Exchange rate overvaluation under hyperinflation: The case of Peru

This dissertation examines the behavior of the exchange rate under two different scenarios. The first one is characterized by, relatively, low inflation or a situation where prices adjust sluggishly. The second is a high inflation economy where prices respond very rapidly even to unanticipated shocks. In the first one, following a monetary expansion, the exchange rate overshoots, i.e. the nominal exchange rate depreciates at a faster pace than the price level. Under high levels of inflation, prices change faster than the exchange rate so the exchange rate undershoots its long run equilibrium value.^ The standard work in this area, Dornbusch (1976), explains the overshooting process in the context of perfect capital mobility and sluggish adjustment in the goods market. A monetary expansion will make the exchange rate increase beyond its long run equilibrium value. This dissertation expands on Dornbusch's model and provides an analysis of the exchange rate under conditions of currency substitution and price flexibility, characteristics of the Peruvian economy during the hyper inflation process that took place at the end of the 1980's. The results of the modified Dornbusch model reveal that, given a monetary expansion, the change in the price level will be larger than the change in the exchange rate if prices react more than proportionally to the monetary shock.^ We will expect this over-reaction in circumstances of high inflation when the velocity of money is increasing very rapidly. Increasing velocity of money, gives rise to a higher relative price variability which in turn contributes to the appearance of new financial (and also non-financial) instruments that report a higher return than the exchange rate, causing people to switch their demand for foreign exchange to this new assets. In the context of currency substitution, economic agents hoard and use foreign exchange as a store of value. The big decline in output originated by hyper inflation induces people to sell this hoarded money to finance current expenses, increasing the supply of foreign exchange in the market. Both, the decrease in demand and the increase in supply reduce the price of foreign exchange i.e. the real exchange rate. The findings mentioned above are tested using Peruvian data for the period January 1985-July 1990, the results of the econometric estimation confirm our findings in the theoretical model. ^

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.