Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
This paper models the value of callable Eurobonds, using stochastic calculus,\nby assuming that the exchange rate follows a geometric Brownian motion\nprocess and the arrival time of an early redemption of the bond by the issuer\nconforms to a negative exponential distribution. The solution to the stochastic\nmodel shows that there is a relationship between the call premium and the\nexpected time to the call which is consistent with traditional Black-Scholes\npricing formulae. The magnitude of the call premium can be viewed as a signal\nto the market on a Government treasuryâ??s or companyâ??s expectations\nabout the future level of interest rates and possible refinancing strategies. This\npaper is unique because as of July 2019 there exists no attempt at valuing\nCallable Eurobonds in the research literature....
The binomial distribution describes the probability of the number of successes\nfor a fixed number of identical independent experiments, each with binary\nout-put. In real life, practical applications like portfolio credit risk management\ntrials are not identical and have different realization probabilities. In\naddition to the number, the quantitative impacts of the respective outputs are\nalso important. There exist no complete model-side implementations for the\nexpansion of the binomial distribution, especially not in the case of specific\nquantitative parameters up to now. Here, a solution of this issue is described by\nthe extended binomial distribution. The key for solving the problem lies in the\nuse of bijection between the elementary events of the binomial distribution\nand the digit sequences of binary numbers. Based on the extended binomial\ndistribution, an analytical portfolio credit risk model is described. The binomial\ndistribution approach minimizes the approximation error in modeling.\nIn particular, the edges of the loss distribution can be determined in a\nrealistic manner. This analytical portfolio credit risk model is especially predestined\nfor management of risk concentrations and tail risks....
This study applies threshold regression model in a bivariate framework to explore\nthe nonlinear long-term relationship among Bitcoin and gold prices\nover the period 2010-2018. Results are threefold: first, we show that gold is a\nsignificant predictor of Bitcoin prices. Second, we find evidence of a\nnon-linear relationship between Bitcoin and gold prices characterized rather\nby a two-regime relationship with a structural break occurring in October\n2017. Third, before the break, there is significant, negative but weak causality\nindicating that Bitcoin is a speculative asset. After the break, the relationship\nbecomes significantly positive revealing diversifier and hedge properties of\nBitcoin....
Considering the effect of inflation on financial development and economic\ngrowth, in this paper we investigate the role of inflation in the effect of financial\ndevelopment on the economic growth in OPEC economies for the period\nof 1970 to 2015. For this purpose, we used Panel Smooth Threshold Regression\n(PSTR) to estimate nonlinear effects of inflation in the relation between\neconomic growth and financial development. The results of estimation show\nthat the thresholds of inflation rate were 20.33 and 20.36 considering the two\ndifferent proxies of financial development (domestic credit provided by banking\nand domestic credit to private sector). The results of nonlinear estimation\nshowed that in over-threshold inflations, the effect of financial development\non the economic growth in the OPEC economies would decrease and would\neven become negative....
Every investor in the market has access to the stock names, making it the\nmost popular information. However, this piece of information is often ignored\nby people and considered insignificant in the decision process. In fact,\nit is almost always the stock names that give investors the first impression of a\nstock, and thus psychologically speaking, should in turn impact the decision\nprocess. In this paper, we score the (Chinese) stock names according to the\nmeaning and the efficiency of passing information, so that we can work out a\nquantitative analysis of the stock names. Theoretically we derive the relationship\nbetween the stock name scores and the expected stock returns. Practically\nwe build up an imaginary market-neutral portfolio and analyze its return\nby historical data. From both the theoretical and the practical aspects we discuss\nthe two hypothesesâ??the liking theory and the information theory, and\nwe show that in the Chinese stock market, the liking theory dominates, which\nis opposite to the result from the US stock market....
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