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263 KiB

Open AccessArticle

Corporate Governance and Corporate Creditworthiness

by Dror Parnes

J. Risk Financial Manag. 2011, 4(1), 1-42; https://doi.org/10.3390/jrfm4010001 - 31 Dec 2011

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We examine the relation between corporate governance and bankruptcy risk as an underlying force affecting a bond’s yield. The level of corporate governance is captured by the G-index, along with the explicit groups of governance provisions. We estimate bankruptcy risk by Z-score, by [...] Read more.

We examine the relation between corporate governance and bankruptcy risk as an underlying force affecting a bond’s yield. The level of corporate governance is captured by the G-index, along with the explicit groups of governance provisions. We estimate bankruptcy risk by Z-score, by cash-flow-score, by O-score, through Merton structural model default probabilities, and by S&P credit ratings. After addressing endogeneity and while controlling for firm-specific factors, based on the four objective methodologies we find that corporate governance is inversely related to bankruptcy risk. Yet, rating agencies take a mixed approach towards this association likely because of the conflicting impact of different governance provisions. Full article

239 KiB

Open AccessArticle

A Pseudo-Bayesian Model for Stock Returns In Financial Crises

by Eric S. Fung, Kin Lam, Tak-Kuen Siu and Wing-Keung Wong

J. Risk Financial Manag. 2011, 4(1), 43-73; https://doi.org/10.3390/jrfm4010043 - 31 Dec 2011

Cited by 10 | Viewed by 4083

Abstract

Recently, there has been a considerable interest in the Bayesian approach for explaining investors' behaviorial biases by incorporating conservative and representative heuristics when making financial decisions, (see, for example, Barberis, Shleifer and Vishny (1998)). To establish a quantitative link between some important market [...] Read more.

Recently, there has been a considerable interest in the Bayesian approach for explaining investors' behaviorial biases by incorporating conservative and representative heuristics when making financial decisions, (see, for example, Barberis, Shleifer and Vishny (1998)). To establish a quantitative link between some important market anomalies and investors' behaviorial biases, Lam, Liu, and Wong (2010) introduced a pseudo-Bayesian approach for developing properties of stock returns, where weights induced by investors' conservative and representative heuristics are assigned to observations of the earning shocks and stock prices. In response to the recent global financial crisis, we introduce a new pseudo-Bayesian model to incorporate the impact of a financial crisis. Properties of stock returns during the financial crisis and recovery from the crisis are established. The proposed model can be applied to investigate some important market anomalies including short-term underreaction, long-term overreaction, and excess volatility during financial crisis. We also explain in some detail the linkage between these market anomalies and investors' behavioral biases during financial crisis. Full article

248 KiB

Open AccessArticle

Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models

by Shu Wing Ho, Alan Lee and Alastair Marsden

J. Risk Financial Manag. 2011, 4(1), 74-96; https://doi.org/10.3390/jrfm4010074 - 31 Dec 2011

Cited by 1 | Viewed by 5274

Abstract

The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the implied volatility [...] Read more.

The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the implied volatility of stocks derived from traded call and put options prices compared to historical volatility estimates sourced from IVolatility.com (“IVolatility”). Our evidence suggests use of the Bayesian approach to estimate volatility can provide a more accurate measure of ex-ante stock price volatility and will be useful in the pricing of derivative securities where the implied stock price volatility cannot be observed. Full article

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338 KiB

Open AccessArticle

Periodically Collapsing Bubbles in Stock Prices Cointegrated with Broad Dividends and Macroeconomic Factors

by Man Fu and Prasad V. Bidarkota

J. Risk Financial Manag. 2011, 4(1), 97-132; https://doi.org/10.3390/jrfm4010097 - 31 Dec 2011

Cited by 1 | Viewed by 3784

Abstract

We study fluctuations in stock prices using a framework derived from the present value model augmented with a macroeconomic factor. The fundamental value is derived as the expected present discounted value of broad dividends that include, in addition to traditional cash dividends, other [...] Read more.

We study fluctuations in stock prices using a framework derived from the present value model augmented with a macroeconomic factor. The fundamental value is derived as the expected present discounted value of broad dividends that include, in addition to traditional cash dividends, other payouts to shareholders. A stochastic discount factor motivated by the consumption-based asset pricing model is utilized. A single macroeconomic factor, namely the output gap determines the non-fundamental component of stock prices. A resulting trivariate Vector Autoregression (TVAR) model of stock prices, broad dividends, and the output gap shows evidence of cointegration in the DJIA and S&P 500 index data. Nonetheless, a sup augmented Dickey-Fuller test reveals existence of periodically collapsing bubbles in S&P 500 data during the late 1990s. Full article

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272 KiB

Open AccessArticle

Multiperiod Hedging using Futures: Mean Reversion and the Optimal Hedging Path

by Vadhindran K. Rao

J. Risk Financial Manag. 2011, 4(1), 133-161; https://doi.org/10.3390/jrfm4010133 - 31 Dec 2011

Viewed by 4150

Abstract

This paper considers the multiperiod hedging decision in a framework of mean-reverting spot prices and unbiased futures markets. The task is to determine the optimal hedging path, i.e., the sequence of positions in futures contracts with the objective of minimizing the variance of [...] Read more.

This paper considers the multiperiod hedging decision in a framework of mean-reverting spot prices and unbiased futures markets. The task is to determine the optimal hedging path, i.e., the sequence of positions in futures contracts with the objective of minimizing the variance of an uncertain future cash flow. The model is used to illustrate both hedging using a matchedmaturity futures contract and hedging by rolling over a series of nearby futures contracts. In each case, the paper derives the conditions under which a single period (myopic) strategy would be optimal as opposed to a dynamic multiperiod strategy. The results suggest that greater the market power of the hedging entity, closer the optimal strategy is to a myopic hedge. The paper also highlights the difference in the optimal hedging path when hedging is based on matched-maturity as opposed to nearby contracts. Full article

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J. Risk Financial Manag., Volume 4, Issue 1 (December 2011) – 5 articles , Pages 1-161

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