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on Corporate Finance |
| By: | Ana Fernandes |
| Abstract: | Why are new financial instruments created? This paper proposes the view that financial development arises as a response to the contractual needs of emerging technologies. Exogenous technological progress generates a demand for new financial instruments in order to share risk or overcome private information, for example. A model of the dynamics of technology adoption and the evolution of financial instruments that support such adoption is presented. Early adoption may be required for financial markets to learn the technology; once learned, financial innovation boosts adoption further. Financial learning emerges as a source of technological diffusion. The analysis identifies a causality link from technology to growth which is nonetheless consistent with empirical findings of a positive effect of current financial development on future growth |
| Keywords: | Technology adoption; financial innovation; learning |
| JEL: | G20 N20 O30 |
| Date: | 2005–06 |
| URL: | http://d.repec.org/n?u=RePEc:ube:dpvwib:dp0513&r=cfn |
| By: | Alessandro Sansone (Department of Economic Sciences; University of Rome “La Sapienza” & School of Finance & Economics; University of Technology, Sydney); Giuseppe Garofalo (Department of Managerial, Technological & Quantitative Studies; University of Viterbo “Tuscia” & Department of Public Economics; University of Rome “La Sapienza”) |
| Abstract: | In this paper we present a continuous time dynamical model of heterogeneous agents interacting in a financial market where transactions are cleared by a market maker. The market is composed of fundamentalist, trend following and contrarian agents who process information from the market with different time delays. Each class of investors is characterized by path dependent risk aversion. We also allow for the possibility of evolutionary switching between trend following and contrarian strategies. We find that the system shows periodic, quasi-periodic and chaotic dynamics as well as synchronization between technical traders. Furthermore, the model is able to generate time series of returns that exhibit statistical properties similar to those of the S&P500 index, which is characterized by excess kurtosis, volatility clustering and long memory. |
| Keywords: | Dynamic asset pricing; Heterogeneous agents; Complex dynamics; Strange attractors; Chaos; Intermittency; Stock market dynamics; Synchronization |
| JEL: | G11 G12 G14 |
| Date: | 2005–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpfi:0510026&r=cfn |
| By: | Sascha Mergner (AMB Generali Asset Managers); Jan Bulla (Georg-August-University, Goettingen) |
| Abstract: | This paper investigates the time-varying behavior of systematic risk for eighteen pan-European sectors. Using weekly data over the period 1987- 2005, four different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t-GARCH(1,1) model, two Kalman filter based approaches, a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique as well as two Markov switching models. A comparison of the different models' ex-ante forecast performances indicates that the random walk process in connection with the Kalman filter is the preferred model to describe and forecast the time-varying behavior of sector betas in a European context. Remarkably, the Markov switching models yield a worse out-of-sample performance than standard OLS. |
| Keywords: | Markov switching; Kalman filter; stochastic volatility; efficient Monte Carlo likelihood; bivariate t-GARCH; European industry portfolios; time-varying beta risk |
| JEL: | C22 C32 G10 G12 G15 |
| Date: | 2005–10–26 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpfi:0510029&r=cfn |
| By: | Gerhard Schroeder (Privat Experimental Research) |
| Abstract: | By analyzing fictitious options - a unique approach - significant mispricing due to the formula of Black and Scholes can be shown systematically and independent from market distortion. Even options based on fictitious, lognormally distributed courses are not valued properly. According to the Law of Large Numbers pricing models based on time distibutions should be applied to strategies rather than to single option prices. The discontinuity of autocorrelation (Stalagmites Effect) has impact on forecasting models. The current impact of volatility - there is no - on option pricing is not justified. |
| Keywords: | Black Scholes, fair value, option pricing, mispricing, derivatives, stalagmites effect, artificially generated 'cloned' quotations, test methods, experimental economical research, predicting, forecasting |
| JEL: | F3 F4 |
| Date: | 2005–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpif:0510024&r=cfn |