Abstract Foreign direct investment (FDI) is considered in the literature as an essential driver of economic growth. This paper aims to investigate the impact of FDI on economic growth of Sierra Leone economy. As a whole, the period under consideration is a thirty-seven year period spanning from 1980 to 2016. Most researchers conclude that there is a positive impact of FDI on Economic Growth of a nation’s economy, but in this paper, we discovered that FDI has no relationship with economic growth in Sierra Leone. Empirical methods were used to analyze data and results are based on regression analysis con ducted from available data. This paper ascertains that FDI (stock) inflow in Sierra Leone has no impact on the economic growth of the nation.

Abstract This paper deals with asset allocation decisions when the considered risk meas ure is directly related to the investor’s level of risk aversion. It is well known that the optimal portfolio weights are considerably sensitive to how assets are ranked on the basis of their risk-return profile. We propose a procedure to construct optimal portfolios that adapt quickly to changes in risk using a time varying asset allocation model based on a modified Sharpe Ratio measure re lated to downside and upside risk weighted using an aversion parameter. The model is applied, as an illustrative example, to six stock markets located in Western Europe basing the analysis on monthly data covering the period January 2000-December 2020.

Abstract We study the Option pricing with linear investment strategy based on dis crete time trading of the underlying security, which unlike the existing con tinuous trading models, provides a feasible real market implementation. Closed form formulas for Call and Put Option price are established for fixed interest rates and their extensions to stochastic Vasicek and Hull-White in terest rates

Abstract Asset pricing models are almost always tested using stock returns over multi ple time periods, and the returns of portfolios over the investment horizon determined using the arithmetic average of these portfolio returns. The arithmetic average returns of portfolios selected using the model’s parameters are calculated and compared. However, investors’ returns are derived from changes in the value of their portfolios. This paper shows how the use of arithmetic returns creates large biases in the magnitude and statistical sig nificance of asset pricing models’ outcomes. It argues only evaluations using the values of portfolios produce reliable results. The identified bias is created because a positive return and its equal but negative return, represent different sized price movements, and this becomes obscured when returns are analysed and averaged over multiple periods. Most existing pricing models are poten tially invalid because of the biases generated by the methodology used in their development.

Abstract Cybersecurity breaches may be correlated due to geography, similar infra structure, or use of a third-party contractor. We show how a logistic regres sion may be used to estimate the probability of an attack where breaches may be correlated among firms up and down the supply chain. We also show how a Poisson regression may be used to estimate the number of records breached. Losses arising from cybersecurity breaches have an unknown distribution. We propose the stock price reaction to a breach as an objective measure of the loss in wealth sustained by the firm due to a breach. This loss measure re flects the immediate and long-term effects of a breach, including reputational effects and other intangible impacts that are otherwise more difficult to quan tify. We examine stock returns for 258 cybersecurity breach announcements over 2011-2016 in order to obtain the empirical loss distribution. We find a five-day abnormal return of −1.44%. Seventy-one percent of these 258 an nouncements result in a negative abnormal return, and a amma distribution provides an excellent fit to these losses. In addition to introducing a predic tive model for correlated losses, our study shows how insurers can use either the empirical stock return distribution of losses or the per record cost of a breach in the pricing of cyberinsurance .

Abstract Value at Risk (VaR) and Expected Shortfall (ES) is commonly used measures of potential risk for losses in financial markets. In literature VaR and ES for the Normal Inverse Gaussian (NIG) distribution, a special case of Generalized Hyperbolic Distribution (GHD), is frequently used. There are however, Nor mal Inverse Gaussian related distributions, which are also special cases of GHD that can also be used. The objective of this paper is to calculate VaR for Normal Weighted Inverse Gaussian (NWIG) distributions. The Expectation Maximization (EM) algorithm has been used to obtain the Maximum Like lihood (ML) estimates of the proposed models for the Range Resource Cor poration (RRC) financial data. We used Kupiec likelihood ratio (LR) for back testing of VaR. Kolmogorov-Smirnov test and Anderson-Darling test have been used for goodness of fit test. Akaike Information Creterion (AIC), Baye sian Information Creterion (BIC) and Log-likelihood have been used for model selection. The results clearly show that the NWIG distributions are good al ternatives to NIG for determining VaR and ES.

Abstract We replicate and price European options on stocks modeled by time-changed geometric Brownian motion. The time change is obtained as the integrated intensity of random arrival times of price changes of the underlier over the life of the option. For European call options we obtain explicit hedging and pricing formulas. This approach is motivated by the need to connect option prices directly to the microstructure properties of the limit order book that determines tick-by-tick stock price changes. The continuous time model is obtained as an appropriate limit of discrete time random walks with ran dom jump times, in the limit of infinitely many independent representative agents.

Abstract This paper deals with asset allocation decisions when the considered risk measure is directly related to the investor’s level of risk aversion. It is well known that the optimal portfolio weights are considerably sensitive to how assets are ranked on the basis of their risk-return profile. We propose a procedure to construct optimal portfolios that adapt quickly to changes in risk using a time varying asset allocation model based on a modified Sharpe Ratio measure related to downside and upside risk weighted using an aversion parameter. The model is applied, as an illustrative example, to six stock markets located in Western Europe basing the analysis on monthly data covering the period January 2000-December 2020.

Abstract Foreign direct investment (FDI) is considered in the literature as an essential driver of economic growth. This paper aims to investigate the impact of FDI on economic growth of Sierra Leone economy. As a whole, the period under consideration is a thirty-seven year period spanning from 1980 to 2016. Most researchers conclude that there is a positive impact of FDI on Economic Growth of a nation’s economy, but in this paper, we discovered that FDI has no relationship with economic growth in Sierra Leone. Empirical methods were used to analyze data and results are based on regression analysis conducted from available data. This paper ascertains that FDI (stock) inflow in Sierra Leone has no impact on the economic growth of the nation.

Abstract We propose a novel actuarial risk model which, unlike the classical Crámer Lundberg model, incorporates a stream of random premiums that offset random claims. A key feature of the model is a discrete time accounting of premiums and claims flow, whereby lending itself to random walk type analysis. We derive various estimates of ruin probability thereby providing an effective method of risk assessment over a future time horizon.

Abstract In this paper, we test the effectiveness of predicting the behavior of stocks utilizing stochastic calculus. We begin by exploring the intuition of Brownian motion by explaining its birth through the observations of Robert Brown and later through Bachelier’s work on its applications to the financial market and finally its rigorous and concretized form proposed by Norbert Wiener. The aforementioned motivates a stochastic differential equation to model the future price fluctuations of a stock wherein Itô integration is prominent and consequently expanded upon. The final part of this paper focuses on the accuracy of the model by back testing it with Apple stock and deriving a correlation coefficient.

Abstract The Normal Inverse Gaussian (NIG) distribution, a special case of the Generalized Hyperbolic Distribution (GHD) has been frequently used for financial modelling and risk measures. In this work, we consider other normal Variance mean mixtures based on finite mixtures of special cases for Generalised Inverse Gaussian as mixing distributions. The Expectation-Maximization (EM) algorithm has been used to obtain the Maximum Likelihood (ML) estimates of the proposed models for some financial data. We estimate Value at risk (VaR) and Expected Shortfall (ES) for the fitted models. The Kupiec likelihood ratio (LR) has been applied for backtesting of VaR. Akaike Information Creterion (AIC), Bayesian Information Creterion (BIC) and Log-likelihood have been used for model selection. The results clearly show that the proposed models are good alternatives to NIG for determining VaR and ES.

Abstract This paper investigates the usefulness of the Hierarchical Equal Risk Contribution algorithm to exploit correlation structure in China’s equity market over 2001-2020. By running a horse race of different combinations of metrics and linkages, we demonstrate that the winner strategy always beats traditional portfolio construction techniques. Better-performing risk-based hierarchy strategies vary with stock-sorting methods by size, mean return, volatility, and Sharpe ratio. However, our treatment results in extremely imbalanced asset allocation, implying that we capture information other than the standard Chinese industrial classification.

Abstract Asian options are generally priced using arithmetic or geometric averages of the underlying stock. However, these methods are not suitable when stock’s volatilities are very low. The motivation to develop derivative prices based on averaging the underlying asset stems from the robust features associated with Asian options which suggest that they are more suited to African markets where prices can be dormant for long periods resulting in low volatilities in stock prices. We propose the use of the modal average as the measure of the underlying stock price when stocks have low volatilities instead of the more popular arithmetic and geometric averages. In particular, the stock price is assumed to follow Geometric Brownian Motion and using the concept of maximum of a function, a model for the modal average of the underlying stock is derived. A process of obtaining the price of a call option is subsequently developed. Theoretically, we prove further that for very low volatilities the modal average model is a better estimator of the expected average of the stock price and consequently produces cheaper option prices than geometric and arithmetic average models. Using data from the Ghana Stock Exchange and the Nasdaq, the proposed model is used to price options sold on selected stocks on the exchange. The numerical results consistently show that for underlying stocks with volatility less than 3%, the modal average model provides cheaper call options than the arithmetic or geometric averages pricing models.

Abstract This paper considers the solution of the equations for ruin probabilities in infinite continuous time. Using the Fourier Transform and certain results from the theory of complex functions, these solutions are obtained as complex integrals in a form which may be evaluated numerically by means of the inverse Fourier Transform. In addition the relationship between the results obtained for the continuous time cases, and those in the literature, are compared. Closed form ruin probabilities for the heavy tailed distributions: mixed exponential; Gamma (including Erlang); Lognormal; Weibull; and Pareto, are derived as a result (or computed to any degree of accuracy, and without the use of simulations).

Abstract With the advent of information technology, retailers have easy access to forecast updates to adjust inventory as the selling season approaches. Rather than giving a second ordering chance, this paper takes lateral transshipment as an alternative, and investigates the transshipment policies in a supplier with two independent asymmetric retailers with a single selling season under both independent and competitive market scenarios. The results show that in the presence of the capital-constrained retailer’s default risk, the capital sufficient retailer is not always preferring to transship out. In the numerical analysis, we find the transshipment policies under competitive scenario are stricter than those under independent scenario.

Abstract This study examined the endogenous nature of negative bubbles forming in meme stocks with the Log-Periodic Power Law (LPPL) Confidence Indicator (CI). A meme stock is a stock that has gained a significant amount of attention on a large social media platform such as Yahoo! or Reddit. This study examined four meme stocks including Tesla, Inc. (TSLA), GameStop Corp. (GME), Koss Corporation (KOSS), and AMC Entertainment Holdings Inc (AMC). The CI was able to detect numerous bubbles forming in meme stocks, but had difficulty in significantly predicting social media-induced exogenous rallies. This may have been due to price movements affected by external causes such as short squeezes. However, the model did provide proof for the formation of previous bubbles that could have been a catalyst for the meme stocks rallies. This study outlines the real unpredictability of many black-swan events, and further studies could be done examining exogenous bubbles.

Abstract Few of us fail to realize foreign exchange rates are ratios rather than prices, but we almost universally use them as if they were in fact the prices of currencies. This paper converts FX rates into prices by defining the value of each currency as its spending power in its home country: the place where it can be spent. Each FX rate becomes the ratio of two such values. The value of the absolute spending power of one currency in relation to another is not considered. Instead, the changes in the spending power of each currency relative to its counterpart contained in daily FX movements are examined. To make this observable, a synthetic exchange rate series is generated from the ten floating currencies most frequently traded against the US dollar. In this series the change in spending power (value) of each currency each day is known. The spending power changes derived from these synthetic FX rates are compared with the information which can be derived from actual exchange rates. The results show changes in FX rates contain almost no useable information on changes in currency values and are in fact unintuitive and highly misleading. If market participants act on the assumption that, in the absence of price sensitive information, yesterday’s FX rate is the best estimate of today’s rate, they will trade in a manner that leads to FX volatility rather than mean reversion. A solution to this problem is proposed. All changes in the value of individual currencies can be made completely transparent by introducing a global benchmark currency to replace the US dollar as the vehicle currency in FX trades.

Abstract In this paper, we study the asymptotic behavior of dynamic coherent risk measures in general settings regardless of specific representations of the risk measures. In particular, we develop three different types laws of large numbers (LLN) for the average values of portfolios. These LLNs capture the limiting behavior of time-consistent dynamic coherent risk measures under appropriate conditions. Our results apply to general probability spaces with a sequence of financial returns characterized by a set of probability measures. We show that the limit of these averages will generally be multivalued within an identified set. We give examples to illustrate the potential applicability of our results and derive asymptotic results on estimation for the risk of returns of financial assets using a time-consistent dynamic coherent risk measure induced by a class of g-expectations.