rise in volatility levels.

Previously, the literature about volatility modeling in Pakistan’s markets has been limited to a few models of relatively small sample size. The current thesis has attempted to overcome these limitations and used diverse models for three types of data series (daily, weekly and monthly). In addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the volatility levels of the KSE.

The purpose of this paper is to explore the forecasting effectiveness of Black-Scholes (BS) focussing parity analysis of time series econometric and implied volatility (IV) numerical techniques.

To analyze the comparative competitiveness of econometric time series and IV models this paper consolidated the study with their inter-relations leading toward multilayered moneyness-maturity correlation of model and market option prices, thoroughly determined the moneyness-maturity combinations of error metrics of Nifty index options.

Out of six models tested and critically examined here, the paper procures only a single model, IV, which best caters to the requirements of option traders and as a result the paper ended up that only IV supports to multifarious moneyness-maturity dimension of option pricing of Nifty index options. The analysis also confirms that the standard VIX is not a reliable tool for determining the base price of Nifty index options (via BS). As the IV landmarks during the most dynamic phase of Indian capital market which is a touchstone to justify the quality of any model, the paper can deduce that IV could continue to perform in hardships of financial contraction par smoothly and effectively.

The final outcome of this research which ended successfully in exploring a dominant model, guided successfully through the most volatile period of Indian economy can be used to safe guard investor's faith and to figure a design which could compete on the canvass of option pricing.

As equity market is always subject to highly unpredictable conditions and may keep on experiencing it through all times to come, the unified objective of research is to find out the most impeccable volatility model to meet out the requirements of option practitioners, specifically contributing upto the satisfaction and expected results during tumultuous period.

I perform the backtesting of 10-day VaR's using daily returns of KOSPI 200 from January 1994 to December 1993 (2,692 days). The seven volatility measures are calculated with the last 300-day data; those are the historical standard deviations, the exponentially weighted moving average (EWMA) volatilities, the standard deviations from GARCH (1, 1) and three measures to consider autocorrelations in daily returns. The seven types of ten-day VaR’s at 1 % and 5% significance levels are estimated from these six volatility measures and 1 or 5 percentile of the last 300-day historical distributions I use the likelihood ratio (LR) test statistics to test the expected frequency and/or independence of the occurrence of extreme losses, that is, the losses which exceed the VaR values. The LR statistics for the expected frequence show that the VaR measure based on the historical standard deviations is the best one, but the LR statistics for independence reject the usefulness of ali the VaR measures.

In this paper, we examine which volatility estimation model best explains KOSPI200-realized volatility in the Korean stock market, which has both heteroscedasticity and jump risk. The sample covers from July 1, 2010 to July 31, 2014, which is a low-volatility period in Korean stock market by which time the effects of the global crisis had almost vanished. We use the intra-day return of KOSPI200, which has been measured by 5-minute intervals. This study finds GARCH-family models are efficient estimators compared to historical volatility and EWMA. Also, among the GARCH-family models, Jump-Diffusion GARCH has shown comparatively good results. Especially this study finds that VKOSPI200 is the most efficient model with the largest adj. R2 and the smallest evaluation statistics during the sample period. Meanwhile, it seems to be necessary to consider jump risk when we estimate volatility in Korean stock market.

This study aims to propose and test an innovative methodology for assessing mortgage lending value. The method tries to improve and rationalize, within the canonical and derivative approach that is generally used by the sector operators, the appraisal of the percentage reduction to be applied to the market value.

Considering that the European Mortgage Federation and the Basel Committee highlight the importance of information about the risks of properties to be loaned on, the value at risk approach has been developed so as to assess the mortgage lending value as a technique of risk analysis. With reference to the Italian context, the method elaborates the historical analysis of the property values in 93 major Italian cities for the residential and commercial intended uses in a significant period (1967-2015) and allows to determine the reduction coefficients of the market value as a function of the central, semi-central and peripheral locations of the property.

The results include the reduction coefficients of the market value for the derivative appraisal of the mortgage lending value. The coefficients obtained satisfy the need for a rational assessment of the property risk and the appropriate spatial contextualization of the risk components related to the local demand and supply, thus eliminating any inconsistency and danger of determining the mortgage lending value using a simple and lump-sum percentage deduction of the market value.

The global economic crisis in the past decade, triggered by the 2007 US Subprime mortgage crisis and consequent collapse of property values, has highlighted the need for high level professional skills in the appraisal of properties as securities for credit exposures. The method proposed for the assessment of the mortgage lending value allows to overcome the uncertainties underlying the determination of an independent value through indirect methods (income approach, cost approach) and rationalize the appraisal of the risk in the traditional derivative approach through a flexible procedure, with it being possible to adapt it to any territorial context, as well as any intended use.

The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations.

First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency.

The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected.

The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied.

In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method.

It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms.

This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc.

This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy.

This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

The recent 2008–2009 financial crisis has led international financial authorities to review the existing regulation; the Basel Committee on Banking Supervision has been thus induced to review the pillars of the Basel Accord (Basel II) in order to strengthen the risk coverage of capital framework (Basel 2.5 and III). These reforms will help to raise capital requirements for the trading book, which represents a major source of losses for internationally financial institutions, especially during crisis periods. In particular, the Committee has introduced a Stressed Value-at-Risk (SVaR) capital requirement, as a new methodology to evaluate market risk.

This chapter aims to shed some lights on the issues major banks have to face when calculating SVaR in the context of emerging markets, pointing out the differences in adopting an estimation model with respect to another one. Our results show a considerable increase in capital requirements especially when new rules are applied to financial markets with high-risk parameters, such as emerging markets are. The increased cost due to higher capital requirements could be a disincentive to investment in markets with higher risk profiles than the developed markets, taking also into account that diversification benefits deriving from investing in emerging economies have shown a decrease over time. The reduction of institutional investors can thus represent a brake on the process of innovation and evolution of emerging markets.